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Evaluating the effects of automated vehicle technology on the capacity of freeway weaving sections
Abstract
Weaving sections, where a merge and a diverge are in close proximity, are considered as crucial bottlenecks in the highway network. Lane changes happen frequently in such sections, leading to a reduced capacity and the traffic phenomenon known as capacity drop. This paper studies how the emerging automated vehicle technology can improve the operations and increase the capacity of weaving sections. We propose an efficient yet effective multiclass hybrid model that considers two aspects of this technology in scenarios with various penetration rates: (i) the potential to control the desired lane change decisions of automated vehicles, which is represented in a macroscopic manner as the distribution of lane change positions, and (ii) the lower reaction time associated with automated vehicles that can reduce headways and the required gaps for lane changing maneuvers. The proposed model is successfully calibrated and validated with empirical observations from conventional vehicles at a weaving section near the city of Basel, Switzerland. It is able to replicate traffic dynamics in weaving sections including the capacity drop. This model is then applied in a simulation-based optimization framework that searches for the optimal distribution of the desired lane change positions to maximize the capacity of weaving sections. Simulation results show that by optimizing the distribution of the desired lane change positions, the capacity of the studied weaving section can increase up to 15%. The results also indicate that if the reaction time is considered as well, there is an additional combined effect that can further increase the capacity. Overall, the results show the great potential of the automated vehicle technology for increasing the capacity of weaving sections.
... Uno et al. [7] identified the potential conflicts in weaving sections by analyzing vehicle movements from recorded videos. Tilg et al. [34] proposed a mixed traffic model calibrated to replicate the traffic dynamics on a weaving section. Although findings from this study revealed the potential of AuVs to improve the capacity of the weaving section, other aspects of mobility and safety remain uncharted. ...
... Maximum detriment in throughput was experienced at 10% AuV share (−3.88%, 1981 vphpl@ 1400 vphpl inflow rate) and maximum gain was attained at 65% AuV share (80.49%, 3720 vphpl@2200 vphpl inflow rate) in comparison to base case (2061 vphpl). We compared our attained results with Tilg et al. [34], where they evaluated the effects of automated vehicles in freeway weaving sections. ...
... As a result, these isolated AuVs had to maintain ACC driving principles while accommodating lane-changing vehicles at high inflow rates. Tilg et al. [34] mentioned that these gap-searching vehicles, specifically HuVs, related to speed attenuation until they engaged in successful lane-changing maneuvers. e gradual increase of the AuV share improved platoon forming probability that led to more stable traffic movement and is analogous to the findings by Talebpour and Mahmassani [75]. ...
Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.
... Studies have been carried out to investigate the effects of AVs in combination with conventional human-driven vehicles (or normal vehicles (NVs)) on the road networks (Friedrich, 2016;Talebpour and Mahmassani, 2016;Ghiasi et al., 2017;Chen et al., 2017;Lamotte et al., 2017;Stern et al., 2018;Pan et al., 2019). The main aims of the research in this area are partly to understand the characteristics of the mixed traffic (Arem et al., 2006;Friedrich, 2016;Talebpour and Mahmassani, 2016;Ghiasi et al., 2017;Pan et al., 2019) and partly to propose new algorithms to incorporate the real-time information from connected and automated vehicles (CAV) to improve the efficiency of the traffic network (Ilgin Guler et al., 2014;Argote-Cabañero et al., 2015;Ghiasi et al., 2017;Stern et al., 2018;Hyland and Mahmassani, 2018;Gong and Du, 2018;Tilg et al., 2018). Arem et al. (2006) studied the effect of vehicles with adaptive cruise control (ACC) and a limited penetration of AVs on the capacity of the mixed traffic, and concluded that under low penetration rates the autonomy may deteriorate the capacity of a freeway. ...
... Other than controlling AVs in traffic streams, potential governance over lane changing and route choice of CAVs have resulted in numerous applications in improving the efficiency of the future road network Tilg et al., 2018;Yu et al., 2018;Li et al., 2018a;Ramezani and Ye, 2019). For instance, Tilg et al. (2018) proposed a hybrid model to optimize lane changing of CAVs at freeway weaving sections serving a mixed traffic to increase the capacity of bottlenecks. ...
... Other than controlling AVs in traffic streams, potential governance over lane changing and route choice of CAVs have resulted in numerous applications in improving the efficiency of the future road network Tilg et al., 2018;Yu et al., 2018;Li et al., 2018a;Ramezani and Ye, 2019). For instance, Tilg et al. (2018) proposed a hybrid model to optimize lane changing of CAVs at freeway weaving sections serving a mixed traffic to increase the capacity of bottlenecks. To add, Li et al. (2018a) studied the route choice optimization of CAVs in a network with mixed traffic to maintain the equilibrium or maximize an efficiency measure of the network. ...
Presence of autonomous vehicles (AVs) affects traffic flow characteristics of a mixed traffic stream comprising human-driven vehicles. To model the impact of AVs on the saturation flow of arterials and highways, we propose analytical models to derive the expected value and variance of headway of a traffic stream with mixed AVs and conventional human-driven (or normal) vehicles (NVs), given the expected penetration rate of AVs. The proposed model considers the arrangement (order) of AVs and NVs in the mixed stream and the expected, the lowest, and the highest achievable headways and their variability are analytically derived. Moreover, the total delay of a two-lane road with interrupted traffic flow is analytically modeled for various possible lane allocation policies: (a) dedicated lanes, (b) mixed-mixed lanes, (c) mixed-NV lanes, and (d) mixed-AV lanes. Microsimulation experiments demonstrate the validity of the developed models for the average headway and its variability, as well as the delay formulas.
... Models of transportation can assist planners and decision-makers in better understanding the potential developments and implications of autonomous vehicles. Modeling research in the context of AV has mostly focused on microscopic traffic models to evaluate capacity impacts [21][22][23][24][25]. ...
... It also depends on the penetration rate of AVs of the total traffic. AVs will partially reduce or even eliminate human factors from traffic flow, reducing the gap for lane changing, headway, reaction time using 360-degree sensors, and cameras expected to increase road capacity, leading to lower congestions [24]. ...
The continuous growth of the population and economy worldwide has led to an increase in the demand for the transportation sector, resulting in a huge increase in the demand for travel in cities worldwide over the past few decades. The growth has led to higher traffic congestion, energy consumption, and greenhouse emissions, affecting the sustainability of cities development. Thus, new technologies are rapidly being developed to compete with the growth. Autonomous vehicles (AV) connected through cooperative intelligent systems (C-ITS) technology is an opportunity for the future of the transportation sector.
The primary motivation of the dissertation is to understand better the expected impact of autonomous systems on social safety and security related to transport systems. The dissertation applies a sizeable macroscopic approach and, on the other hand, other statistical methods to discover the expected effect of the spread of AVs. Accordingly, the study focuses on building several models that can be used to find the expected effects of AVs for different road networks using different available parameters.
As autonomous transport systems are expected to be highly cooperative and connected, the dissertation also investigates the different cyber security issues to reveal the estimated security impacts caused by the increasing penetration of AVs. Finally, a study of the effect of connected AVs in emergencies and network vulnerability on a macroscopic level is made.
... One literature review conducted on the potential effects of AVs on road transportation emphasized improvements in traffic flow, pedestrian mobility, travel demand, safety, and emissions reduction, while also noting uncertainties regarding long-term impacts [18]. Another study explored the role of CAVs and AVs in enhancing transportation systems' efficiency and sustainability, using PTV Vissim simulation and other tools to provide recommendations for urban planners and policymakers [19]. ...
... 19 present the impact of different autonomous vehicle (AV) behaviors and penetration rates on carbon monoxide emissions (CO), nitrogen oxides emissions (NOx), volatile organic compounds emissions (VOC), and fuel consumption across various traffic signal cycle times (60 to 204 s). Emissions and fuel consumption generally decrease with shorter cycle times across all driving behaviors and AV penetration rates. ...
The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.
... A key factor to consider is safety, which should be a North Star for new models and technological advancements [3]. To integrate novel approaches into intelligent transportation systems [4] and to better understand the impact of AVs on both the road [5] and travel decisions [6], interdisciplinary research is required for behavioral modeling, autonomous vehicles' perception, and altruistic behavior, e.g., leveraging different notions recently adopted from psychology to robotics [7]. ...
... Merging is challenging for AVs since they have to identify sufficient gaps to merge on the highway while assuring safety by accounting for human driving behaviors such as sudden deceleration/acceleration or sudden lane change, and they must complete the merging operation before the current lane ends [9]. That being said, in [5], it is shown that when the merging behavior is optimally controlled, the actual discharge rate in weaving sections (merges closely followed by diverges) can be increased with a greater penetration rate of AVs. ...
The number of Autonomous Vehicles (AVs) coexisting with conventional human-driven vehicles is expected to increase significantly in the coming years. This coexistence will last decades before full AV adoption is achieved worldwide. However, the cautious nature of AVs and the aggressive behavior of some human drivers could create unprecedentedly challenging scenarios for AVs, such as being stuck on merge lanes and blocked by human-driven vehicles. On the other hand, the cooperative behavior of other human drivers could assist AVs in avoiding deadlock situations. In this paper, we propose to leverage AVs to tally the cooperative driving behavior of human-driven vehicles. To this end, we model cooperative driving behavior in a “highway merge” scenario, which tends to be challenging for AVs. We vary the percentage of cooperative human-driven vehicles and estimate the percentage of AVs required to tally cooperative acts. Results show that when fifty percent of the human drivers cooperate, cooperation leads to statistically significant reductions of up to 68%, 46%, 38%, and 5% in stop delay, number of stops, vehicle delay, and travel time, respectively. Finally, we demonstrate that a 30% penetration of AVs is sufficient to tally up to 78% of cooperative behavior in highway scenarios. To promote cooperation across the population, our future work revolves around the construction of vehicular profiles based on their cooperative behavior. These profiles will be regularly updated and disseminated among AVs to aid their cooperative decisions toward human-driven vehicles in the upcoming interactions.
... R ECENT studies have shown that connected and autonomous vehicles (CAVs) can ease traffic flow instabilities at low CAV penetration levels [1]- [4]. To do so, these systems require accurate knowledge of traffic conditions. ...
... Interestingly, it succeeds in tracing backward propagating shockwave patterns by tying together the limited information obtained from the sparse trajectories. In other words, the model is able to reconstruct shockwave patterns with varying sizes and shapes depending on the local traffic conditions, rather than using a mere interpolation assuming a constant wave speed [4], [39]- [45]. Fig. 4 (i, iv, v) shows a scenario where the model captured the dynamic stop-and-go traffic regime (red regions), which cannot be characterized by a single shockwave speed. ...
