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Quantifying the Safety Impact of Connected and Autonomous Vehicles in Motorways: A Simulation-Based Study
Abstract
Connected and Autonomous Vehicles (CAV) is the developing summit of the integration between artificial intelligence (AI), robotics, automotive design and information technologies. Many researchers are investigating their effects on traffic safety. This study tries to quantify the volume of incidents when sharing the road human-driven vehicles and fully CAV. After modeling the geometry of 4.5 km of motorway and the parameters of connectivity and automation using Aimsun Next platform, several scenarios of the percentages of CAV (0%, 25%, 50%, 75%, and 100%) were driven in microsimulation runs. Then the microsimulation generated vehicles trajectories that are used to identify conflicts using the Surrogate Safety Assessment Model (SSAM). The results of this analysis confirm previous research in that the reduction of number of conflicts will be up to 35% with low and moderate penetration rates of CAV and more than 80% if the road is operated only with CAV.
... The fleet mixes proposed in this study were simulated for one hour with a 0.1 s time step, following previous studies [13], [14], [41], and an 18-min warming-up period calculated as in [42], considering the length and average speeds of the freeway segment. Based on previous studies (e.g., [43], [44]) each scenario was assigned a total of 15 simulated replications. To achieve a 90% confidence interval level, Shahdah et al. [43] defined the number of simulation runs (N) as follows: ...
... t = 2.14 α = 0.05, 14 degrees of freedom, and E = 0.10×192, which was a sufficient sample. In addition, in a previous study [44], 30 and 50 runs were tested for each scenario, and the results did not change significantly, indicating that 15 runs were a representative sample. As a step prior to the traffic microsimulation, a check of the modeled network validity was applied to verify if traffic operations (exposure and arrival) of the network matched the observed traffic operations in the case study. ...
... Table I and Section II show that TTC is the most commonly used measure in the literature. In particular, following the concept in literature, which indicates that the shorter reaction times of CAVs could make these vehicles more capable of significantly decreasing the TTC threshold [15], [19], [26], and considering a previously conducted sensitivity analysis [44] that showed a statistically significant difference when examining the change in the frequency of conflicts involving L4 vehicles under several TTC values (0.50, 1.00, 1.50, 2.00, and 2.50 s), this study used two different TTC thresholds: ...
Connected and Autonomous Vehicles (CAVs) are becoming a reality and are progressively penetrating the markets level by level. CAVs are a promising solution for traffic safety. However, robust studies are needed to explore and assess the expected behavior. This study attempts to evaluate traffic safety resulting from a near-real introduction of CAVs with different levels of automation (from Level 1 to Level 4). The investigation consisted of modeling different CAV levels using Gipps’ model, followed by the simulation of nine mixed fleets at a motorway segment. Subsequently, the Surrogate Safety Assessment Model was used for safety analysis. According to the results: (1) the gradual penetration of CAV levels led to a progressive reduction in traffic conflicts, ranging from 18.9% when the penetration of high levels of automation (Level 3 and Level 4 vehicles) is 5%, to 94.1% when all the vehicles on the traffic flow are Level 4; (2) human-driven vehicles and vehicles with low levels of automation (Level 1 and Level 2 vehicles) are more frequently involved in conflicts (as follower vehicles) than vehicles with high automation levels. E.g. human-driven vehicles are involved in conflicts from 8% to 122% more, while vehicles with high automation levels are involved in conflicts from 80% to 18% less than their sharing percentages, respectively, depending on different mixed fleets. This study confirms the theory and conclusions from previous literature that indicate a safety gain due to CAV penetration. Moreover, it provides a broader perspective and support for the introduction of CAVs levels.
... For more details about the validation process, refer to our previous analysis [16]. A preliminary analysis [26] was conducted to assign a statistically sufficient number of runs based on Shahdah et al.'s [35] equation, which was found to be 15 runs. Therefore, 15 runs were applied for each value for each parameter analysed in the one-at-a-time analysis, and then 15 runs were applied for each pair of examined values of the parameters analysed in the two-at-a-time sensitivity analysis. ...
Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.
... Accordingly, SSMs have been extensively employed to identify potential trafc conficts when CAVs share roads. In most previous studies, CAVs typically have a high automation level (i.e., L4) [3][4][5][6][7]. However, other studies have also included several levels of automation [8][9][10]. ...
