Journal articles on the topic 'Connected vehicle (CV)'
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1
Wang, Xingmin, Shengyin Shen, Debra Bezzina, James R. Sayer, Henry X. Liu, and Yiheng Feng. "Data Infrastructure for Connected Vehicle Applications." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 9, 2020): 85–96. http://dx.doi.org/10.1177/0361198120912424.
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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.
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Shao, Yunli, Mohd Azrin Mohd Zulkefli, and Zongxuan Sun. "Vehicle and Powertrain Optimization for Autonomous and Connected Vehicles." Mechanical Engineering 139, no. 09 (September 1, 2017): S19—S23. http://dx.doi.org/10.1115/1.2017-sep-6.
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This article discusses the potential of using autonomous and connected vehicle (CV) technologies to save energy. It also focuses on the potential energy savings of internal combustion engine-based vehicles (ICVs) and hybrid electric vehicles (HEVs). An example of vehicle and powertrain co-optimization for HEV eco-approaching and departure is also given. CV technologies are gaining increasing attention around the world. Vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication enable real-time access to traffic information that was not available before, including preceding vehicles’ location, speed, pedal position, traffic signal phasing and timing (SPaT). The example shown in this article demonstrates the potential benefits from vehicle and powertrain co-optimization by investigating an eco-approaching and departure application. More research in this area can offer more mature solutions to implement such optimization in a real-production vehicle.
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Wei, Lu, Jin-hong Li, Li-wen Xu, Lei Gao, and Jian Yang. "Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment." Journal of Advanced Transportation 2022 (May 2, 2022): 1–11. http://dx.doi.org/10.1155/2022/9568723.
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Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.
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Miao, Zuoyu, K. Larry Head, and Byungho Beak. "Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (May 30, 2018): 160–72. http://dx.doi.org/10.1177/0361198118774691.
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Deployment of connected vehicles will become possible for most American cities in the next 10 to 20 years. Connected vehicle (CV) applications (e.g., mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle’s ID changes every 5 minutes, so the topic of rematching vehicles is of interest in privacy protection and performance measure research. This paper explores the possibility of rematching connected vehicles’ IDs using popular machine learning techniques, including logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear and nonlinear support vector machine (SVM) and nearest neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a software-in-the-loop technique. The best average mismatching rate is 14%. To assess potential factors’ effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measurement of future CV applications feasible and also suggest that more secure mechanisms are needed to protect the public.
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Chen, Bo, Darrell Robinette, Mahdi Shahbakhti, Kuilin Zhang, Jeff Naber, Jeremy Worm, Christopher Pinnow, and Christopher Morgan. "Connected Vehicles and Powertrain Optimization." Mechanical Engineering 139, no. 09 (September 1, 2017): S12—S18. http://dx.doi.org/10.1115/1.2017-sep-5.
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This article discusses the basic concept of connected and automated vehicles (CAV) technology. The common methods to improve fuel economy are also introduced. The effects of connectivity on vehicle/powertrain control and optimization are also elaborated. The Michigan Tech NEXTCAR project is also presented to provide a more detailed view of predictive vehicle/powertrain control enabled by CAV technologies. The U.S. Department of transportation (DOT) and other federal/state funding agencies have supported research and pilot deployment efforts to develop crosscutting CV technologies and evaluate the effectiveness of CV technologies in real-world transportation systems. The concurrent development of connected and automated vehicle technologies is anticipated to provide synergistic benefits to improve traffic safety, mobility, and energy efficiency. It is observed that increased CAV technologies are being deployed in real-world transportation systems.
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Finley, Melisa D., Cameron R. Mott, Kevin N. Balke, Hassan Charara, Purser K. Sturgeon, David Florence, and Chiara Silvestri Dobrovolny. "Development and Testing of Connected Vehicle Wrong-Way Driving Applications." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 59–69. http://dx.doi.org/10.1177/0361198119839986.