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model’s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels.
... Sulejic et al. [13] developed an algorithm based on particle swarm optimization to optimize the lane-changing distribution and alleviate the problem of lane-changing concentration in the weaving section. Tilg et al. [14] applied the automated vehicle technology and proposed a multiclass hybrid model to optimize the lane-changing distribution and increase the capacity of the weaving section. ...
To alleviate the lane-changing conflicts between weaving vehicles and enhance the traffic efficiency in the weaving section of urban expressway under the connected autonomous vehicle (CAV) environment, a cooperative lane-changing strategy for CAVs is proposed. The strategy consists of an upper layer of decision making, which determines the lane-changing sequences of weaving vehicles based on their lane-changing advantages quantified by a set of utility functions, and a lower layer of control, which generates detailed instructions of speed adjustments and lane-changing manoeuvres for weaving vehicles. To verify the effectiveness of the proposed strategy under different traffic demand settings, a numerical simulation, including a base case and a control case, is conducted. Then, to further verify the effectiveness of the proposed strategy for the mixed traffic state and compare its performance with the existing CAV lane-changing method, benchmark and comparison tests with six different market penetration rates (MPRs) of CAVs are carried out under the congested demand setting. In addition, the delay improvement ratio, inverse time-to-collision, and ratio of large deceleration time are selected as performance indicators to investigate the effect of the proposed strategy on enhancing the operational efficiency, traffic safety, and passenger’s comfort within the weaving section. According to the simulation results, the overall efficiency, safety and comfort in the weaving section under the CAV environment, are all improved, when the proposed strategy is applied to weaving vehicles. The proposed strategy is also superior to the existing CAV lane-changing method on maintaining traffic efficiency and safety. Therefore, the proposed cooperative lane-changing strategy, based on CAV technologies, shows good potential in solving the problem of lane-changing conflicts within the weaving section and facilitating the traffic management and traffic control of urban expressway.
... and their impacts on traffic flow dynamics on freeways and in urban areas, e.g. [Til18]. It is expected that the driving behavior of CAVs will be fundamentally different from that of human drivers. ...
Connected and automated vehicles (CAVs) will behave fundamentally differently than human drivers. In mixed traffic, this could lead to inefficiencies and safety-critical situations since neither human drivers nor CAVs will be able to fully anticipate or predict surrounding traffic dynamics. Thus, some researchers proposed to separate CAVs from conventional vehicles by dedicating exclusive lanes to them. However, the separation of road infrastructure can negatively impact the system’s capacity. While the effects of CAV lanes were addressed for freeways, their deployment in urban settings is not yet fully understood. This paper systematically analyzes the effects of CAV-lanes in an urban setting accounting for the corresponding complexities. We employ microscopic traffic simulation to model traffic flow dynamics in a detailed manner and to be able to consider a wide array of supply-related characteristics. These concern intersection geometry, public transport operation, traffic signal control, and traffic management. Our study contributes to the existing literature by revealing the potential of CAV lanes in an urban setting while accounting for the behavioral and topological complexities. The results of this study can support decision-makers in the design of future urban transportation systems and to prepare cities for the upcoming era of automation in traffic.
... Autonomous vehicles (AVs) depend on both automation levels of the Society of Automotive Engineers (SAE) present in the traffic stream and the percentage of AVs in the total traffic flow (AV penetration). AVs will decrease or even totally eliminate human factors from traffic flow, which are believed to increase road capacities' resulting in less congestions [170]. Studies on motorways in the USA showed that automated vehicle penetration of 90% of the total traffic would reduce both delay and fuel consumption by 60% and 25%, respectively [171]. ...
... Therefore, it is expected that physical traffic signals will no longer be necessary for future traffic management [16], since the Vehicle-to-Everything (V2X) technique enables virtual traffic lights [17] that reside at each vehicle to reduce the difficulty of signal detection. On the other hand, CAVs enable the application of vehicle platooning [18], [19], [20], [21], [22], which can further increase road storage by reducing inter-vehicular headway considerably [23], [24], [25], [26] and improve energy efficiency by mitigating aerodynamic drag and unnecessary speed fluctuations [27]. Hence, researchers try to facilitate the advantages of vehicle platooning to enhance traffic mobility at autonomous intersections, which raises a critical research question of how to determine the optimal platoon formation scheme for vehicles to optimize important objective indicators subject to relevant constraints. ...
This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajecto-ries according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications. Index Terms-Cooperative driving, vehicle platooning, traffic scheduling, queue spillback, unsignalized intersections.
... To optimize the road network, researchers control congestion boundary by using an improved macrograph [21], the distribution of the desired lane change positions [22], the balanced traffic [23], large-scale and reasonable OD traffic distribution [24,25]. Zhang et al. [26] linked the OD model to the Macroscopic Fundamental Diagram (MFD) and calculated net-work capacity. ...
The traffic carrying capacity of the road network is limited. When the limit is exceeded, traffic congestion will restrict the control and development of transportation systems. This paper aims to determine the connotation of traffic carrying capacity in some adjacent weaving sections (AWS) and put forward a novel calculation model for self‐organizing carrying capacity. Firstly, the in‐depth analysis found that each weaving section has a local‐whole and interactive relationship with AWS. Secondly, the self‐organizing critical state of AWS is analyzed by calculating the threshold from slow to congestion. Finally, the Renhe Interchange was used as an experimental object to study the carrying capacity under the self‐organizing critical state of AWS. The VISSIM software and Microsoft Visual Studio 2020‐C# software are employed to verify the model. The self‐organizing critical occupancy rate before the optimization is 38.60%, and the carrying capacity is 7527 pcu/h. After optimization, the self‐organizing critical occupancy rate is 48.70%, and the carrying capacity is 9497 pcu/h. The results show that the model can simulate the carrying capacity of self‐organized criticality. It can also provide a basis for urban managers to maintain the stable development of the system and ensure the overall operation quality of the AWS.
... Another factor influencing the effect of AVs on a network is the percentage of AVs in the total traffic flow (AV penetration) [8]. Human driving factors in a traffic network are expected to be partially eliminated using innovative AV technologies, such as 360-degree cameras and sensors [38]. AV platooning showed a 60% reduction in gap time between vehicles, showing a significant improvement in road capacity and congestion reduction [24,31]. ...
Purpose
This study first presents a method to identify the parameters increasing road vulnerability on a macroscopic road network model. The second part exploresthe effect size difference of the analyzed attributes on network vulnerability through the implementation of different autonomous vehicles (AVs) penetrations and automation levels.
Methods
The road traffic network of Budapest, Hungary on PTV VISUM is studied by adopting a passenger car unit factor procedure to simulate the effect of AVs on road saturation. Five link parameters were used: length, distance from the centre, speed, number of lanes, and number of connectors. Network vulnerability was studied by simulating a combination of road elimination process with different passenger car unit values for AVs.
Results
The analysis found the number of road lanes is the most significant parameter, affecting the link criticality; followed by road length and distance from the centre. The analysis of four AV scenarios with different AV penetration and level of automation showed huge effect differences ranging from 3.50% for a simple AV automation level with low AV percentage to as large to 28.53% for a fully automated fleet.
Conclusions
AV implementation has proved efficient in reducing the amount of travel delays in the case of road failure. Finally, it was found that the number of lanes remained the most significant influencing parameter on travel delay. The main question is to discover the effect size difference of the analyzed attributes on network vulnerability through the implementation of different AVs penetrations and automation levels.
... By 2045, AVs might account for up to half of new vehicle sales and 40% of vehicle trips [4]. Potential effects of AVs on traffic flow and performance can be investigated in several approaches: using vehicle throughput as principal performance measure to evaluate their coexistence with human-driven vehicles on heterogeneous motorways [5,14,15], investigate AVs influences on the capacity of urban freeway segments [2,16,17], and impacts of AVs on network performance parameters at 100% penetration [18]. According to the studies, AVs will alter traffic flows in a variety of ways, enhancing or deteriorating traffic performance. ...
Automated Vehicles (AVs) are predicted to have a substantial impact on safety, traffic congestion, energy consumption, and, eventually, urban space transformation. In the near future, the market penetration of AVs is likely to increase significantly. Until AVs become common reality on roadways, there will be an extended transition period during which different types of AVs with varied driving logics will coexist alongside human-driven vehicles. This study analyzes the range of potential impacts on traffic performance for various types of AV driving logics and physical interventions in heterogenous motorways using microscopic traffic simulation considering several hypothetical scenarios. The simulations clearly portrayed how network performance changes with physical modifications on the network elements and behavioral modifications with AV driving logics in PTV Vissim. Traffic performance results based on average delay, travel speed, vehicles arrived in the network, travel time and queue length depicted better results for most parameters with advanced driving logics and higher penetrations. The driving logics should evolve faster to progressive levels to balance the trade-off between the various safety and performance attributes. Overall, automation alone could not bring the expected improvements, others aspects such as AV-readiness of infrastructures and the change in driving behaviors of humans alongside AVs should be assessed in simulation environments parallel to the legal issues regarding deployment of AVs.
... In the context of CAV operations at weaving sections, Tilg et al. (2018) evaluated the effect of CAVs on the weaving section capacity. Their research focused on the use of automation to make lane changing decisions, as well as leveraging the reduced reaction time and lower headway levels of CAVs. ...
... Such differences become important for use cases where a large number of repetitions of the traffic model are required, e.g. simulation-based optimization or model calibration (e.g Osorio and Bierlaire, 2009;Tilg et al., 2018Tilg et al., , 2020bAmeli et al., 2020;Ge et al., 2014). At the same time, no numerical error is introduced by the larger time-step as indicated in Section 5.1. ...
The well-known Lighthill–Whitham–Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the eulerian formulation of VT found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory (KWT). We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model, and the ability to model complex bottlenecks. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic KWT simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for extending existing VT applications to the network level, for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.
... He [24] used a cellular automaton to build a weaving-area discrete model of a multilane urban expressway by analyzing vehicle characteristics. Tilg et al. [25] studied how to improve traffic capacity in weaving areas with automated vehicle technology. Cai et al. [26] obtained traffic capacity by different simulation models of weaving areas and put forward a model of traffic capacity in weaving areas that is appropriate for urban roads in mountainous areas. ...
