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Comparison of Speed-Density Models in the Age of Connected and Automated Vehicles
Abstract
Fundamental diagrams (FDs) present the relationship between flow, speed, and density, and give some valuable information about traffic features such as capacity, congested and uncongested situations, and so forth. On the other hand, high accuracy speed-density models can produce more efficient FDs. Although numerous speed-density models are presented in the literature, there are very few models for connected and autonomous vehicles (CAVs). One of the recent spend-density models that takes into account the penetration rate of CAVs is provided by Lu et al. However, the estimation power of this model has not been tested against other speed-density models, and it has not been applied to high-speed networks such as freeways. Thus, this paper made a comparison between the Lu speed-density model and a well-known speed-density model (Papageorgiou) in freeway and grid networks. Different CAV behaviors (aggressive, normal, and conservative) are evaluated in this comparison. The comparison has been made between two speed-density models using the mean absolute percentage error (MAPE) and a t-test. The MAPE and t-test results show that differences between the two speed-density models are not significant in two case studies and that Lu is a powerful speed-density model to estimate speed compared with a well-known speed-density model. For the sake of comparing the above-mentioned models, this paper investigates the impact of CAVs on capacity based on FDs. The results suggest that the magnitude of the impacts of CAVs on road capacity (capacity increment percentage) which are obtained from two speed-density models are very close to each other. Also, the extent to which CAVs affect road capacity is highly dependent on their behavior.
... In SUMO, the movement of vehicles is modelled using carfollowing and lane-changing models on the microscale. The method for modelling CAVs in this study is in line with the work of Lu et al. and Karbasi et al. (2023). The fundamental idea behind modelling the longitudinal movement of CAVs is that they have the same car-following model as HDVs, with some modifications to simulate the full automation feature of CAVs. ...
... Thanks to automation technologies, CAVs have a shorter reaction time, allowing them to follow the leading vehicle with a smaller headway distance compared to HDVs. It is assumed that CAVs have a shorter time headway, a smaller minimum gap, and a faster acceleration than CAVs, and they can avoid collisions if the leading vehicle begins braking within their acceleration bounds (Karbasi et al. 2023;Lu et al. 2020). The parameters modified for CAVs in the Krauss car-following model (Krauss, Wagner, and Gawron 1997) are listed in Table 3. ...
... This reflects the differences in the lane-changing behaviour between the two vehicle types. For more information on the selection of car-following and lane-changing parameters for CAVs, please refer to (Karbasi et al. 2023). ...
... In SUMO, the movement of vehicles is modelled using carfollowing and lane-changing models on the microscale. The method for modelling CAVs in this study is in line with the work of Lu et al. and Karbasi et al. (2023). The fundamental idea behind modelling the longitudinal movement of CAVs is that they have the same car-following model as HDVs, with some modifications to simulate the full automation feature of CAVs. ...
... Thanks to automation technologies, CAVs have a shorter reaction time, allowing them to follow the leading vehicle with a smaller headway distance compared to HDVs. It is assumed that CAVs have a shorter time headway, a smaller minimum gap, and a faster acceleration than CAVs, and they can avoid collisions if the leading vehicle begins braking within their acceleration bounds (Karbasi et al. 2023;Lu et al. 2020). The parameters modified for CAVs in the Krauss car-following model (Krauss, Wagner, and Gawron 1997) are listed in Table 3. ...
... This reflects the differences in the lane-changing behaviour between the two vehicle types. For more information on the selection of car-following and lane-changing parameters for CAVs, please refer to (Karbasi et al. 2023). ...
One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases.
... In the simulations, both lanes are defined with similar MPR, and both CVs and HVs are allowed to change lanes. However, according to [86], CVs are defined slightly differently from HVs in terms of their driving behavior. This Page 21 of 30 difference in driving behavior can cause CVs to make different lane-change actions compared to HVs, but both types of vehicles still perform lane changes due to speed gain. ...
Connected and autonomous vehicles (CAVs) possess the capability of perception and information broadcasting with other CAVs and connected intersections. Additionally, they exhibit computational abilities and can be controlled strategically, offering energy benefits. One potential control strategy is real-time speed control, which adjusts the vehicle speed by taking advantage of broadcasted traffic information, such as signal timings. However, the optimal control is likely to increase the gap in front of the controlled CAV, which induces lane changing by other drivers. This study proposes a modified traffic flow model that aims to predict lane-changing occurrences and assess the impact of lane changes on future traffic states. The primary objective is to improve energy efficiency. The prediction model is based on a cell division platform and is derived considering the additional flow during lane changing. An optimal control strategy is then developed, subject to the predicted trajectory generated for the preceding vehicle. Lane change prediction estimates future speed and gap of vehicles, based on predicted traffic states. The proposed framework outperforms the non-lane change traffic model, resulting in up to 13% energy savings when lane changing is predicted 4-6 seconds in advance.