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The Texas A&M Transportation Institute (TTI) and Southwest Research Institute (SwRI) recently developed connected vehicle (CV) applications that detect wrong-way vehicles, warn wrong-way drivers, notify traffic management agencies and law enforcement, and alert affected travelers. The research team reviewed the state of the practice regarding intelligent transportation systems (ITS) and CV technologies being applied as wrong-way driving (WWD) countermeasures. Next, the research team identified user needs associated with the implementation of CV WWD applications, and developed a concept of operations and functional requirements for CV WWD applications. The research team then built, tested, and successfully conducted a proof-of-concept demonstration of the CV WWD applications at the Texas A&M University RELLIS Campus.
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Chandan, Kancharla Kamal Keerthi, Álvaro Jorge Maia Seco, and Ana Maria César Bastos Silva. "A Real-time Traffic Signal Control Strategy Under Partially Connected Vehicle Environment." PROMET - Traffic&Transportation 31, no. 1 (February 27, 2019): 61–73. http://dx.doi.org/10.7307/ptt.v31i1.2832.
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The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.
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Bertini, Robert L., Haizhong Wang, and Kevin Carstens. "Preparing Oregon for Connected Vehicle Deployment: Application Prioritization Process." Transportation Research Record: Journal of the Transportation Research Board 2615, no. 1 (January 2017): 1–10. http://dx.doi.org/10.3141/2615-01.
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To build on a project recently completed for the Oregon Department of Transportation (DOT), processes and tools were developed to prioritize the implementation of connected vehicle (CV) applications. Internal mechanisms for addressing CV development and deployment at the Oregon DOT were assessed; the technical maturity of each potential CV application was scanned, reviewed, and assessed; preliminary goals were developed; prospective CV applications were linked; and applications that fit with the potential role of the Oregon DOT in advancing these initiatives were refined, prioritized, and ranked. A shared vision and business plan that prioritizes CV applications for Oregon is recommended. An internal effort aimed at producing a small set of priority CV applications for further development is described. This effort culminated in a CV application prioritization workshop that included a priority mapping exercise, discussion of the CV concept, and an initial mapping of goals and applications. The workshop identified seven near-term priority CV applications for the Oregon DOT; 12 applications that the Oregon DOT will monitor (and possibly collaborate on with others in the future); and eight applications that the Oregon DOT will monitor but that will be led by others. The Oregon DOT has used the results of this effort as a springboard for hiring new staff dedicated to CV policy, forming a CV steering team, and launching a CV business plan. The process and tools can be used by other states and transportation agencies in their CV application prioritization processes.
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Rahman, Md Sharikur, Mohamed Abdel-Aty, Ling Wang, and Jaeyoung Lee. "Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 19 (June 11, 2018): 91–101. http://dx.doi.org/10.1177/0361198118776113.
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This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.
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Chowdhury, Mashrur, Mizanur Rahman, Anjan Rayamajhi, Sakib Mahmud Khan, Mhafuzul Islam, Zadid Khan, and James Martin. "Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 22 (October 6, 2018): 10–23. http://dx.doi.org/10.1177/0361198118799034.
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The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.
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Yang, Guangchuan, Mohamed M. Ahmed, and Biraj Subedi. "Distraction of Connected Vehicle Human–Machine Interface for Truck Drivers." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 7, 2020): 438–49. http://dx.doi.org/10.1177/0361198120929692.
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Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings, commonly displayed on a human–machine interface (HMI), thus reducing the likelihood of crashes caused by human error. Nevertheless, the presence of an in-vehicle CV HMI may pose an increasing threat to driver distraction, particularly for truck drivers and under high workload driving conditions. With this concern, this research investigated the effects of a HMI developed by the Wyoming Department of Transportation CV Pilot on truck drivers’ cognitive distraction and driving behavior through a driving simulator experiment. Revealed preference survey and vehicle dynamics data were employed to assess the cognitive distractions of the Pilot’s HMI. Simulation results indicated that when CV warnings were displayed on the HMI, they did not introduce significant effects on participants’ longitudinal and lateral control of the vehicle. Nevertheless, from the revealed preference survey, it was found that approximately 27% of the participants indicated that the CV HMI tended to introduce additional visual workload for them, particularly when approaching an active freeway work zone under reduced visibility condition. In this regard, this research pointed out that the design of CV warnings and HMI displays needs to incorporate drivers’ ability to recognize and react safely to the received CV warnings to minimize the cognitive distractions introduced by the CV HMI.