The weaving area is an important junction that affects the efficiency and traffic safety at the municipal interchanges. To explore the factors affecting traffic efficiency and the spatial distribution characteristics of traffic risks in the weaving areas of municipal interchanges, this study employed a microscopic traffic software of Vissim to construct a simulation model of weaving areas and evaluate the impact of road and traffic design parameters in the short weaving on traffic efficiency and risks, which includes ramp delay, mainline through lane (TL) traffic delay, average ramp speed, TL average speed, and collision rate. Through variance analysis via a large number of simulation scenarios, the weaving length is identified as the most important factor affecting traffic efficiency and risks in the short weaving area. Subsequently, three different types of weaving lengths with 350 m, 450 m, and 550 m were set to conduct the sensitivity analysis based on four performance indexes of mean acceleration and deceleration, acceleration range, deceleration range, and speed standard deviation as the representative variables of spatial risk distribution. The simulation results illustrate that a shorter weaving length has a significant influence on risk distribution, especially the highest risk probability at the positions after three-quarters of the inner and outer lanes in the short weaving area at the municipal interchange. Finally, this study verified the traffic risk reduction method of having traffic safety facilities and traffic organization at the complex interchange with double-entry and single-exit weaving areas in the city of Guangzhou, China. The research proposed a method to analyze the influence of the design parameters in the short weaving area on traffic efficiency and safety and provided a reference for the risk spatial distribution analysis and improvement in the short weaving area.
... It also depends on the penetration rate of AVs of the total traffic. AVs will partially reduce or even eliminate human factors from traffic flow, reducing gap for lane changing, headway, reaction time using 360-degree sensors and cameras expected to increase road capacity, leading to lower congestion [3]. ...
The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.
... Autonomous vehicles (AVs) depend on both automation levels of the Society of Automotive Engineers (SAE) present in the traffic stream and the percentage of AVs in the total traffic flow (AV penetration). AVs will decrease or even totally eliminate human factors from traffic flow, which are believed to increase road capacities' resulting in less congestions [27]. Studies on motorways in the USA showed that automated vehicle penetration of 90% of the total traffic would reduce both delay and fuel consumption by 60% and 25%, respectively [28]. ...
Several transport policies reduce pollution levels caused by private vehicles by introducing autonomous or electric vehicles and encouraging mode shift from private to public transport through park and ride (P&R) facilities. However, combining the policies of introducing autonomous vehicles with the implementation of electric vehicles and using the P&R system could amplify the decrease of transport sector emissions. The COPERT software has been used to calculate the emissions. This article aims to study these policies and determine which combinations can better reduce pollution. The result shows that each combination of autonomous vehicles reduces pollution to different degrees. In conclusion, the shift to more sustainable transport modes through autonomous electric vehicles and P&R systems reduces pollution in the urban environment to a higher percentage. In contrast, the combination of autonomous vehicles has lower emission reduction but is easier to implement with the currently available infrastructure.
... Most existing studies on cooperative driving at conflicting areas assumed that all the participant vehicles are AVs. Now, researchers showed more interest in establishing new strategies for AVs to cooperate with HVs in conflicting areas (Zhao et al., 2018;Tilg et al., 2018;Ding et al., 2020;Yang and Oguchi, 2020). Some studies also discussed the co-existence and co-optimization of traffic signal systems for HVs and cooperative driving for AVs (Yang et al., 2016;Guo et al., 2019;Niroumand et al., 2020). ...
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs’ impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided.
... In the context of CAV operations at weaving sections, Tilg et al. (2018) evaluated the effect of CAVs on the weaving section capacity. Their research focused on the use of automation to make lane changing decisions, as well as leveraging the reduced reaction time and lower headway levels of CAVs. ...
In this research, we propose novel mathematical models and algorithms for optimizing connected and automated vehicles’ (CAVs) trajectories at freeway weaving segments assuming 100 percent CAV market penetration. The proposed system receives vehicle arrival information and generates optimal trajectories that are relayed to the CAVs. The algorithm simulates vehicle arrivals and develops optimal vehicle trajectories on a 2-lane weaving section (one mainline and one auxiliary lane). We test the optimization algorithm under a variety of demand scenarios and using real-world arrival data. Results suggest that the proposed algorithm increases the average travel speed and capacity by 12 to 16% and up to 11%, respectively when compared to HCM estimates. Also, our case study shows the algorithm increases the average speed by 17%, 30%, and 38% for minimum time headways of 1.7 s, 1.4 s, and 1 s, respectively, compared to conventional vehicles.
... The research that currently exists on this issue provides relevant insights through the simulation platform, where microscopic features of CAVs such as acceleration and headway time are incorporated [3,5,6]. Based on the new driving features of CAVs, innovative controlling algorithms, including CAV platooning and cooperative lane-changing strategies, are developed and verified in the simulation platform [9,10,[17][18][19]. The majority of the simulation results have verified that with the growth of CAV penetration rate, the increase of CAV platoon intensity, and the reduction of headway time, the road capacity will be improved. ...
Abstract Advances in connected and autonomous vehicles have the promise to reshape the future of the transportation system. How and when the benefits associated with automation and connectivity technology will start to impact the performance of an urban corridor is an issue of interest for traffic operators. This paper proposes an analytical capacity model for urban corridors with mixed traffic based on the concept of macroscopic fundamental diagram. The model incorporates the full spectrum of connected and autonomous vehicle penetration rates as well as the reaction times of different vehicle following patterns. The connected and autonomous vehicle platoon intensity, formulated as an exponential function of the connected and autonomous vehicle penetration rate, is also considered in the proposed analytical capacity model. Numerical experiments are conducted to verify that different reaction time settings yield disparate results. Some reaction time settings were found to cause the corridor capacity to increase monotonically with the connected and autonomous vehicle penetration rate while others led to decreases in corridor capacity with connected and autonomous vehicle penetration rates. Finally, the validity of the proposed methodology is verified via simulation tests in VISSIM 2020.
... Literature [7] proposed the segmentation model and improved regression model for weaving speed prediction, and used genetic algorithm to calibrate the parameters of segmentation model and improved regression model respectively. Literature [8] proposed an effective multi-level hybrid model, which takes into account the location distribution of lane change and vehicle automatic response time under different penetration rates. The experimental results show that this method can effectively reduce the headway and clearance required for lane change, thus improving the traffic capacity. ...
Traffic congestion is usually caused by the frequent lane changing behaviour of vehicles in the weaving segments, especially in several adjacent weaving segments. In order to solve the problem of weaving congestion, this paper proposes a traffic guidance method based on self-organizing critical state, and designed a Nash equilibrium optimization scheme based on average traffic delay. Then VISSIM software carries on the simulation verification to the actual neighbor weaving segments. First, it validates the induced traffic organization method, keeps each information input consistent, and only changes the induced distance. Through the daily average traffic volume (4092 pcu / h), the traffic volume in the early peak period (5340 pcu / h), the traffic volume in the late peak period (4596 pcu / h) and annual average traffic volume (3276 pcu/h) of the two near neighbor weaving segments in Chongqing. The results show that the optimal lane change constraint distance is 60% of the length of weaving segment, and the corresponding average traffic delays are reduced by 57%, 73%, 63% and 72% respectively. Through the simulation and optimization of the whole day traffic operation, the average delay reduction rate is as high as 84%. The effectiveness of the proposed method is demonstrated by integrating other output file evaluation indexes, which can be used as a reference for future research on self-organizing criticality optimization method of neighbor weaving segments.
... This process continues until the convergence criteria are met. Examples for a successful application of such models for the field of traffic simulation are shown in Tilg et al. (2018) and He (2014). We conclude that the sequential model-based optimization approach is suitable for our problem setting. ...
The three-dimensional passenger macroscopic fundamental diagram (pMFD) describes the relation of the network accumulation of public transport and private vehicles, and the passenger production. It allows for modeling the multi-modal traffic dynamics in urban networks and deriving innovative performance indicators. This paper integrates this concept into a multi-modal transport system design framework formulated as a simulation-based optimization problem. In doing so, we consider the competition for limited road space and the operational characteristics, such as congestion occurrences, at the strategic design level. We evaluate the proposed framework in a case study for the Sioux Falls network. Thereby, we deliver a proof of concept, and show that the proposed methodology indeed designs a transport system which benefits the overall system's performance. This paper further advances the integration of sequential model-based optimization techniques, macroscopic traffic flow concepts, and traffic simulation to design multi-modal transport systems. This supports transport planners and local authorities in composing efficient and robust transport networks.
... As we move into the future, with more advanced sensing and instrumentation technologies, it will be possible to identify driving patterns dynamically, in real-time, and issue warnings to the driver when appropriate [55]. Some of the insights generated with naturalistic driving data could also be used to design automated cars that drive in a more sustainable manner. ...
A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups, and definition of similar behaviors. Naturalistic Driving methods provide a solution by allowing the collection of exhaustive datasets in quantitative and qualitative terms. However, exploiting and analyzing these datasets is complex and resource-intensive. Moreover, most of the previous studies, have constrained the great potential of naturalistic driving datasets to very specific situations, events, and/or road sections. In this paper, we propose a novel methodology for extracting driving patterns from naturalistic driving data, even from small population samples. We use Geographic Information Systems (GIS), so we can evaluate drivers' behavior and reactions to certain events or road sections, and compare across situations using different spatial scales. To that end, we analyze some kinematic parameters such as speeds, acceleration, braking, and other forces that define a driving attitude. Our method favors an adequate mapping of complete datasets enabling us to achieve a comprehensive perspective of driving performance.
... Also, in this paper impact of autonomous vehicles on stability is investigated. Tilg et al. (2018) determined impact of automated vehicles on the weaving section using a multiclass hybrid model and they considered lane changing and reaction time in the simulation. ...
Shortly, Automated Vehicles (AV) will be used in urban streets in many countries. On the other hand, many countries are faced with congested problems and are looking for some way to solve congestion problems. Therefore, it is necessary to find the impact of these vehicles on different aspects of transportation planning. In recent years many researchers have been encouraged to investigate the impact of Connected and Automated Vehicles (CAV) on the capacity of transportation networks. In this paper, we have a specific goal, and the goal is to show how CAVs can influence roads' capacity. In this research, we choose the SUMO simulator to reach our goal. Besides, we use Krauss car-following model to specify the following vehicle behavior and also choose the speed-density relationship and macroscopic fundamental diagram (MFD) to determine the density and capacity of our network. In the last part, we show the result of a simulation-based on data collected from the SUMO simulator. Based on results, CAVs have great potential to improve transportation networks' situation from a capacity perspective.