This study aims to estimate the severity of conflicts that may arise from the introduction of connected and automated vehicles (CAVs) by examining the vehicle paths generated by microsimulations of mixed fleets of human-driven vehicles and CAVs with different levels of automation (L1-L4 vehicles). The study assesses the severity of conflicts using a holistic approach that considers three dimensions: (1) proximity to collision, via the time-to-collision (TTC) indicator; (2) potential consequences of a conflict, via single surrogate safety measures such as maximum speed (MaxS) and vehicle speed difference (DeltaS); and (3) a combination of both dimensions to assign severity scores, via TTC and velocity vectors. The study’s findings suggest that moderate penetration rates of L3 and L4 vehicles (35–55%) show significant differences in the number of traffic conflicts with varying TTC values. Additionally, high penetration rates of L3 and L4 vehicles (above 55%) result in lower values of conflict consequences measures such as MaxS and DeltaS. Furthermore, the study shows that conflict consequences decrease if the follower is a L3 or L4 vehicle. The study’s findings also reveal that there is a considerable reduction in high severity conflicts when the penetration rate of CAV levels reaches 50%, and the full operation of L4 vehicles results in a 75.5% reduction in high severity conflicts. Therefore, this study provides valuable insight into the potential severe conflicts during the transition period from manual vehicle operation to full CAV operation. Overall, the study’s findings highlight the importance of assessing the severity of potential conflicts arising from the introduction of CAVs. By considering the proximity to collision and the potential consequences of conflicts, the study provides a comprehensive assessment of the severity of conflicts. This information can inform the development of policies and strategies to ensure the safe and responsible introduction of CAVs into our transportation systems.
Connected and Autonomous Vehicles (CAVs) are expected to bring major transformations to transport efficiency and safety. Studies show a range of possible impacts, from worse efficiency of CAVs at low penetration rates, to significant improvements in both efficiency and safety at high penetration rates and loads. However, these studies tend to explore efficiency and safety separately, focus on one type of a road network, and include only cars rather than other vehicle types. This paper presents a comprehensive study on impact of CAVs on both efficiency and safety, in three types of networks (urban, national, motorway), simulating different penetration rates of vehicles with multiple levels of automation, using historical traffic data captured on Irish roads. Our study confirms existing results that near-maximum efficiency improvements are observed at relatively low penetration rates, but reveals further insights that the exact penetration ranges between 20% and 40% depending on the network type and traffic conditions. Safety results show a 30% increase of conflicts at lower penetration rates, but 50-80% reduction at higher ones, with consistent improvement for increased penetration. We further show that congestion has a higher impact on conflicts than penetration rates, highlighting the importance of unified evaluation of efficiency and safety.
Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.
Autonomous Vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from VISSIM traffic micro-simulator using Surrogate Safety Assessment Model (SSAM). Behaviours of Human-driven Vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car following model. The safety investigation is conducted for two case studies, including a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).
Autonomous vehicles offer a wide variety of potential benefits. One commonly discussed benefit is improved traffic operations (that is, decreased congestion, decreased delay, and improved efficiency) due to the way that autonomous vehicles are expected to behave in a traffic stream. In this research, we evaluate the effect of varying the percentage of autonomous vehicles in the overall vehicle fleet mix on transportation network performance.
This paper presents a new experimental approach to simulate projected autonomous driving styles based on the accelerations at three road profiles. This study was focused on the determination of ranges of accelerations in triaxial direction to simulate the autonomous driving experience. A special device, known as the Automatic Acceleration and Data controller (AUTOAccD), has been developed to guide the designated driver to accomplish the selected accelerations based on the road profiles and the intended driving styles namely assertive, defensive and light rail transit (LRT). Experimental investigations have been carried out at three different road profiles (junction, speed hump, and corner) with two designated drivers with five trials on each condition. A driving style with the accelerations of LRT has also been included in this study as it is significant to the present methodology because the autonomous car is predicted to accelerate like an LRT, in such a way that it enables the users to conduct activities such as working on a laptop, using personal devices or eating and drinking while travelling. The results demonstrated that 92 out of 110 trials of the intended accelerations for autonomous driving styles could be achieved and simulated on the real road by the designated drivers. The differences between the two designated drivers were negligible, and the rates of succeeding in realizing the intended accelerations were high. The present approach in simulating autonomous driving styles focusing on accelerations can be used as a tool for experimental setup involving autonomous driving experience and acceptance.