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Chandan, Kancharla K. K., Álvaro J. M. Seco, and Ana M. C. Bastos Silva. "Real-Time Incident-Responsive Signal Control Strategy under Partially Connected Vehicle Environment." Journal of Advanced Transportation 2022 (July 22, 2022): 1–16. http://dx.doi.org/10.1155/2022/8970695.
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The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle.
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Bakhit, Peter R., Osama A. Osman, and Sherif Ishak. "Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (January 2017): 168–75. http://dx.doi.org/10.3141/2645-18.
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Lane changing is a complex decision-making process that is affected by factors such as vehicle features, driver characteristics, network attributes, and traffic conditions. Understanding the changes in driver behavior and vehicle trajectory before the lane change initiation process is essential to the design of a safe and reliable crash avoidance system. The recently introduced connected vehicle (CV) technology provides opportunities for real-time, high-resolution data exchange capability between vehicles. This study explored the high-resolution vehicle trajectory data attainable in CV environments for detecting the onset of lane change maneuvers. The observed change in behavior before the initiation of such a maneuver was examined to identify the associated driving pattern. This pattern was used to develop two lane change detection models: an artificial neural network (ANN) model and a multiple logistic regression (MLR) model. The two models were trained and tested with Next Generation Simulation data collected from a weaving freeway segment in Arlington, Virginia. The results show 80% detection accuracy for the ANN model, compared with 72% for the MLR model. The developed models identified the vehicle speed, acceleration, and speed relative to the lead vehicle as the most significant attributes for lane change detection. Drivers’ intentions could be detected early and potential crashes could be prevented by training these models to capture similar driving behavior patterns.
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Islam, Mhafuzul, Mizanur Rahman, Sakib Mahmud Khan, Mashrur Chowdhury, and Lipika Deka. "Development and Performance Evaluation of a Connected Vehicle Application Development Platform." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (May 2020): 537–52. http://dx.doi.org/10.1177/0361198120917146.
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Connected vehicle (CV) application developers need a development platform to build, test, and debug real-world CV applications, such as safety, mobility, and environmental applications, in edge-centric cyber-physical system (CPS). The objective of this paper is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables application developers to build, test, and debug CV applications in real-time while meeting the functional requirements of any CV applications. The efficacy of the CVDeP was evaluated using two types of CV applications (one safety and one mobility application) and they were validated through field experiments at the South Carolina Connected Vehicle Testbed (SC-CVT). The analyses show that the CVDeP satisfies the functional requirements in relation to latency and throughput of the selected CV applications while maintaining the scalability and security of the platform and applications.
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Yu, Bin, Miyi Wu, Shuyi Wang, and Wen Zhou. "Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment." International Journal of Environmental Research and Public Health 16, no. 22 (November 8, 2019): 4373. http://dx.doi.org/10.3390/ijerph16224373.
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Connected vehicles (CVs) exchange a variety of information instantly with surrounding vehicles and traffic facilities, which could smooth traffic flow significantly. The objective of this paper is to analyze the effect of CVs on running speed. This study compared the delay time, travel time, and running speed in the normal and the connected states, respectively, through VISSIM (a traffic simulation software developed by PTV company in German). The optimization speed model was established to simulate the decision-makings of CVs in MATLAB, considering the parameters of vehicle distance, average speed, and acceleration, etc. After the simulation, the vehicle information including speed, travel time, and delay time under the normal and the connected states were compared and evaluated, and the influence of different CV rates on the results was analyzed. In a two-lane arterial road, running speed in the connected state increase by 4 km/h, and the total travel time and delay time decrease by 5.34% and 16.76%, respectively, compared to those in the normal state. The optimal CV market penetration rate related to running speed and delay time is 60%. This simulation-based study applies user-defined lane change and lateral behavior rules, and takes different CV rates into consideration, which is more reliable and practical to estimate the impact of CV on road traffic characteristics.