... The study of van Beinum, Farah, Wegman, and Hoogendoorn (2018) showed that lane changes caused by merging and diverging vehicles create most turbulencean increase in the amount of traffic leads to a higher level of turbulence, and larger available space for merging and diverging results in the lower level of turbulence. Tilg, Yang, and Menendez (2018) further considered the automated vehicle (AV) and proposed a multiclass hybrid model and a simulation-based optimization framework to study how AV technology can improve the stability of the operation and increase the capacity of weaving sections. ...
With increasing traffic demand in urban areas of metropolises, many tunnels have been constructed to improve road capacity and traffic mobility. The distance between two consecutive tunnels is relatively short which usually forms a weaving section, leading to considerable traffic conflicts. The objective of this study is to evaluate the safety performance of such inter-tunnel sections. Conflict prediction models based on negative binomial regression were developed to identify influential factors. Field data were collected at ten selected sites in Nanjing, China, and used for calibrating and validating the proposed models. Two types of inter-tunnel weaving sections (type 1 and type 2) were found in the field with distinct lane markings and operation rules. The unique lane markings in type 1 weaving sections are designed to isolate weaving traffic flows and thus reduce conflicts, but in practice, contradictory to its design intention, lead to more traffic conflicts compared with type 2 weaving sections. In addition, the length of the diverging section, merging section, and whole weaving section are found to be significant influencing factors on the conflict occurrence. The findings in the present study are expected to help engineer better design inter-tunnel sections.
... In addition, lane management and route choice control of CAVs have resulted in numerous applications in improving the efficiency of a road network [9], [21]. Tilg et al. [19] for instance optimized the lane changing of CAVs at freeway weaving sections that carry a mixed traffic to improve their capacity. ...
... However, a more realistic scenario should include vehicles of different nature, as for instance autonomous vehicles and human-driven vehicles [33][34][35]. To guarantee an efficient integration and coordination, control algorithms are often designed so to mimic the human-like driving behavior [36,37], and, given the always-increasing number of vehicles, a collective intelligence, interweaving autonomous and non-autonomous vehicles at short and long-distance, becomes crucial to mitigate phenomena known as traffic waves [38][39][40]. Nevertheless, collective motions might be dominated by instability, and suitable control analytical methods must be provided to achieve safe and efficient global traffic motion [41,42]. ...
The present paper investigates a new paradigm to control a swarm of moving individual vehicles, based on the introduction of a few random long-range communications in a queue dominated by short-range car-following dynamics. The theoretical approach adapts the small-world theory, originally proposed in social sciences, to the investigation of these networks. It is shown that the controlled system exhibits properties of higher synchronization and robustness with respect to communication failures. The considered application to a vehicle swarm shows how safety and security of the related traffic dynamics are strongly increased, diminishing the collision probability even in the presence of a hacker attack to some connectivity channels.
... As a result, freight tour planning and terminal operations can also be affected. In the vicinity of these bottlenecks, the lane-changing behavior is shown to affect traffic throughput, safety, and turbulence (6)(7)(8)(9)(10)(11)(12). A limited body of research has shown that heterogeneity exists within the merging and diverging behaviors of drivers (13)(14)(15). ...
Lane change models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. It is our proposition that truck drivers too are heterogeneous in their lane change behavior and there exist inter-driver differences within truck drivers. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic and gap acceptance characteristic of lane change maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. A majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within lane change component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.
... Recent studies have shown that connected and autonomous vehicles (CAVs) can ease traffic flow instabilities at low CAV penetration levels [5,33,34,39]. To do so, these systems require accurate knowledge of traffic conditions. ...
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5\%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation.
... The signal-based control methods mainly aim to alleviate the highly concentrated lane change in the weaving area, which could be further grouped into two categories -lane change decentralization methods and traffic prediction and control methods. The studies on lane change decentralization address the cooperative lane change from the decision planning perspective (Mai, Jiang, and Chung 2016;Park and Smith 2012;Park, Bhamidipati, and Smith 2011;Tilg, Yang, and Menendez 2018), focusing on distributing lane-changing activities across available weaving area. It is achieved by sending individual messages to drivers based on their location to advise them when to start their lane change via V2X communication (Mai, Jiang, and Chung 2016). ...
In weaving areas, vehicles frequently carry out conflicting lane-changing manoeuvres. The frequent lane change in this area results in rapid changes in vehicles’ speed, which in turn reduces traffic efficiency and create traffic bottlenecks at weaving areas. This research proposes a cooperative weaving motion planner for connected and automated vehicles to reduce traffic oscillation. The proposed motion planner is based on model predictive control method and solved by Chang-Hu’s method (Yu, Yu, and Jia 2018). The motion planner only requires longitudinally automation which is accessible for most commercialized luxury vehicles. Simulation evaluation was conducted to quantify the performance of the proposed motion planner. The results show that the proposed motion planner is able to reduce traffic oscillation by 2.7% to 28.0%. Furthermore, the computation time of the proposed planner is fewer than 20 milliseconds indicating readiness to real-time application.
Objectives:
With the growing market penetration of connected and autonomous vehicles (CAVs), the interaction between conventional human-driven vehicles (HDVs) and CAVs will be inevitable. However, the effects of CAVs in mixed traffic streams have not been extensively studied in China. This study aims to quantify the changes in driving characteristics of an HDV while following a CAV compared to following another HDV and investigate the corresponding impact on traffic safety and the environment caused by these changes.
Methods:
Firstly, two scenarios were built on a driving simulation platform. In scenario 1, the driver follows a vehicle programmed to execute the speed profile of the HDV obtained from the Shanghai Naturalistic Driving Study (SH-NDS) project. In scenario 2, the driver follows a vehicle whose speed profile is calibrated according to the Cooperative Adaptive Cruise Control (CACC) follow-along theory. Secondly, the speed, acceleration, and headway of 30 individuals in each following scenario were analyzed. Speed and acceleration volatility (standard deviation, deviation rate) and time-to-collision (TTC) were selected as indexes to assess the safety impact. The emission and fuel consumption models were used to determine the environmental impact after being localized by the parameters.
Results:
HDVs following CAVs exhibit less driving volatility in speed and acceleration, show remarkable improvements in TTC, consume less fuel, and produce fewer emissions on average.
Conclusions:
By introducing CAVs into the road traffic system, traffic operation safety and environmental quality will be improved, with a more stable flow status, lower collision risk, and less air pollution.
Connected and autonomous vehicles (CAVs) are developing rapidly nowadays. In the near future, we may see human-driving vehicles (HVs) and CAVs running on the same road. The characteristics of human drivers and the interaction between HVs and CAVs, therefore, need to be further studied. All these factors will have a great impact on traffic operation. This paper aims to explore a modeling method to analyze the operation mechanism of mixed traffic flows of HVs and CAVs. Considering HV drivers’ cognitive behavioral characteristics and HV-CAV interaction effects, the forward and lateral movement rules of HVs and CAVs are proposed based on the Cellular Automata model. Then the fundamental diagram, congestion degree, lane-changing frequency, and time-spatial diagram are obtained by numerical simulation. The results show that the presence of CAVs is positively related to macroscopic traffic parameters, including velocity, flow and critical density. CAVs help to relieve traffic congestion and instabilities. The congestion degree of pure CAV traffic flow is about 1/3 that of pure HV traffic flow. When the CAV penetration rate is relatively low, the interaction between HV and CAV has a significant impact on traffic operation. Specifically, when the CAV penetration rate is below 0.5∼0.6, increasing the CAV penetration rate will increase the lane-changing frequency of the whole traffic flow.
As one of the innovative technologies of intelligent transportation systems (ITS), Connected and Autonomous Vehicles (CAVs) have been deployed gradually. Given that there will be a long transition period before reaching a fully CAVs environment, it is crucial to assess the potential impacts of CAVs on mixed traffic flow. Considering platoon formation process, this study develops a platoon cooperation strategy based on “catch-up” mechanism, and then analyzes the impact on fundamental diagram, traffic oscillation, and traffic safety within mixed traffic. Simulation results show that with an increasing market penetration rate (MPR) of CAVs, road capacity shows an increasing trend. Compared with base scenario, a clear increase in road capacity is also observed under platoon scenario. With an increasing MPR, traffic oscillation is shown to reduce largely. Furthermore, the proposed platoon strategy could dampen frequent shockwaves and shorten the propagation range of waves. Regarding traffic safety, multiple surrogate safety measures (SSMs) are used to evaluate the traffic risk: including Criticality Index Function (CIF), Potential Index for Collision with Urgent Deceleration (PICUD), and Deceleration Rate to Avoid a Crash (DRAC). With increasing MPR, collision risk identified by CIF and DRAC shows an increase tendency, while that identified by PICUD has no apparent trend. Furthermore, the platoon strategy is shown to increase the severity of traffic conflicts significantly. Overall, this study provides novel insights into CAVs deployment through the analysis of platoon strategy.
Connected and automated vehicles (CAVs) require proper infrastructure for safer and more reliable operations. Many state and local planning agencies have developed multiyear capital programs to provide such infrastructure in a timely manner within their limited budgets. Meanwhile, the traffic environment will evolve over time as CAV technologies become available (i.e., toward the mixed environment of CAVs and human-driven vehicles), which requires infrastructure plans specific to different planning terms (i.e., short-, medium-, and long-term) to accommodate changing infrastructure needs. To develop an effective multiyear infrastructure plan, planning agencies need to understand changing infrastructure needs with time, identify alternative infrastructure options for different planning terms, and select the most appropriate ones based on their long-term vision. This study performed a systematic literature review to develop a knowledge base for multiyear infrastructure planning for CAVs. To be more specific, the literature review aims to develop the following knowledge areas: (1) identification of existing and future infrastructure options for the operation of CAVs, (2) understanding the role of infrastructure to support different functions of CAVs to realize safety, mobility, and environmental benefits, and (3) integration of the aforementioned findings into planning agencies' multiyear infrastructure plans for CAVs. Based on the review, this study categorizes different CAV infrastructure into existing infrastructure and future infrastructure options while considering five system functions of CAVs (i.e., cooperative merging, platoon-ing, intersection movement, dynamic routing, and cooperation and connected functions) to illustrate the role of these infrastructure options under different traffic scenarios. The implementation of the developed knowledge base is demonstrated through a case study of two selected state agencies' long-term infrastructure planning for CAVs.
You can read the extended English abstract version of this article here: https://ceej.aut.ac.ir/jufile?ar_sfile=88672&lang=en....................................................................................................