... Traditional tra c ow models, as proposed by Verhoef, Nijkamp, and Rietveld (1997) and adopted by various scholars (Karbasi et al., 2022;Parisi et al., 2021), often rely on observable variables such as speed and density. However, limitations in these models capturing the dynamic nature of congestion have prompted the exploration of emerging technologies. ...
Traffic congestion, a prevalent global issue, has entrenched itself as a persistent problem, posing substantial challenges for both residents and commuters, especially in developing nations. This study addresses this concern by delving into the intensity, patterns, and characteristics of traffic within selected road corridors in the Abeokuta metropolis, Nigeria. Data on road types and land use were meticulously collected through structured observations using a pre-designed checklist. Traffic censuses were executed to extract characteristics during morning and evening peak periods. Quantifying traffic volume and capacity in "vehicles per hour" (vph) and Passengers Car Unit per hour (PCU/hr), the study utilized the chi-square test to scrutinize differences in traffic volume during distinct peak hours. Furthermore, Analysis of Variance (ANOVA) was applied to assess variations in traffic composition among the studied road corridors. The study's findings spotlight Sapo-Ijaye-Iyana Mortuary, recording the highest daily peak traffic volume at 2315 vph, with cars dominating at 4444 vph. Buses emerged as the most impactful vehicle class, exerting influence at 4872 PCU/hr. The Chi-square test indicates no significant differences in vph between morning and evening peaks (x = 0.822604; p = 0.84405). Correspondingly, ANOVA results (f = 3.3106; p = 1.0000) suggest that traffic composition did not significantly differ across the surveyed roads. Recommendations from the study emphasize the enhancement of road capacities through upgrades to meet current and future transportation demands. Additionally, alternative traffic routes, such as rail lines, are proposed to facilitate the movement of heavy-duty trucks.
... Over the past decade, thanks to rapid development in sensory systems [4], Artificial Intelligence (AI) [5], and wireless communication [6], the concept of Connected and Automated Vehicles (CAVs) has been turned from a science fiction into a scientific fact. It is widely accepted that the driving behavior of future CAVs has the characteristics of connectivity and controllability compared to Human-driven Vehicles (HDVs) [12][13][14][15]. On the one hand, with the aid of Vehicle-to-Everything (V2X) communication, CAVs can share their real-time status information (position, velocity, acceleration etc.) and intentions with neighboring vehicles and Roadside Unit (RSU). ...
Merging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.
Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators.
There is increasing interest in connected and automated vehicles (CAVs), since their implementation will transform the nature of transportation and promote social and economic change. Transition toward cooperative driving still requires the understanding of some key questions to assess the performances of CAVs and human-driven vehicles on roundabouts and to properly balance road safety and traffic efficiency requirements. In this view, this paper proposes a simulation-based methodological framework aiming to assess the presence of increasing proportions of CAVs on roundabouts operating at a high-capacity utilization level. A roundabout was identified in Palermo City, Italy, and built in Aimsun (version 20) to describe the stepwise methodology. The CAV-based curves of capacity by entry mechanism were developed and then used as target capacities. To calibrate the model parameters, the capacity curves were compared with the capacity data simulated by Aimsun. The impact on the safety and performance efficiency of a lane dedicated to CAVs was also examined using surrogate measures of safety. The paper ends with highlighting a general improvement with CAVs on roundabouts, and with providing some insights to assess the advantages of the automated and connected driving technologies in transitioning to smarter mobility.
Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.
Existing microscopic traffic models represent the lane-changing maneuver as a continuous and uninterrupted lateral movement of the vehicle from its original to the target lane. We term this representation as Continuous Lane-Changing (CLC). Recent empirical studies find that not all lane-changing maneuvers are continuous; the lane-changer may pause its lateral movement during the maneuver resulting in a Fragmented Lane-Changing (FLC). We analysed a set of 1064 lane changes from NGSIM dataset which contains 270 FLCs. In comparison to a CLC, this study investigates the distinction of an FLC in terms of its execution and its effects on neighbouring vehicles. We find that during the execution of an FLC, the lane-changer exhibits distinct kinematics and takes a longer duration to complete the lane-changing. We propose a trajectory model to describe the lateral kinematics during an FLC. Additionally, we find that the FLC induces a distinct effect on the follower in the target lane, and propose a model to describe the transient behavior of the target-follower during an FLC. The modelling results suggest that the accuracy of traffic flow models can be improved by deploying lane change execution and impact models that are specific to FLC and CLC. Besides, this study identifies a set of factors that might be related to the decision-making process behind FLC: an average driver executes an FLC when the preceding and following vehicles in the target lane are slower, and when the follower in the target lane is closer than those observed during the onset of a CLC. Our findings suggest that FLC is motivated by an increased necessity to change lane such as during a mandatory lane change.
Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicles (AVs) influence the road capacity . The effects that AVs bring to the macroscopic fundamental diagram (MFD) were investigated through microscopic traffic simulations. This is a key issue as the MFD is a basic model to describe road capacity in practical traffic engineering. Accordingly, the paper investigates how the different percentage of AVs affects the urban MFD. A detailed simulation study was carried out by using SUMO both with an artificial grid road network and a real-world network in Budapest. On the one hand, simulations clearly show the capacity improvement along with AVs penetration growth. On the other hand, the paper introduces an efficient modeling for MFDs with different AVs rates by using the generalized additive model (GAM).
The main goal of this paper is to determine the achievable network capacity increases, through utilization of vehicles with Automated Driving Systems. The SUMO microscopic traffic simulator was the chosen tool for this, as it is capable of simulating individual vehicles traveling on any given network. Influence over the parameters of vehicles, network and the simulation itself on the fly was realized through the MATLAB interface-TraCI4Matlab. During simulations the goal was to fill up the network with the specified vehicles, thus examining network flows. At last, the relevant data were collected to draw the Macroscopic Fundamental Diagrams, and the equations between speed limit and maximum flows were calculated.
This study is an attempt to establish a suitable speed–density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all static and dynamic properties of speed–flow–density relationships. As a first attempt for Indian traffic condition, two behavioural parameters, namely the kinematic wave speed at jam (Cj) and a proposed saturation flow (λ), are estimated using empirical observations. The parameter Cj is estimated by developing a relationship between driver reaction time and vehicle position in the queue at the signalised intersection. Functional parameters are estimated using Levenberg–Marquardt algorithm implemented in the R statistical software. Numerical measures such as root mean squared error, average relative error and cumulative residual plots are used for assessing models fitness. We set out several static and dynamic properties of the flow–speed–density relationships to evaluate the models, and these properties equally hold good for both homogenous and heterogeneous traffic states. From the numerical analysis, it is found that very few models replicate empirical speed–density data traffic behaviour. However, none of the existing functional forms satisfy all the properties. To overcome the shortcomings, we proposed two new speed–density functional forms. The uniqueness of these models is that they satisfy both numerical accuracy and the properties of fundamental diagram. These new forms would certainly improve the modelling accuracy, especially in dynamic traffic studies when coupling with dynamic speed equations.
The projected rapid growth of the market penetration of connected and autonomous vehicle technologies (CAV) highlights the need for preparing sufficient highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). This study proposes an analytical capacity model for highway mixed traffic based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. The model structure is examined from a theoretical perspective and an analytical approach is identified to solve the the optimal CAV lane number at certain common headway settings. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management.
As a fundamental traffic diagram, the speed-density relationship can provide a solid foundation for traffic flow analysis and efficient traffic management. Because of the change in modern travel modes, the dramatic increase in the number of vehicles and traffic density, and the impact of traffic signals and other factors, vehicles change velocity frequently, which means that a speed-density model based on uninterrupted traffic flow is not suitable for interrupted traffic flow. Based on the coil data of urban roads in Wuhan, China, a new method which can accurately describe the speed-density relation of interrupted traffic flow is proposed for speed fluctuation characteristics. The model of upper and lower bounds of critical values obtained by fitting the data of the coils on urban roads can accurately and intuitively describe the state of urban road traffic, and the physical meaning of each parameter plays an important role in the prediction and analysis of such traffic.
Vehicular Ad Hoc Network (VANET) is a promising platform for the Intelligent Transportation System (ITS). Car-following theory is a significant research direction in the field of VANET, it describes the one-by-one following process of vehicles on the same lane in traffic flow. The paper analyzed Krauss car-following model and proposed a new model based on the speed safe and Krauss model in SUMO 0.21.0 platform to improve the authenticity of model. We implement it on the SUMO platform. This model considers the movement state of the car and the gradual process of deceleration in vehicles braking. Simulation experimental results show the superiority of new model over other ones in simulation that models real streets. It achieves good results in wait time, travel time, lane-change number and safe speed.
Road transport is a major source of environmental pollution. Cars and trucks, which are the most common types of vehicles, exhaust a variety of pollutants (e.g., nitrogen oxides and particulate matter) that are detrimental to human health. Research on the ecological impacts of road vehicles has highlighted the importance of reducing pollutant emissions. This chapter aims to investigate the impacts of road maintenance sites on pollution in the surrounding environment, which is a slightly different and interdisciplinary aspect of the problem.