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Wang, Jiangfeng, Jiarun Lv, Chao Wang, and Zhiqi Zhang. "Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics." Journal of Advanced Transportation 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/6905431.
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A route choice prediction model is proposed considering the connected vehicle guidance characteristics. This model is proposed to prevent the delay in the release of guidance information and route planning due to inaccurate timing predictions of the traditional guidance systems. Based on the analysis of the impact of different connected vehicle (CV) guidance strategies on traffic flow, an indexes system for CV guidance characteristics is presented. Selecting five characteristic indexes, a route choice prediction model is designed using the logistic model. A simulation scenario is established by programming different agents for controlling the flow of vehicles and for information acquisition and transmission. The prediction model is validated using the simulation scenario, and the simulation results indicate that the characteristic indexes have a significant influence on the probability of choosing a particular route. The average root mean square error (RMSE) of the prediction model is 3.19%, which indicates that the calibration model shows a good prediction performance. In the implementation of CV guidance, the penetration rate can be considered an optional index in the adjustment of the guidance effect.
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Guo, Xiaoyu, Yongxin Peng, Sruthi Ashraf, and Mark W. Burris. "Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 20, 2020): 120–33. http://dx.doi.org/10.1177/0361198120940311.
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Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.
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Gao, Kai, Shuo Huang, Jin Xie, Neal N. Xiong, and Ronghua Du. "A Review of Research on Intersection Control Based on Connected Vehicles and Data-Driven Intelligent Approaches." Electronics 9, no. 6 (May 26, 2020): 885. http://dx.doi.org/10.3390/electronics9060885.
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Benefiting from the application of vehicle communication networks and new technologies, such as connected vehicles, video monitoring, automated vehicles and vehicle–road collaboration, traffic network data can be observed in real-time. Applied in the field of traffic control, these technologies can provide high-quality input data and make a more comprehensive evaluation of the effectiveness of traffic control. However, most of the control theories and strategies adopted by adaptive control systems cannot effectively use these real-time, high-precision data. In order to adapt to the development of the times, intersection control theory needs to be further developed. This paper reviews the intersection control strategies from many perspectives, including intelligent data-driven control, conventional timing control, induction control and model-based traffic control. There are three main directions for intersection control based on the connected vehicle environment: (1) data-driven reinforcement learning control; (2) adaptive performance optimization control; (3) research on traffic control based on the environment of connected vehicles (CV); and (4) multiple intersection control based on the CV environment. The review gives a clear view of the data-driven intelligent control theory and its application for intelligent transportation systems.
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Cvijovic, Zorica, Milan Zlatkovic, Aleksandar Stevanovic, and Yu Song. "Multi-Level Conditional Transit Signal Priority in Connected Vehicle Environments." Put i saobraćaj 67, no. 2 (June 14, 2021): 1–12. http://dx.doi.org/10.31075/pis.67.02.01.
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Connected Vehicles (CV) are an emerging technology with a large potential to improve traffic operations and safety. This paper develops and tests advanced CV-based multi-level conditional Transit Signal Priority (TSP). The algorithms are using the latitude/longitude (lat/lon) coordinates of CV vehicles and intersections to establish communication, share information and request priority. The TSP strategies are implemented through controllers’ built-in features and logic processor, using Econolite ASC/3 as a representative traffic signal controller. The tests were performed in VISSIM microsimulation with the ASC/3 Software-in-the-Loop (SIL) controller emulator. State Street in Salt Lake City, UT, is selected as a test-case corridor. The paper shows that the developed signal control priority (SCP) algorithms are successful in reducing delays for target vehicles in excess of 6%, without significant impacts on other traffic. The information obtained from CV vehicles can be used to further enhance control algorithms and create adaptive SCP programs.