Congestion is one of the problems that has bothered many countries in recent decades and has imposed huge costs on many countries. For this reason, many researchers are looking for ways to reduce congestion in transportation networks. On the other hand, it is predicted that the emergence of Automated Vehicles and Connected and Automated Vehicles can be effective in reducing congestion on the roads and increasing the capacity of the roads. For this reason, this study investigates the effect of Automated Vehicles and Connected and Automated Vehicles on the capacity of roads. In this study, a freeway network with the Merge section is used and the simulations are implemented using SUMO microscopic simulator software. In this study, to determine the driving behavior, the car following model for longitudinal movements and the lane changing model for lateral movements have been used. The Krauss car-following model and the LC2013 lane-changing model were used to determine driving behavior in this study. The simulation results show that Automated Vehicles can increase road capacity by up to 52% and Connected and Automated Vehicles can increase road capacity by up to 65%, which indicates the potential of these vehicles to increase capacity and reduce congestion. The results also show that these vehicles can have a significant impact on capacity when the presence of these vehicles on the road is significant.
This paper explores the efficiency of a novel merging system based on a cooperative late merge strategy (CLMS) to mitigate the capacity reduction in work zones due to lane closure. Cooperative late merge strategies in connected vehicles (CV) and connected and autonomous vehicles (CAV) environments are formulated to enhance throughput by reducing gaps and increasing the synchronized speed in the work zone. We propose decentralized and centralized systems based on vehicle-to-vehicle and vehicle-to-infrastructure communication. The decentralized CLMS incorporates a modified lane-changing model to reflect the cooperative feature under the CV environment. The centralized CLMS is developed to further optimize the work zone throughput based on gap reduction and speed harmonization features
enabled by CAV. The results prove that the decentralized CLMS outperforms other systems by increasing throughput as well as reducing delay and queue length. The centralized CLMS demonstrated substantial improvements compared to other systems. The simulation results prove that the decentralized CLMS improves capacity by 17% and the centralized CLMS by 45%, when compared to a traditional work zone system.
This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.
This paper presents a novel approach to improve traffic throughput near diverge and weave bottlenecks in mixed traffic with human-driven vehicles (HDVs) and connected automated vehicles (CAVs). This is done by the strategic assignment of CAVs across lanes. The main principle is to induce strategic and necessary lane changes (LCs) (by CAVs and HDVs) well upstream of the potential bottleneck, so that the traffic flow approaching the bottleneck is organized and exhibits fewer throughput-reducing LCs at the bottleneck. A hybrid approach is used to investigate the problem: macroscopic analytical approach to formulate lane assignment strategies, and numerical simulations to quantify the improvements in throughput for various scenarios. Several strategies are formulated considering various operational conditions for each bottleneck type. Furthermore, compensatory behaviour of HDVs in response to the flow/density imbalance created by the CAV lane assignment is explicitly accounted for in our framework. Evaluation by numerical simulations demonstrates significant benefits of the proposed method, even at low to moderate CAV penetration rates: they can lead to an increase of throughput by several percent, thereby decreasing delays significantly.
An accurate quantification of traffic flow characteristics with and without bus-only lane is important for determining transit priority strategies. Examinations of real-world vehicle trajectory data, like those from the NGSIM dataset, found that an effective discharge rate may be discounted when merging or lane-changing behaviors are observed. To deal with this, this manuscript analytically derives the effective discharge rate of a roadway segment by considering multi-stage merging behavior and vehicular traffic kinematics near a side entrance, for optimal bus-only lane conversion. The classic fluid-based approximation model by Newell that characterizes the queuing and dissipation process is extended to a multi-state queuing analysis framework. Three effective discharge rate discount factors are explicitly derived, for bus-only lane, general-purpose lane, and mixed-traffic lane, respectively. The derived effective discharge rates are represented by mathematically-simple expressions in a closed-form, and correctly account for the effects of demand inputs, such as arrival rate and the ratio of vehicles of different types, vehicular performance (such as acceleration rate), and traffic flow characteristics (such as backward wave speed and jam density). The bus-only lane conversion scheduling problem is then formulated as an optimization model to minimize total traffic delay. Validation, using NGSIM data, showed that our model reduced the effective discharge rate estimation error significantly on both arterials and freeways. Sensitivity analysis revealed that the optimal bus-only lane scheduling time varied in response to traffic demand and vehicle ratios, and the developed optimization model was always beneficial when compared with the demand-oriented strategy.
The growth in technology on autonomous transportation systems is currently motivating a number of research initiatives. This paper first presents a survey of the literature on autonomous marine vessels in general. By identifying the main research interests in this field, we define 10 thematic categories. The collected articles are then classified according to these categories. We show that research on autonomous vessels has increased dramatically in the past decade. However, most of the published articles have focused on navigation control and safety issues. Studies regarding other topics, such as transport and logistics, are very limited. Although our main interest is the literature on autonomous vessels, we contrast its development with respect to the literature on autonomous cars so as to have a better understanding about the future potentials in the research on autonomous vessels. The comparison shows that there are great opportunities for research about transportation and logistics with autonomous vessels. Finally, several potential research areas regarding logistics with autonomous vessels are proposed. As the technology behind remote‐controlled or autonomous ships is maturing rapidly, we believe that it is already time for researchers in the field to start looking into future water‐borne transport and logistics using autonomous vessels.
This paper envisions a mixed network with autonomous vehicle (AV) expressways and non-autonomous local streets in the future, where a trip may consist of both the AV self-driving part and the manual-driving part. A linear traffic corridor approach is adopted, where AV expressways and non-autonomous streets are in parallel and are connected through the entrance/exit. This mimics the intermediate future where AV expressways may be initially deployed along major corridors. We model and analyze road users’ route choice in such a mixed network. We also examine how AV expressways are integrated with the local streets can affect system performance.
This paper investigates the mechanisms of how spatially distributed lane changes (LCs) interact and contribute to “capacity-drop” at three types of extended bottlenecks: merge, diverge, and weave. A hybrid approach is used to study the problem: analytical approach to capture the behavior of merging and diverging LCs and numerical simulations to quantify capacity-drop considering various geometric configurations of extended bottlenecks. This study focuses on the impact of LC vehicles’ bounded acceleration on “void” (wasted space) creation in traffic streams when they insert/desert at a lower speed, and interactions among multiple voids. We found that (1) LCs closer to the downstream end of bottlenecks are more likely to create persisting voids and contribute to capacity-drop. (2) For weave bottlenecks, capacity-drop is governed by two counteracting effects of LCs: persisting voids and utilization of vacancies created by diverging vehicles; (3) the more balanced the merging and diverging flows, the lower the capacity-drop; and (4) capacity-drop is minimum if merging LCs occur downstream of diverging LCs, and maximum in the opposite alignment.
This paper shows the results of ex-ante impact assessment of automated vehicles on capacity of freeways using microscopic traffic flow simulation. The simulation was conducted for different penetration rates of automated vehicles in Germany´s national vehicle fleet, which were predicted using a newly developed vehicle cohort stock model. For this aim, the standard segments of German freeway infrastructure including basic, merge, diverge, and weaving segments were simulated. The resulting capacity increments were assigned to a country-wide traffic flow model of Germany. In the next step, an economic appraisal was conducted based on the methodology for the cost-benefit analysis used in the current German Federal Transport Infrastructure Plan (BVWP). The results reveal that the conservative driving behavior of automated vehicles, as foreseen by the current legislation, has a negative impact on the capacity of freeways. On the contrary, automated technologies that allow shorter headways between the vehicles, have the potential to increase the capacity of the freeway network by 30 % and reduce traffic delays significantly. However, small market penetration rates of automated vehicles do not lead to discernible capacity benefits and the potential benefits are likely to be realized at higher penetration into the traffic mix.
Weaving section is one of the major bottlenecks in urban expressway network. A field observation showed that approximately 80% of the weaving vehicles changed their lanes within the most upstream 25% part of the entire section length. It means an insufficient use of provided infrastructure, and strategies to use them are necessary. Then this study developed vehicle control algorithms to avoid conflicts in weaving sections and aimed to achieve the capacity improvement. The proposed algorithms were implemented into a microscopic traffic simulation model in order to evaluate their effects. Two types of weaving sections and several scenarios of weaving ratios were prepared. The amount of the throughput at the downstream section was counted and the lane change positions of individual vehicles were recorded. The result showed that the total throughput was decreased gradually as the weaving ratio was increased. However, this drop were improved significantly by applying some algorithms. This means that there is a potential to utilize the existing infrastructure more efficiently and to alleviate congestion without huge construction investment.
The traffic in a weaving segment is subject to lane-changing turbulence in excess of that normally present on basic motorway segments. Empirical studies have observed a lane-changing concentration problem, as traffic flow increases, which can cause flow break down and congestion. This paper focuses on the lane-changing concentration problem in weaving segments. A Cooperative Intelligent Transport System (C-ITS) advisory has been shown to alleviate such a lane-changing concentration problem. The advisory aims to distribute the lane-changing along the weaving segment. Unlike previous methods in the literature, where weaving vehicles are assigned according to fixed distributions, this paper proposes an algorithm to optimize the lane-changing distribution. The proposed optimization algorithm was developed based on particle swarm optimization. The optimized lane-changing distribution for a one-sided motorway weaving segment using microscopic simulation has been evaluated. The initial results show that the proposed algorithm could be used as a successful optimization technique for the lane- changing concentration problem.
Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80 km/h, 60 km/h, and 40 km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942 veh/h/lane has been measured with the 40 km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.
It has been empirically observed for years that the queue discharge rate is lower than the prequeue capacity. This difference is called the capacity drop. The magnitude of capacity drop varies over a wide range, depending on the local traffic conditions. However, it is unknown what determines the capacity drop value. No thorough empirical analysis has yet revealed a reliable relationship between the capacity drop and the congestion level. This paper fills the gap by revealing, through empirical analysis, the relationship between vehicle speed in congestion and the queue discharge rate. The research studies congested states in which speed ranges from 6 to 60 km/h. The queue discharge rate is shown to increase considerably with increasing speed in the congestion. In contrast to previous research, this study bases the relationship on empirical data collected on freeways, and the data present a sufficiently large observation sample. A discussion about the influence of weather and study site characteristics on the discharge rate indicates that the relationship needs site-specific calibrations. This study provides a better prediction of capacity drop and a better theoretical understanding of the fluctuations in capacity drop.
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The objective of this article is to propose an analytical traffic model adapted to freeway weaving sections. To this aim, a theoretical weaving section is considered at the macroscopic level as the superposition of two merges and two diverges. The model endogenously incorporates the capacity drop related to the weaving lane changes. It only depends on six parameters: three parameters of the fundamental diagram (the free-flow speed, the jam density, and the wave speed in congestion), the vehicles’ acceleration, the relaxation factor, and the length of the anticipation zone. A sensitivity analysis proves that the acceleration rate and the relaxation parameter most influence the capacity drop. The analytical model agrees well with microsimulation results and empirical data collected on a weaving section in France.