Road pavement and infrastructure (bridges, viaducts, and tunnels) must be maintained for the road network to function properly. Maintenance sites interrupt the normal flow of traffic, which leads to traffic jams, higher travel times, and pollutant emissions.
This chapter explores a variety of approaches to traffic emissions modeling to identify a numerical model capable of determining the ecological impacts of maintenance sites on the surrounding environment. A simple simulation will be conducted to demonstrate the importance of the subject. Research gaps are presented at the end of the chapter to guide future studies.
Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads. Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs). This paper aims to quantify such impacts by studying the high-resolution vehicle trajectory data that are obtained from microscopic simulation. Two platoon clustering strategies for CACC- an ad hoc coordination strategy and a local coordination strategy-are implemented. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. According to the two-sample Kolmogorov-Smirnov test, however, the distributions of the hard braking events (as a potential safety impact) for HVs change significantly under local coordination strategy. For both of the clustering strategy, CAVs increase the average lane change frequency for HVs. The break-even point for average lane change frequency between the two strategies is observed at 30% MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change frequency following a monotonically increasing pattern in response to MPR, and it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction state of the car-following model for HVs is analyzed. It is revealed that the composition of the interaction state could be influenced by CAVs as well. One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs.
Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.
The transportation network can provide additional utility by addressing the safety concerns on roads. On-road fatalities are an unfortunate loss of life and lead to significant costs for society and the economy. Connected and Autonomous Vehicles (CAVs), envisaged as operating with idealised safety and cooperation, could be a means of mitigating these costs. This paper intends to provide insights into the safety improvements to be attained by incrementally transitioning the fleet to CAVs. This investigation is done by constructing a calibrated microsimulation environment in Vissim and deploying the custom developed Virdi CAV Control Protocol (VCCP) algorithm for CAV behaviour. The CAV behaviour is implemented using an application programming interface and a dynamic linking library. CAVs are introduced to the environment in 10% increments, and safety performance is assessed using the Surrogate Safety Assessment Module (SSAM). The results of this study show that CAVs at low penetrations result in an increase in conflicts at signalised intersections but a decrease at priority-controlled intersections. The initial 20% penetration of CAVs is accompanied by a +22%, −87%, −62% and +33% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively. CAVs at high penetrations indicate a global reduction in conflicts. A 90% CAV penetration is accompanied by a −48%, −100%, −98% and −81% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively.
Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
The fundamental diagram of a road, including free-flow capacity and queue discharge rate, is very important for traffic engineering purposes. In the real word, most traffic measurements come from stationary loop detectors. This paper proposes a method to fit Wu's fundamental diagram to loop detector data. Wu's fundamental diagram is characterised by five parameters, being free-flow speed, wave speed, free-flow capacity, queue discharge rate and jam density. The proposed method entails fixing the wave speed and the free-flow speed. The method consists of two steps. We first use a triangular fundamental diagram to separate the congested branch from the free-flow branch. Then, the remaining three parameters of Wu's fundamental diagram are fitted on each branch using a least-square fit. This method is shown to be robust for cases tested in real life, and hence very noisy, data.
SUMO is an open source microscopic traffic simulation. A major component of modelling microscopic vehicle behavior is the lane-changing behavior on multi-lane roads. We describe a new model which uses a 4-layered hierarchy of motivations to determine the vehicle behavior during every simulation step and motivate in which ways it improves the current lane-changing model.
A model for traffic flow is developed by treating the traffic stream as a continuous fluid. Fluid dynamic principles are then used to derive relations between speed, density, and flow.
The continuous growth of road traffic volumes leads to significant environmental and economical problems. For this reason there have been efforts for more than four decades to understand the dynamics of traffic flow in order to find ways to optimize traffic with respect to a reduction of environmental impacts and economical losses due to congestion. In this work a microscopic model of traffic flow is proposed that adds to the understanding of the different types of congestion that are found in traffic flow. The main assumption the model is based on is the fact that in general vehicles move without colliding. From this property of vehicle motion a model can be derived that shows a rich dynamics and proves to be in good agreement with empirical data. The model is mainly characterized by the parameters describing typical acceleration and deceleration capabilities of the vehicles. It closely resembles other well - known models for certain choices of these parameters. By varying acceleration and deceleration capabilities a thorough understanding of the dynamics of the model and the previously known special cases is gained.
Three macroscopic mathematical models are presented and compared on the basis of real traffic data collected from the Boulevard Périphérique in Paris. All models include the conservation equation and a nonlinear, static, or dynamic volume-density relationship. Parameter estimation is effectuated for each model so as to reflect the characteristics of the particular application case considered. Sensitivity investigations for changed application conditions are included.
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