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Mjogolo, Festo, and Thobias Sando. "Operational Evaluation of Effectiveness of Connected Vehicle Smartphone Technology on a Signalized Corridor." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 17, 2020): 1041–51. http://dx.doi.org/10.1177/0361198120933630.
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Various connected vehicle (CV) technology applications reported in current literature have the potential to solve the challenges faced by the transportation sector. Over the last decade, extensive research efforts have focused on performance evaluation and the benefits of innovative CV applications; findings indicate that CV technology can effectively mitigate the safety, mobility, and environmental challenges experienced on today’s transportation networks. The majority of previous research has evaluated CV technology through simulation studies. However, a field study is the ideal method of assessing CV technology effectiveness. Field observational studies to validate previous research findings have not been conducted. Therefore, a field study to obtain the actual effectiveness of CV technology was warranted, not only to validate previous findings, but to add to the body of knowledge surrounding this topic. This research presents a field study evaluation of the effectiveness of CV smartphone technology on a 1.1-mi segment of State Road 121, containing five intersections, in Gainesville, Florida. Field observations were conducted using a CV application that uses a smartphone application, EnLighten (Connected Signals, Inc., Oregon, United States), to communicate intersection information to a driver’s smartphone, which serves as a vehicle on-board unit. Traffic operation performance was evaluated using a start-up lost time and discharge distribution model. Findings showed that the CV smartphone technology improved intersection performance with a reduction in start-up lost time of approximately 86%. The CV technology had no impact on the distribution model of position-dependent discharge headways.
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Ahmed, Mohamad M., Guangchuan Yang, and Sherif Gaweesh. "Development and Assessment of a Connected Vehicle Training Program for Truck Drivers." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 2 (February 2019): 113–26. http://dx.doi.org/10.1177/0361198119827904.
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In Wyoming, because of the high likelihood of dangerous winter driving conditions, truck-related crashes on the freeway have resulted in remarkable fatalities, extended closures, and significant economic losses. The emerging connected vehicle (CV) technology has the potential to communicate road and traveler information to commercial truck drivers, which would eventually promote traffic safety. Nevertheless, currently there is a lack of a systems training program to deliver the CV technology to truck drivers. This paper developed a CV training program which contained an e-learning conceptual education module and a hands-on driving simulator training module. The e-learning module presented the concept of various CV warnings and notifications, including forward collision warning (FCW), distress notification (DN), and traveler information messages (TIMs). The hands-on driving simulator training module provided participants with a simulated environment where they could practice the CV warnings that they had met during the e-learning. Five simulation scenarios were provided to the participants, including two single-alert scenarios and three multiple-alert scenarios. A quiz section in the e-learning module and a comprehensive post-training questionnaire survey within the hands-on module were employed to evaluate the effectiveness of the developed CV training program. Based on the feedback of 18 professional snowplow truck drivers from the Wyoming Department of Transportation, it was found that the proposed training program provided truck drivers with up-to-date knowledge and experience on CV technology.
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Li, Howell, Tom Platte, Jijo Mathew, W. Benjamin Smith, Enrique Saldivar-Carranza, and Darcy M. Bullock. "Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 9, 2020): 305–14. http://dx.doi.org/10.1177/0361198120914606.
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The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.
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Yi, Ziwei, Wenqi Lu, Xu Qu, Linheng Li, Peipei Mao, and Bin Ran. "Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis." Sensors 21, no. 24 (December 13, 2021): 8322. http://dx.doi.org/10.3390/s21248322.
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Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β1 or β2 is the biggest the best for the traffic.
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Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 70–83. http://dx.doi.org/10.1177/0361198119841285.