Capacity drop, which is defined as discharge flow drop after bottleneck activation, has been frequently observed on urban highways, especially in merging sections. Maintaining high capacity on roadways is a main concern for traffic operators, theorists, and transportation modelers. Accordingly, many researchers have investigated capacity drop, yet highway capacity and discharge flow measurement methods vary, and results are not comparable. A systematic methodology is introduced for finding capacity drops by using detector data to estimate roadway capacity and discharge flow. The impact of the number of lanes on capacity drops at merging sections on highways is investigated. Results show that capacity drop is negatively related to the number of lanes. Detailed information is analyzed for individual lanes and off-ramp effect on capacity drop. Individual lane analysis supports the negative relation- ship between the amount of capacity drop and the number of lanes. A decrease in capacity drop is observed when the flow ratio of the off-ramp increases.
One of the main challenges arising when calibrating a complex traffic simulation model concerns the selection of the most important input parameters. The quasi-optimized trajectory-based elementary effects (quasi-OTEE) and the Kriging-based sensitivity analysis (SA) are two recently developed efficient approaches for the SA of computationally expensive simulation models. In this paper, two experimental studies using two different traffic simulation models (i.e., Aimsun and VISSIM) are presented to compare these two approaches and to better understand their advantages and disadvantages. Results show that both approaches are able to identify, to a good degree, the important parameters. In particular, the quasi-OTEE is better for screening the parameters, whereas the Kriging-based SA has higher precision in ranking the parameters. These findings suggest the following rule of thumb for the SA of computationally expensive traffic simulation models: the quasi-OTEE SA can be used first to screen the parameters and to decide which parameters to discard. Then, the Kriging-based SA can be used to refine the analysis and calculate first-order indexes to identify the correct rank of the important parameters.
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally
expensive, black-box, global optimization problems that may have continuous,
mixed-integer, or pure integer variables. Due to the black-box nature of the
objective function, derivatives are not available. Hence, surrogate models are
used as computationally cheap approximations of the expensive objective
function in order to guide the search for improved solutions. Due to the
computational expense of doing a single function evaluation, the goal is to
find optimal solutions within very few expensive evaluations. The multimodality
of the expensive black-box function requires an algorithm that is able to
search locally as well as globally. MATSuMoTo is able to address these
challenges. MATSuMoTo offers various choices for surrogate models and surrogate
model mixtures, initial experimental design strategies, and sampling
strategies. MATSuMoTo is able to do several function evaluations in parallel by
exploiting MATLAB's Parallel Computing Toolbox.
This paper presents the surrogate model based algorithm SO-I for solving purely integer optimization problems that have computationally expensive black-box objective functions and that may have computationally expensive constraints. The algorithm was developed for solving global optimization problems, meaning that the relaxed optimization problems have many local optima. However, the method is also shown to perform well on many local optimization problems, and problems with linear objective functions. The performance of SO-I, a genetic algorithm, Nonsmooth Optimization by Mesh Adaptive Direct Search (NOMAD), SO-MI (Müller et al. in Comput Oper Res 40(5):1383–1400, 2013), variable neighborhood search, and a version of SO-I that only uses a local search has been compared on 17 test problems from the literature, and on eight realizations of two application problems. One application problem relates to hydropower generation, and the other one to throughput maximization. The numerical results show that SO-I finds good solutions most efficiently. Moreover, as opposed to SO-MI, SO-I is able to find feasible points by employing a first optimization phase that aims at minimizing a constraint violation function. A feasible user-supplied point is not necessary.
How the location and duration of an incident affect delays near a recurrent bottleneck is evaluated in this study. With conventional kinematic wave theory and some dimensional analysis, tools are provided to determine whether an incident will cause generalized delays (i.e., delays that have a lingering effect for the whole length of the peak hour) according to the incident's magnitude, location, and duration. The results apply to a broad range of cases, encompassing many types of facilities and incidents. Furthermore, the results can be used as a foundation for developing and implementing new strategies to obtain significant reductions in delay. The value of fault-free surveillance is also analyzed and presented as part of an optimization problem for locating roadside assistance vehicles. It is found that this value is very high, which could justify installing advanced traffic-monitoring schemes near major bottlenecks.
The Newell–Daganzo merge model is not only very simple but also accurately reproduces experimental findings. However, the capacity downstream of the merge is an exogenous variable in the model. This is a serious limitation for merges that behave as active bottlenecks because their downstream capacity is a direct consequence of the merging behavior. This paper proposes an analytical model that extends the Newell–Daganzo model by incorporating, endogenously, the capacity drop related to the merging process. Two cases are investigated depending on the traffic states on the on-ramp. The model properties are analyzed and a sensitivity analysis is performed to quantify the relative contribution of the each parameter in the capacity drop. Finally, the extended Newell–Daganzo model is validated with experimental data coming from an active merge bottleneck on the M6 freeway in UK.
The climbing lane is an effective means by which to increase roadway capacity, improve safety, and provide smooth traffic operation on mountainous and rolling terrains where significant trucking prevails. Current critical length design guides for the climbing lane in the American Association of State Highway and Transportation Officials (AASHTO) Green Book is based on truck speed profile charts derived using the design standard of the weight-to-power ratio equal to, 120 kg/kW (200 lb/hp). For locations where a significant mix of larger and heavier trucks with more axial loads exists, AASHTO suggests one apply a more representative weight-to-power ratio but gives no specific computational procedure other than a cautionary statement. This study proposes a well-defined approach and formulation to obtain truck speed profiles, based on the nominal, dynamic, kinematic, and operating characteristics of trucks on grades. The design controls that regulate the resultant speed profiles include not only the weight-to-power ratio and the grade used in the current AASHTO manual, but also the design engine power of trucks (or the corresponding design load). The formulations, design charts, and tables derived from the design standards consistent with prevailing truck manufacturing specification allow designers to deduce the appropriate critical length for the climbing lane when significant deviation is found between the prevalent truck weight-to-power ratio and the current design standard. For practical design purposes, a tractable approximation model of reasonable accuracy that is suitable for manual calculation is also proposed, thus providing a feasible alternatives to the look-up design chart currently employed by AASHTO.
Recent empirical observations at freeway merge bottlenecks have revealed (i) a drop in the bottleneck discharge rate when
queues form upstream, (ii) an increase in lane-changing maneuvers simultaneous with this “capacity drop”, and (iii) a reversal
of the drop when the ramp is metered.
This paper shows that a simple vehicle lane-changing theory, which has been shown to explain related phenomena at lane-drops
and moving bottlenecks, also explains the new phenomena at merges. In this theory, lane-changing vehicles are modeled as discrete
particles endowed with realistic accelerations, and are embedded in a multilane stream where each lane obeys the kinematic
wave model. This theory is parsimonious: only one of its four parameters has to be calibrated by running the model.
Our simulations show that the theory predicts surprisingly well the cumulative flows at all locations, the vehicle trip times,
the number of lane-changing maneuvers, the capacity drop, its recovery upon metering, and the distribution of these measures
across lanes and over time. Applications are discussed.
We present a new strategy for the constrained global optimization of expensive black box functions using response surface models. A response surface model is simply a multivariate approximation of a continuous black box function which is used as a surrogate model for optimization in situations where function evaluations are computationally expensive. Prior global optimization methods that utilize response surface models were limited to box-constrained problems, but the new method can easily incorporate general nonlinear constraints. In the proposed method, which we refer to as the Constrained Optimization using Response Surfaces (CORS) Method, the next point for costly function evaluation is chosen to be the one that minimizes the current response surface model subject to the given constraints and to additional constraints that the point be of some distance from previously evaluated points. The distance requirement is allowed to cycle, starting from a high value (global search) and ending with a low value (local search). The purpose of the constraint is to drive the method towards unexplored regions of the domain and to prevent the premature convergence of the method to some point which may not even be a local minimizer of the black box function. The new method can be shown to converge to the global minimizer of any continuous function on a compact set regardless of the response surface model that is used. Finally, we considered two particular implementations of the CORS method which utilize a radial basis function model (CORS-RBF) and applied it on the box-constrained Dixon–Szeg test functions and on a simple nonlinearly constrained test function. The results indicate that the CORS-RBF algorithms are competitive with existing global optimization algorithms for costly functions on the box-constrained test problems. The results also show that the CORS-RBF algorithms are better than other algorithms for constrained global optimization on the nonlinearly constrained test problem.
Recent research in algorithms for solving global optimization problems using response surface methodology has shown that it
is in general not possible to use one surrogate model for solving different kinds of problems. In this paper the approach
of applying Dempster-Shafer theory to surrogate model selection and their combination is described. Various conflict redistribution
rules have been examined with respect to their influence on the results. Furthermore, the implications of the surrogate model
type, i.e. using combined, single or a hybrid of both, have been studied. The suggested algorithms were applied to several
well-known global optimization test problems. The results indicate that the used approach leads for all problems to a thorough
exploration of the variable domain, i.e. the vicinities of global optima could be detected, and that the global minima could
in most cases be approximated with high accuracy.
A multi-lane traffic flow model realistically captures the disruptive effects of lane- changing vehicles by recognizing their limited ability to accelerate. While they accelerate, these vehicles create voids in the traffic stream that affect its character. Bounded acceleration explains two features of freeway traffic streams: the capacity drop of freeway bottlenecks, and the quantitative relation between the discharge rate of moving bottlenecks and bottleneck speed. The model com- bines a multilane kinematic wave module for the traffic stream, with a detailed constrained-motion model to describe the lane-changing maneuvers, and a behavioral demand model to trigger them. The behavioral demand model has only one parameter. It was held constant in all experiments.
Connected vehicles give more precise and detailed information on vehicle movements, thus can be beneficial to provide priority to public transportation. This paper proposes a transit signal priority algorithm using connected vehicle information for multimodal traffic control. The algorithm can also be adapted to scenarios with near-side or far-side bus stops. Moreover, it can minimize either signal delay or schedule delay for buses while minimizing additional car delays. Simulation is conducted for different volume to capacity ratios, bus arrivals, bus occupancies, and penetration rates. Results show that this algorithm successfully reduces the total passenger delay. It is also shown that this algorithm is not sensitive to the assumed bus passenger occupancy, nor the estimation of bus dwell time, hence does not require accurate information on these parameters. Overall, this algorithm seems rather promising as it significantly reduces the delay of buses with minimal increase to the delay of cars in the conflicting approach.