The need for safety in transportation systems has increased the popularity and applicability of Vehicular Ad-Hoc Networks (VANETs) in recent years. On-time reception and processing of alarms caused by possible accidents as well as the preventive actions have important roles in reducing human and financial losses in road accidents. In such cases, the performance of safety applications should be evaluated and guaranteed to show whether or not they can ensure the safety of humans and cars. In this paper, we analyze the behavior of Vehicular Ad-Hoc Networks by checking the real-time properties of the IEEE 802.11p protocol using a Colored Petri Net model. To analyze the performance of related standards, simulations are conducted using CPNTools. Standards from European Telecommunications Standards Institute (ETSI), and Vehicle Safety Communications (VSC) are evaluated in this research. We will show that such standards may not completely fulfill the safety requirements in particular situations.
Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.
Vehicle automation and communication systems (VACS) are expected to appear in an increasing amount of vehicles within the next years. Among the wide range of proposed VACS, the ones that include vehicle-to-infrastructure communication capabilities may be exploited both as sensors and as actuators. This enables traffic control centres to obtain more accurate information on the current traffic state and to assign to each vehicle appropriate control tasks, so as to achieve a global traffic flow target. The concept employs and exploits the synergistic (integrated) action of a number of old and new control measures, including ramp metering, vehicle speed control, and lane changing control, at a macroscopic level. The problem is tackled through a Quadratic Programming optimisation problem used as the core of a model predictive control framework. The optimal control actions may be sent directly to vehicles equipped with adaptive cruise control (ACC), affecting directly their cruise speed and, in addition, their lane-changing behaviour. The effectiveness and the computational feasibility of the proposed approach are demonstrated via microscopic simulation for a variety of ACC settings and penetration rates of equipped vehicles.
The primary objective of this paper is to provide a statistical relationship between traffic conflicts estimated from microsimulation and observed crashes in order to evaluate safety performance, in particular the effect of countermeasures. A secondary objective is to assess the effect of conflict risk tolerance and number of simulation runs on the estimates of countermeasure effects so obtained. Conflicts were simulated for a sample of signalized intersections from Toronto, Canada, using VISSIM microscopic traffic simulation and several crash–conflict relationships were obtained. A separate sample of treated intersections from Toronto was used to compare countermeasure effects from the integrated crash–conflict expression to a conventional, but rigorous crash-based Empirical Bayes before-and-after analysis that was already done, with the results published, for the same sites and treatment. The countermeasure considered for this investigation involved changing the left turn signal operation for the treated intersection sample from permissive to protected-permissive. The results support the view that countermeasure effects can be estimated reliably from conflicts derived from microsimulation, and more so when a suitable number of simulation runs and conflict tolerance thresholds are used in the crash–conflict relationship.
Work with developing a traffic-conflicts technique started at our department in 1973 and a technique for operational use was specified in 1974. Since then the technique has been modified and is still under further development, but many of the bases are unchanged, such as the basic hypothesis which says that there is a distinct relation between conflicts with a certain degree of seriousness and accidents.
Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
Various wireless communication systems exist, which enable a wide range of applications and use cases in the vehicular environment. These applications can be grouped into three types, namely, road safety, traffic efficiency, and infotainment, each with its own set of functional and performance requirements. In pursuance of assisting drivers to travel safely and comfortably, several of these requirements have to be met simultaneously. While the coexistence of multiple radio access technologies brings immense opportunities towards meeting most of the vehicular networking application requirements, it is equally important and challenging to identify the strength and weaknesses of each technology and understand which technology is more suitable for the given networking scenario. In this paper, we evaluate two of the most viable communication standards, Institute of Electrical and Electronics Engineers (IEEE) 802.11p and long-term evolution (LTE) by 3rd Generation Partnership Project for vehicular networking. A detailed performance evaluation study of the standards is given for a variety of parameter settings such as beacon transmission frequency, vehicle density, and vehicle average speed. Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements. Furthermore, through extensive simulation-based study, we validated the effectiveness of both standards to handle different application requirements and share insight for further research directions. The results indicate that IEEE 802.11p offers acceptable performance for sparse network topologies with limited mobility support. On the other hand, LTE meets most of the application requirements in terms of reliability, scalability, and mobility support; however, it is challenging to obtain stringent delay requirements in the presence of higher cellular network traffic load.
Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.
We use traffic simulations to quantify the impact of autonomous vehicles in various traffic scenarios, where vehicles at higher automation levels behave more opportunistically in car-following and lane-changing and can react to road situations more quickly. Our experimental results show that an increased automation level can improve traffic efficiency but may lead to more potential conflicts between vehicles, which should not be neglected if human drivers still need to take part in the driving.
This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from micro-simulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.
This study aims to analyze the impact of connected and autonomous vehicles (CAVs) on traffic safety under various penetration rates. Based on a recently proposed heterogeneous flow model, the mixed traffic flow with both conventional vehicles and CAVs was simulated and studied. The frequency of dangerous situations and value of time-to-collision in the mixed traffic flow under different CAV penetration rates was calculated and used as indicators of CAV’s impact on traffic safety. Acceleration rate and velocity difference distribution of the mixed traffic flow was presented to show the evolution of mixed traffic flow dynamics with the increase in CAV penetration rates within the mixed flow. Results show that the condition of traffic safety is greatly improved with the increase in the CAV penetration rate. More cautious car-following strategy of the CAV would contribute to a greater benefit on traffic safety, though less gain in capacity. With the increase in CAV penetration rate, the portion of smooth driving is increased. Velocity difference between vehicles is decreased and traffic flow is greatly smoothed. Stop-and-go traffic will be greatly eased.
Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12-47%, 50-80%, 82-92% and 90-94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
The objective of this study was to develop a heterogeneous traffic-flow model to study the possible impact of connected and autonomous vehicles (CAVs) on the traffic flow. Based on a recently proposed two-state safe-speed model (TSM), a two-lane cellular automaton (CA) model was developed, wherein both the CAVs and conventional vehicles were incorporated in the heterogeneous traffic flow. In particular, operation rules for CAVs are established considering the new characteristics of this emerging technology, including autonomous driving through the adaptive cruise control and inter-vehicle connection via short-range communication. Simulations were conducted under various CAV-penetration rates in the heterogeneous flow. The impact of CAVs on the road capacity was numerically investigated. The simulation results indicate that the road capacity increases with an increase in the CAV-penetration rate within the heterogeneous flow. Up to a CAV-penetration rate of 30%, the road capacity increases gradually; the effect of the difference in the CAV capability on the growth rate is insignificant. When the CAV-penetration rate exceeds 30%, the growth rate is largely decided by the capability of the CAV. The greater the capability, the higher the road-capacity growth rate. The relationship between the CAV-penetration rate and the road capacity is numerically analyzed, providing some insights into the possible impact of the CAVs on traffic systems.
Automated driving systems (ADSs) are expected to prevent traffic accidents caused by driver carelessness on freeways. There is no doubt regarding this safety benefit if all vehicles in the transportation system were equipped with ADSs; however, it is implausible to expect that ADSs will reach 100% market penetration rate (MPR) in the near future. Therefore, the following question arises: ‘Can ADSs, which consider only situations in the vicinity of an equipped vehicle, really contribute to a significant reduction in traffic accidents?’ To address this issue, the interactions between equipped and unequipped vehicles must be investigated, which is the purpose of this study. This study evaluated traffic safety at different MPRs based on a proposed index to represent the overall rear-end crash risk of the traffic stream. Two approaches were evaluated for adjusting longitudinal vehicle maneuvers: vehicle safety-based maneuvering (VSM), which considers the crash risk of an equipped vehicle and its neighboring vehicles, and traffic safety-based maneuvering (TSM), which considers the overall crash risk in the traffic stream. TSM assumes that traffic operational agencies are able to monitor all the vehicles and to intervene in vehicle maneuvering. An optimization process, which attempts to obtain vehicle maneuvering control parameters to minimize the overall crash risk, is integrated into the proposed evaluation framework. The main purpose of employing the optimization process for vehicle maneuvering in this study is to identify opportunities to improve traffic safety through effective traffic management rather than developing a vehicle control algorithm that can be implemented in practice. The microscopic traffic simulator VISSIM was used to simulate the freeway traffic stream and to conduct systematic evaluations based on the proposed methodology. Both TSM and VSM achieved significant reductions in the potential for rear-end crashes. However, TSM obtained much greater reductions when the MPR was greater than 50%. This study should inspire transportation researchers and engineers to develop effective traffic operations strategies for automated driving environments.