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A joint traffic signal optimization algorithm is proposed which utilizes connected vehicle (CV) information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize total number of stopping maneuvers. The contribution of this paper is provision of speed guidance to both autonomous (AVs) and human-driven speed guidance-enabled vehicles (SGVs), recognizing that the latter may not fully comply with the speed guidance and would require some delay (i.e., reaction time) to implement it. The control algorithm is triggered at regular discrete time intervals during which CV information is used to identify the presence of non-CVs and incorporate them into signal timing decision-making. Optimal speeds are determined for any AVs or SGVs so that they can travel through the intersection at the expected departure time without stopping, considering both acceleration/deceleration and human reaction times. Simulation tests are performed under different CV, AV, and SGV penetration rates, while explicitly modeling the potential human errors and varying acceptance levels. The results suggest that average delay and number of stops decrease with higher CV penetration rate. Furthermore, the number of stops decreases as the ratio of both AVs and SGVs increases. While AVs are about 10% more efficient than SGVs, human-driven vehicles still provide a benefit even when they do not fully comply with speed guidance information. Sensitivity tests suggest that operation is not significantly affected by the range of human driver errors in speed compliance or range of reaction times.
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S. Raj, Jennifer. "Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors." March 2021 3, no. 2 (July 17, 2021): 93–106. http://dx.doi.org/10.36548/jucct.2021.2.003.
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The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.
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Wu, Wei, Ling Huang, and Ronghua Du. "Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment." Sensors 20, no. 1 (December 29, 2019): 191. http://dx.doi.org/10.3390/s20010191.
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Most existing signal timing plans are optimized given vehicles’ arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. “Speed guidance model at a red light” and “speed guidance model at a green light” are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions.
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Khazraeian, Samaneh, Mohammed Hadi, and Yan Xiao. "Safety Impacts of Queue Warning in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2621, no. 1 (January 2017): 31–37. http://dx.doi.org/10.3141/2621-04.
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Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.
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Li, Weixia, Guoyuan Wu, Kanok Boriboonsomsin, Matthew J. Barth, Samer Rajab, Sue Bai, and Yi Zhang. "Development and Evaluation of High-Speed Differential Warning Application Using Vehicle-to-Vehicle Communication." Transportation Research Record: Journal of the Transportation Research Board 2621, no. 1 (January 2017): 81–91. http://dx.doi.org/10.3141/2621-10.
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Connected vehicle (CV) technology has shown great potential in enhancing safety, mobility, and environmental sustainability of roadway transportation systems by enabling vehicles and infrastructure to exchange real-time information through wireless communications. The information available through CV technology has been used to develop a variety of onboard driver assistance tools and applications. This paper presents an innovative, safety-focused CV application called the high-speed differential warning (HSDW). The application identifies potential hazards resulting from high-speed differentials between HSDW-equipped vehicles (ego-vehicles) and surrounding remote vehicles and then provides alerts to drivers of ego-vehicles to help them take appropriate actions. In this research, real-world scenarios and parameters of the HSDW application are first identified. A driver response strategy for each scenario is then developed in consonance with findings from a public survey on the HSDW application. Next, a comprehensive evaluation of various scenarios is conducted with a well-calibrated Paramics traffic microscopic simulation tool set up for a real-world traffic network. Finally, the safety performance of HSDW-equipped vehicles, unequipped vehicles, and overall traffic is analyzed with the surrogate safety assessment model. Results demonstrate that the proposed HSDW application improves the safety performance of ego-vehicles without compromising the mobility and environmental sustainability performance of the overall traffic.
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Omidvar, Aschkan, Mahmoud Pourmehrab, Patrick Emami, Rebecca Kiriazes, John C. Esposito, Clark Letter, Lily Elefteriadou, Carl D. Crane, and Sanjay Ranka. "Deployment and Testing of Optimized Autonomous and Connected Vehicle Trajectories at a Closed-Course Signalized Intersection." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 19 (June 21, 2018): 45–54. http://dx.doi.org/10.1177/0361198118782798.