This paper provides formulations of traffic operational capacity in mixed traffic, consisting of automated vehicles (AVs) and regular vehicles, when traffic is in equilibrium. The capacity formulations take into account (1) AV penetration rate, (2) micro/mesoscopic characteristics of regular and automated vehicles (e.g., platoon size, spacing characteristics), and (3) different lane policies to accommodate AVs such as exclusive AV and/or RV lanes and mixed-use lanes. A general formulation is developed to determine the valid domains of different lane policies and more generally, AV distributions across lanes with respect to demand, as well as optimal solutions to accommodate AVs.
Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.
In this paper, a novel mesoscopic multilane model is proposed to enable simultaneous simulation of mandatory and discretionary lane-changing behaviors to realistically capture multilane traffic dynamics. The model considers lane specific fundamental diagrams to simulate dynamic heterogeneous lane flow distributions on expressways. Moreover, different priority levels are identified according to different lane-changing motivations and the corresponding levels of urgency. Then, an algorithm is proposed to estimate the dynamic mandatory and discretionary lane-changing demands. Finally, the lane flow propagation is defined by the reaction law of the demand–supply functions, which can be regarded as an extension of the Incremental-Transfer and/or Priority Incremental-Transfer principles. The proposed mesoscopic multilane cell transmission model is calibrated and validated on a complex weaving section of the State Route 241 freeway in Orange County, California, showing both the positive and negative impact of lane changing maneuvers, e.g., balancing effect and capacity drop, respectively. Moreover, the empirical study verifies that the model requires no additional data other than the cell transmission model does. Thus, the proposed model can be deployed as a simple simulation tool for accessing dynamic mesoscopic multilane traffic state from data available to most management centers, and also the potential application in predicting the impact of traffic incident or lane control strategy.
This paper deals with the derivation of analytical formulae to estimate the effective capacity at freeway merges in a multilane context. It extends two previous papers that are based on the same modeling framework but that are restricted to a single lane on the freeway (or to the analysis of the right lane only). The analytical expression for the one-lane capacity is recursively applied for all lanes. Lane-changing maneuvers (mandatory for the on-ramp vehicles and discretionary for others) are divided into two non-overlapping local merging areas. Discretionary lane-changes are transformed into a lane-changing flow using an appropriate analytical formula. This defines a system of equations whose unknowns are the capacity on all lanes and the inserting flow coming from the on-ramp. A sensitivity analysis shows that vehicle acceleration and the truck ratio are the most influential parameters for the total capacity. The analytical formulae are proven to match with numerical results from a traffic simulator that fully describes vehicle dynamics. Finally, they provide very good estimates when compared to experimental data for an active merge on the M6 freeway in UK.
Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.
This paper focuses on the derivation of analytical formulae to estimate the effective capacity at freeway merges. It extends previous works by proposing a generic framework able to account for a refined description of the physical interactions between upstream waves and downstream voids created by inserting vehicles within the merge area. The provided analytical formulae permits to directly and accurately compute the capacity values when the merge is self-active, i.e. when both upstream roads are congested while downstream traffic conditions are free-flow.
Sensitivity analysis (SA) is able to identify the most influential parameters of a given model. Application of SA is usually critical for reducing the complexity in the subsequent model calibration and use. Unfortunately it is hardly applied, especially when the model is in the form of a computationally expensive black-box computer program. A possible solution concerns applying SA to the metamodel (i.e., an approximation of the computationally expensive model) instead. Among the other options, the use of Gaussian process metamodels (also known as Kriging metamodels) has been recently proposed for the SA of computationally expensive traffic simulation models. However, the main limitation of this approach is its dependence on the model dimensionality. When the model is high-dimensional, the estimation of the Kriging metamodel may still be problematic due to its high computational cost. In order to overcome this problem, in the present paper, the Kriging-based approach has been combined with the quasi-optimized trajectory based elementary effects (quasi-OTEE) approach for the SA of high-dimensional models. The quasi-OTEE SA is used first to screen the influential and non-influential parameters of a high-dimensional model; then the Kriging-based SA is used to calculate the variance-based sensitivity indices, and to rank the most influential parameters in a more accurate way. The application of the proposed sequential SA is illustrated with several numerical experiments. Results show that the method can properly identify the most influential parameters and their ranks, while the number of model evaluations is considerably less than the variance-based SA (e.g., in one of the tests the sequential SA requires over 50 times less model evaluations than the variance-based SA).
This paper examines the influence of two major aspects on the solution quality of surrogate model algorithms for computationally expensive black-box global optimization problems, namely the surrogate model choice and the method of iteratively selecting sample points. A random sampling strategy (algorithm SO-M-c) and a strategy where the minimum point of the response surface is used as new sample point (algorithm SO-M-s) are compared in numerical experiments. Various surrogate models and their combinations have been used within the SO-M-c and SO-M-s sampling frameworks. The Dempster-Shafer Theory approach used in the algorithm by Muller and Pich, (J Glob Optim 51:79-104, 2011) has been used for combining the surrogate models. The algorithms are numerically compared on 13 deterministic literature test problems with 2-30 dimensions, an application problem that deals with groundwater bioremediation, and an application that arises in energy generation using tethered kites. NOMAD and the particle swarm pattern search algorithm (PSWARM), which are derivative-free optimization methods, have been included in the comparison. The algorithms have also been compared to a kriging method that uses the expected improvement as sampling strategy (FEI), which is similar to the Efficient Global Optimization (EGO) algorithm. Data and performance profiles show that surrogate model combinations containing the cubic radial basis function (RBF) model work best regardless of the sampling strategy, whereas using only a polynomial regression model should be avoided. Kriging and combinations including kriging perform in general worse than when RBF models are used. NOMAD, PSWARM, and FEI perform for most problems worse than SO-M-s and SO-M-c. Within the scope of this study a Matlab toolbox has been developed that allows the user to choose, among others, between various sampling strategies and surrogate models and their combinations. The open source toolbox is available from the authors upon request.
The response surface (RS) method based on radial basis functions (RBFs) is proposed to model the input–output system of large‐scale structures for model updating in this article. As a methodology study, the complicated implicit relationships between the design parameters and response characteristics of cable‐stayed bridges are employed in the construction of an RS. The key issues for application of the proposed method are discussed, such as selecting the optimal shape parameters of RBFs, generating samples by using design of experiments, and evaluating the RS model. The RS methods based on RBFs of Gaussian, inverse quadratic, multiquadric, and inverse multiquadric are investigated. Meanwhile, the commonly used RS method based on polynomial function is also performed for comparison. The approximation accuracy of the RS methods is evaluated by multiple correlation coefficients and root mean squared errors. The antinoise ability of the proposed RS methods is also discussed. Results demonstrate that RS methods based on RBFs have high approximation accuracy and exhibit better performance than the RS method based on polynomial function. The proposed method is illustrated by model updating on a cable‐stayed bridge model. Simulation study shows that the updated results have high accuracy, and the model updating based on experimental data can achieve reasonable physical explanations. It is demonstrated that the proposed approach is valid for model updating of large and complicated structures such as long‐span cable‐stayed bridges.
This paper introduces a surrogate model based algorithm for computationally expensive mixed-integer black-box global optimization problems with both binary and non-binary integer variables that may have computationally expensive constraints. The goal is to find accurate solutions with relatively few function evaluations. A radial basis function surrogate model (response surface) is used to select candidates for integer and continuous decision variable points at which the computationally expensive objective and constraint functions are to be evaluated. In every iteration multiple new points are selected based on different methods, and the function evaluations are done in parallel. The algorithm converges to the global optimum almost surely. The performance of this new algorithm, SO-MI, is compared to a branch and bound algorithm for nonlinear problems, a genetic algorithm, and the NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search) algorithm for mixed-integer problems on 16 test problems from the literature (constrained, unconstrained, unimodal and multimodal problems), as well as on two application problems arising from structural optimization, and three application problems from optimal reliability design. The numerical experiments show that SO-MI reaches significantly better results than the other algorithms when the number of function evaluations is very restricted (200–300 evaluations).
Though there have been numerous studies of freeway weaving sections (i.e., segments in which an on-ramp is followed by an off-ramp), there remains a significant lack of empirical and theoretical understanding of the traffic behavior that causes weaving sections to become bottlenecks with varying discharge flows. The present research entails empirical analysis and theoretical modeling of what triggered the bottleneck activations and discharge flow changes in two freeway weaving sections. Both sites were recurrent bottlenecks during the rush, and investigations revealed that changes in the spatial patterns of vehicular lane-changes, especially among Freeway-to-Ramp (F-R) maneuvers, caused variations in bottleneck discharge flow. When the F-R maneuvers were concentrated near a weaving section’s on-ramp, they became more disruptive, resulting in bottleneck activations with diminished discharge flows. Findings further indicated that the spatial distributions of these lane changes, in turn, were dictated by the traffic conditions in the auxiliary lane (i.e., the lane connecting the off-ramp to the upstream on-ramp). Reductions in on-ramp flows increased the attractiveness of the auxiliary lane, thus motivating F-R drivers to perform their maneuvers nearer the onramp. Conversely, increases in on-ramp flows motivated F-R drivers to perform their maneuvers over a wider stretch of the weaving section.
This chapter presents and compares two statistical approaches to computer experiments. The second approach does so by taking random input points. Randomness is required to generate probability or confidence intervals. The first approach introduces randomness by modeling the function, f, as a realization of a Gaussian process. Deterministic computer simulations of physical phenomena are becoming widely used in science and engineering. Some of the most widely used computer models arise in the design of the semiconductors used in the computers themselves. There are two main statistical approaches to computer experiments, one based on Bayesian statistics and a frequentist one based on sampling techniques. A Bayesian approach to modeling simulator output can be based on a spatial model adapted from the geo-statistical Kriging model. This approach treats the bias or systematic departure of the response surface from a linear model as the realization of a stationary random function. This model has exact predictions at the observed responses and predicts with increasing error variance as the prediction point moves away from all the design points.
Frequent lane-changes in highway merging, diverging, and weaving areas often can disrupt traffic flow and, even worse, lead to accidents. For balanced lane-changing areas, where different lanes share the same traffic conditions, based on the observation that a vehicle has to take two lanes during its lane-changing period, we consider its contribution to traffic density is doubled. That is, there are latitudinal interactions between vehicles. Then, we incorporate additional effective density from lane-changing traffic into the fundamental diagram, from which we can clearly see the disruption effect of lane-changing traffic such as capacity-drop. With the modified fundamental diagram, we develop a simple kinematic wave theory of lane-changing dynamics, from whose Riemann solutions we present new definitions of local traffic supply and demand. In the future, we will be interested in calibrating associated parameters in the lane-changing model with observed data.