Googles dramatic ascent and subsequent domination in the past fifteen years of the technology and information industries has financially enabled Google to explore seemingly unrelated projects ranging from Google Mail to the Google Car. In particular, Google has invested a significant amount of resources in the Google Car, an integrated system that allows for the driverless operation of a vehicle. While initial reports indicate that the Google Car driverless automobile will be more safe and efficient than current vehicles, the Google Car is not without its critics. In particular, the existential threat that the car presents to several large industries, including the insurance, health care and construction industries, creates an additional challenge to the success of the Google Car well beyond the standard competitive threats from other established car manufacturers in the automobile industry, which begs the question, Can the Google Car be successful? With so many challenges above and beyond the competitive forces typically threatening long-term profitability, will the Google Car be able to create and sustain a competitive advantage for Google in the driverless car space?
IntelliDrive, the integration of vehicles and the infrastructure through wireless communication, has generated great interest in the transportation community. It is widely expected that IntelliDrive will support significantly improved transportation operations. However, there have been few studies investigating (1) how IntelliDrive will be used to improve operations, and (2) how to estimate expected benefits of IntelliDrive operations applications. In this study, the research team investigated the application of IntelliDrive to address merging conflicts created by freeway on-ramps. Although merging conflicts contribute significantly to freeway congestion, transportation engineers have limited options with existing traffic surveillance and management tools. IntelliDrive offers the potential for more active management of the surface transportation system, providing capabilities that may be of significant benefit in ramp management. In this research, a lane changing advisory algorithm was developed to explore the potential of IntelliDrive in improving freeway ramp management. Utilizing new IntelliDrive capabilities (highly detailed individual vehicular data and personalized advisory information provision), the proposed algorithm attempts to reduce merging conflicts by encouraging early mainline freeway lane changes to create more space in ramp merging areas. An evaluation of the algorithm was conducted by using a PARAMICS microscopic simulation model of a heavily traveled freeway network in Orange County, California for morning peak hours. The results have revealed that IntelliDrive has the potential to increase vehicle miles traveled by up to 4.3% and reduce vehicle hours traveled by up to 4.6%, which resulted in up to 9.3% higher average speeds, in the best case. However, the results also indicated that, to achieve the greatest possible benefits, the proposed algorithm requires very high compliance of drivers and near full deployment of IntelliDrive.
The IEEE 802.11 working group proposed a standard for the physical and medium access control layers of vehicular networks called 802.11p. In this paper we report experimental results obtained from communication between vehicles using 802.11p in a real scenario. The main motivation is the lack of studies in the literature with performance data obtained from off-the-shelf 801.11p devices. Our study characterizes the typical conditions of an 802.11p point-to-point communication. Such a study serves as a reference for more refined simulation models or to motivate enhancements in the PHY/MAC layers. Field tests were carried out varying the vehicle's speed between 20 and 60 km/h and the packet length between 150 and 1460 bytes, in order to characterize the range, throughput, latency, jitter and packet delivery rates of 802.11p links. It was observed that communication with vehicles in motion is unstable sometimes. However, it was possible to transfer data at distances over 300 m, with data rates sometimes exceeding 8 Mbit/s.
The ability to predict the response of a vehicle in a stream of traffic to the behaviour of its predecessor is important in estimating what effect changes to the driving environment will have on traffic flow. Various proposed to explain this behaviour have different strengths and weaknesses. The paper constructs a new model for the response of the following vehicle based on the assumption that each driver sets limits to his desired braking and acceleration rates. The parameters in the model correspond directly to obvious characteristics of driver behaviour and the paper goes on to show that when realistic values are assigned to the parameters in a simulation, the model reproduces the characteristics of real traffic flow.
A structure is proposed to connect the decisions which a driver has to make before changing lanes. The model is intended to cover the urban driving situation, where traffic signals, obstructions and heavy vehicles all exert an influence. The structure is designed to ensure that the vehicles in traffic simulations behave logically when confronted with situations commonly encountered in real traffic. The specific mathematical expression of the questions embedded in the decision process and employed in the present implementation of the model are not critical and can be replaced by alternatives, but the heirarchy of the decisions is crucial. On the basis of experience to date, the lane changing model produces a realistic simulation of driver behaviour and has proved very robust under a wide range of conditions.
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