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In this paper, we discuss the deployment and testing of an intelligent real-time isolated intersection traffic control system (IICS), designed to optimize simultaneously signal control and automated vehicle (AV) and connected vehicle (CV) trajectories for low demand condition. The work described here is part of an ongoing larger project (funded by the National Science Foundation and the Florida Department of Transportation, FDOT) to develop, test, and deploy the IICS. The focus of this paper is on the deployment and testing of the algorithm at the Traffic Engineering and Research Laboratory (TERL), FDOT’s closed-course facility. The algorithm (described in more detail elsewhere) optimizes signal control and provides optimal AV and CV trajectories at isolated intersections. The algorithm is designed to handle AV, CV, and conventional vehicles in a mixed traffic and low demand condition. The paper provides an overview of the IICS framework, discusses the development and testing of the necessary software and hardware, and presents the scenarios tested at the TERL. The results from the field test confirm the feasibility of the IICS, and will be used to enhance it for future testing and ultimately for field deployment and in high demand condition.
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Abdelkader, Ghadeer, Khalid Elgazzar, and Alaa Khamis. "Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities." Sensors 21, no. 22 (November 19, 2021): 7712. http://dx.doi.org/10.3390/s21227712.
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In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.
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He, Yaqin, Md Tawhidur Rahman, Michelle Akin, Yinhai Wang, Kakan Dey, and Xianming Shi. "Connected Vehicle Technology for Improved Multimodal Winter Travel: Agency Perspective and a Conceptual Exploration." Sustainability 12, no. 12 (June 22, 2020): 5071. http://dx.doi.org/10.3390/su12125071.
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Accurate and real-time traffic and road weather information acquired using connected vehicle (CV) technologies can help commuters perform safe and reliable trips. A nationwide survey of transit operation managers/supervisors was conducted to assess the suitability for CV transit applications in improving the safety and mobility during winter weather. Almost all respondents expressed positive attitudes towards the potential of CV applications in improving winter transit travel and voiced their concerns over the safety consequences of CV equipment failure, potential of increased driver distraction, and reliability of system performance in poor weather. A concept of operations of CV applications for multimodal winter travel was developed. In the conceptual framework, route-specific road weather and traffic flow data will be used by the transit managers/supervisors to obtain real-time operational status, forecast operational routes and schedules, and assess operational performance. Subsequently, multimodal commuters can receive the road-weather and traffic-flow information as well as transit routes and schedule information.
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Wang, Xu, Jiangchen Li, Can Zhang, and Tony Z. Qiu. "Active Warning System for Highway-Rail Grade Crossings Using Connected Vehicle Technologies." Journal of Advanced Transportation 2019 (February 11, 2019): 1–11. http://dx.doi.org/10.1155/2019/3219387.
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Highway-rail grade crossing (HRGC) collisions are a significant safety concern around the world. HRGC collisions have a high risk of injuries and fatalities. To mitigate that risk, safety countermeasures for both active and passive HRGCs have been implemented. Leveraging the latest developments in connected vehicle (CV) technologies, CV-based warning systems perform well in safety applications for roadway networks. However, few have been developed to focus on safety improvements specifically for HRGCs. To bridge this gap, this paper proposes a novel active warning system that was created with readily available CV technologies and devices. A crossing risk assessment model was developed and evaluated in simulation and field applications. The proposed model predicts the crossing risk probabilities in the near future. When road users are in great risk of a collision, the warning system sends out auditory and visual alerts and displays the estimated waiting time. The test results reveal that the proposed warning system is promising for field implementation to improve safety at grade crossings.
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Li, Jiangchen, Chen Qiu, Mudasser Seraj, Liqun Peng, and Tony Z. Qiu. "Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 21, 2019): 36–48. http://dx.doi.org/10.1177/0361198119837505.