This work conducts a comprehensive investigation of traffic behavior and characteristics during freeway ramp merging under congested traffic conditions. On the Tokyo Metropolitan Expressway, traffic congestion frequently occurs at merging bottleneck sections, especially during heavy traffic demand. The Tokyo Metropolitan Expressway public corporation, generally applies different empirical strategies to increase the flow rate and decrease the accident rate at the merging sections. However, these strategies do not rely either on any behavioral characteristics of the merging traffic or on the geometric design of the merging segments. There have been only a few research publications concerned with traffic behavior and characteristics in these situations. Therefore, a three-year study is undertaken to investigate traffic behavior and characteristics during the merging process under congested situations.
Extensive traffic data capturing a wide range of traffic and geometric information were collected using detectors, videotaping, and surveys at eight interchanges in Tokyo Metropolitan Expressway. Maximum discharged flow rate from the head of the queue at merging sections in conjunction with traffic and geometric characteristics were analyzed. In addition, lane changing maneuver with respect to the freeway and ramp traffic behaviors were examined. It is believed that this study provides a thorough understanding of the freeway ramp merging dynamics. In addition, it forms a comprehensive database for the development and implementation of congestion management techniques at merging sections utilizing Intelligent Transportation System.
The paper considers global optimization of costly objective functions, i.e. the problem of finding the global minimum when there are several local minima and each function value takes considerable CPU time to compute. Such problems often arise in industrial and financial applications, where a function value could be a result of a time-consuming computer simulation or optimization. Derivatives are most often hard to obtain, and the algorithms presented make no use of such information.
Several algorithms to handle the global optimization problem are described, but the emphasis is on a new method by Gutmann and Powell, A radial basis function method for global optimization. This method is a response surface method, similar to the Efficient Global Optimization (EGO) method of Jones. Our Matlab implementation of the Radial Basis Function (RBF) method is described in detail and we analyze its efficiency on the standard test problem set of Dixon-Szegö, as well as its applicability on a real life industrial problem from train design optimization. The results show that our implementation of the RBF algorithm is very efficient on the standard test problems compared to other known solvers, but even more interesting, it performs extremely well on the train design optimization problem.
A simple theory of traffic flow is developed by replacing individual vehicles with a continuous “fluid” density and applying an empirical relation between speed and density. Characteristic features of the resulting theory are a simple “graph-shearing” process for following the development of traffic waves in time and the frequent appearance of shock waves. The effect of a traffic signal on traffic streams is studied and found to exhibit a threshold effect wherein the disturbances are minor for light traffic but suddenly build to large values when a critical density is exceeded.
This paper uses the method of kinematic waves, developed in part I, but may be read independently. A functional relationship between flow and concentration for traffic on crowded arterial roads has been postulated for some time, and has experimental backing (§2). From this a theory of the propagation of changes in traffic distribution along these roads may be deduced (§§2, 3). The theory is applied (§4) to the problem of estimating how a ‘hump’, or region of increased concentration, will move along a crowded main road. It is suggested that it will move slightly slower than the mean vehicle speed, and that vehicles passing through it will have to reduce speed rather suddenly (at a ‘shock wave’) on entering it, but can increase speed again only very gradually as they leave it. The hump gradually spreads out along the road, and the time scale of this process is estimated. The behaviour of such a hump on entering a bottleneck, which is too narrow to admit the increased flow, is studied (§5), and methods are obtained for estimating the extent and duration of the resulting hold-up. The theory is applicable principally to traffic behaviour over a long stretch of road, but the paper concludes (§6) with a discussion of its relevance to problems of flow near junctions, including a discussion of the starting flow at a controlled junction. In the introductory sections 1 and 2, we have included some elementary material on the quantitative study of traffic flow for the benefit of scientific readers unfamiliar with the subject.
Three freeway bottlenecks, each with a distinct geometry, are shown to share a relation between vehicle density and losses in discharge flow. Each bottleneck suffered reductions in discharge once queues formed just upstream. This so-called “capacity drop” was related to the density measured over some extended-length freeway segment near each bottleneck. Pronounced increase in this density always preceded a capacity drop. For each bottleneck, the densities that coincided with capacity drops were reproducible. When normalized by a bottleneck’s number of travel lanes and averaged across observation days, the density that coincided with capacity drop was even similar across bottlenecks. (These densities were nearly identical for two of the bottlenecks and the more notable difference observed for the third may be only an artifact of how the data were collected.) The findings indicate that traffic-responsive schemes to control density hold promise for increasing bottleneck discharge flows. Standardized control logic might even suffice for bottlenecks of various forms. With an eye toward future testing and deployment of such control schemes, we present and validate in an Appendix A to this paper a simple algorithm for the real-time measurement of density over freeway links of extended lengths.
It is postulated that lane-changing vehicles create voids in traffic streams, and that these voids reduce flow. This mechanism is described with a model that tracks lane changers precisely, as particles endowed with realistic mechanical properties. The model has four easy-to-measure parameters and reproduces without re-calibration two bottleneck phenomena previously thought to be unrelated: (i) the drop in the discharge rate of freeway bottlenecks when congestion begins, and (ii) the relation between the speed of a moving bottleneck and its capacity.
Measurements taken downstream of freeway/on-ramp merges have previously shown that discharge flow diminishes when a merge becomes an isolated bottleneck. By means of observation and experiment, we show here that metering an on-ramp can recover the higher discharge flow at a merge and thereby increase the merge capacity. Detailed observations were collected at a single merge using video. These data revealed that the reductions in discharge flow are triggered by a queue that forms near the merge in the freeway shoulder lane and then spreads laterally, as drivers change lanes to maneuver around slow traffic. Our experiments show that once restrictive metering mitigated this shoulder lane queue, high outflows often returned to the median lane. High merge outflows could be restored in all freeway lanes by then relaxing the metering rate so that inflows from the on-ramp increased. Although outflows recovered in this fashion were not sustained for periods greater than 13 min, the findings are the first real evidence that ramp metering can favorably affect the capacity of an isolated merge. Furthermore, these findings point to control strategies that might generate higher outflows for more prolonged periods and increase merge capacity even more. Finally, the findings uncover details of merge operation that are essential for developing realistic theories of merging traffic.
A crucial challenge faced by current microscopic traffic flow models is capturing the relaxation phenomena commonly observed near congested on-ramps: vehicles are willing to accept very short spacings as they enter the freeway, but “relax” to more comfortable values shortly thereafter. This paper introduces a framework to solve this problem using a macroscopic theory of vehicle lane-changing inside microscopic models. In this theory, lane changes take place according to a stochastic process that has been validated in the field, and whose mean value is a function of lane-specific macroscopic quantities. As a consequence, the lane-changing logic becomes very simple compared to existing microscopic lane-changing models, and requires only one extra parameter. The resulting microscopic model is validated with empirical data.
We introduce a master–worker framework for parallel global optimization of computationally expensive functions using response surface models. In particular, we parallelize two radial basis function (RBF) methods for global optimization, namely, the RBF method by Gutmann [Gutmann, H.M., 2001a. A radial basis function method for global optimization. Journal of Global Optimization 19(3), 201–227] (Gutmann-RBF) and the RBF method by Regis and Shoemaker [Regis, R.G., Shoemaker, C.A., 2005. Constrained global optimization of expensive black box functions using radial basis functions, Journal of Global Optimization 31, 153–171] (CORS-RBF). We modify these algorithms so that they can generate multiple points for simultaneous evaluation in parallel. We compare the performance of the two parallel RBF methods with a parallel multistart derivative-based algorithm, a parallel multistart derivative-free trust-region algorithm, and a parallel evolutionary algorithm on eleven test problems and on a 6-dimensional groundwater bioremediation application. The results indicate that the two parallel RBF algorithms are generally better than the other three alternatives on most of the test problems. Moreover, the two parallel RBF algorithms have comparable performances on the test problems considered. Finally, we report good speedups for both parallel RBF algorithms when using a small number of processors.
Recent work by Johnson et al. (J. Statist. Plann. Inference26 (1990) 131–148) establishes equivalence of the maximin distance design criterion and an entropy criterion motivated by function prediction in a Bayesian setting. The latter criterion has been used by Currin et al. (J. Amer. Statist. Assoc.86 (1991) 953–963) to design experiments for which the motivating application is approximation of a complex deterministic computer model. Because computer experiments often have a large number of controlled variables (inputs), maximin designs of moderate size are often concentrated in the corners of the cuboidal design region, i.e. each input is represented at only two levels. Here we will examine some maximin distance designs constructed within the class of Latin hypercube arrangements. The goal of this is to find designs which offer a compromise between the entropy/maximin criterion, and good projective properties in each dimension (as guaranteed by Latin hypercubes). A simulated annealing search algorithm is presented for constructing these designs, and patterns apparent in the optimal designs are discussed.
The ability of microscopic (simulation) models to represent lane-changing behavior according to reality has recently been questioned. In this paper the merging maneuver (a specific type of lane changing) is analyzed with empirical data. First, a conceptual model is composed; it includes the factors influencing merging behavior, namely the merge location and its relation to prevailing driving conditions, gap acceptance, and the relaxation phenomenon. The empirical data set consists of 35 min of vehicle maneuvers on 400 m of freeway, collected by a camera mounted underneath a helicopter. This process results in a data set of 3,459 vehicle trajectories, from which 704 trajectories describe merging vehicles. It is found that different merge locations are used under congested and free-flow traffic conditions. During free-flow, most vehicles merge at the first half of the acceleration lane. Under congested traffic conditions, relatively more merges are registered at the end of the acceleration lane. The smallest accepted gap observed in the data set lies between 0.75 and 1.0 s. Net headways between the merging vehicle and the new leader and new follower of less than 0.25 s are recorded. These short accepted gaps are growing over time and indicate relaxation behavior. From the data analysis it can be concluded that gap acceptance theories, as they are used in current models and theories to model merge behavior, are not able to model the observed behavior accurately.
This report is part of PATH Task Order 4141 and shows how moving obstructions can be modeled numerically with kinematic wave theory. It shows that if a moving obstruction is replaced by a sequence of fixed obstructions at nearby locations with the same "capacity", then the error in vehicle number converges uniformly to zero as the maximum separation between the moving and fixed bottlenecks is reduced. This result implies that average flows, densities, accumulations and delays can be predicted as accurately as desired with this method. Thus, any convergent finite difference scheme can now be used to model moving bottlenecks. An example is given.