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Although connected vehicle (CV)-based signal coordination has some proposed prototypes and has been investigated by several different strategies, the existing works have issues that require attention, including additional CV-based system uncertainties and platoon priority request considerations. Thus, a generalized framework consisting of a platoon-based bicyclic coordination diagram (Bi-PCD), a new probabilistic surrogate quantification, and a platoon priority-based offset optimization in the CV environment is proposed to improve the coordination performance. The proposed Bi-PCD extends the scope of the current Purdue coordination diagram (PCD) and its variants by covering extra practical and emerged variables. A prototype and field tests in a CV test pilot for general scenarios were implemented in a typical arterial road to verify performances of the proposed Bi-PCD and the offset optimization method. Field results demonstrated that using Bi-PCD could obtain explicit platoon features in both limited and full CV penetration conditions. The comprehensive analysis showed that the proposed Bi-PCD and the platoon priority-based offset optimization could further improve performances of the signal coordination.
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Wang, Pang-wei, Hong-bin Yu, Lin Xiao, and Li Wang. "Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion." Journal of Sensors 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/7248189.
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With the development of connected vehicle (CV) and Vehicle to X (V2X) communication, more traffic data is being collected from the road network. In order to predict future traffic condition from connected vehicles’ data in real-time, we present an online traffic condition evaluation model utilizing V2X communication. This model employs the Analytic Hierarchy Process (AHP) and the multilevel fuzzy set theory to fuse multiple sources of information for prediction. First, the contemporary vehicle data from the On Board Diagnostic (OBD) is fused with the static road data in the Road Side Unit (RSU). Then, the real-time traffic evaluation scores are calculated using the variable membership model. The real data collected by OBU in field test demonstrates the feasibility of the evaluation model. Compared with traditional evaluation systems, the proposed model can handle more types of data but demands less data transfer.
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Wang, Qinzheng, Xianfeng (Terry) Yang, Blaine D. Leonard, and Jamie Mackey. "Field Evaluation of Connected Vehicle-Based Transit Signal Priority Control under Two Different Signal Plans." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 7 (June 2, 2020): 172–80. http://dx.doi.org/10.1177/0361198120921161.
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In 2017, a connected vehicle (CV) corridor utilizing dedicated short-range communication (DSRC) technology was built along Redwood Road, Salt Lake City, Utah. One main goal of this CV corridor is to implement transit signal priority (TSP) when the bus is behind its published schedule by a certain threshold. With the data generated by the transit vehicles, transmitted through the DSRC system, logged by traffic signal controller, and coupled with the Utah Transit Authority (UTA) data from transit operation system, some performance data of the TSP can be analyzed including TSP requested, TSP served, bus reliability, bus travel time, and bus running time. For providing better signal coordination to buses, the signal plan for this CV corridor underwent retiming in October 2018. This research aims to compare the TSP performance before and after the signal retiming. The field data of August, September, November, and December in 2018 were selected to perform this evaluation. Results show that the TSP served rate after signal retiming is 35.29%, which is higher than that of 33.12% before signal retiming. In addition, compared with the signal plan before October, bus reliability northbound and southbound on the CV corridor was improved by 2.4% and 1.47%, respectively; bus travel time and bus running time were reduced as well.
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Arafat, Mahmoud, Shahadat Iqbal, and Mohammed Hadi. "Utilizing an Analytical Hierarchy Process with Stochastic Return On Investment to Justify Connected Vehicle-Based Deployment Decisions." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 12, 2020): 462–72. http://dx.doi.org/10.1177/0361198120929686.
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With the increasing interest in connected vehicles (CV), it becomes all the more important to support decisions by transportation agencies to invest in Connected Vehicle to Infrastructure (V2I) applications. This paper presents a method that can be used to justify the investment in CV-based safety applications considering the availability of existing solutions. The method utilizes a combination of stochastic return on investment (ROI) analysis and a multi-criteria decision-analysis (MCDA) procedure to account for uncertainties, to consider effects that cannot be converted to dollar values, and to account for stakeholder priorities. The stochastic ROI analysis is applied using Monte Carlo simulations and included as part of the selection criteria in the MCDA method using the Analytical Hierarchy Process (AHP). This paper applies the method to support the deployment of CV-based applications to address transportation safety concerns on urban arterials. These applications can be categorized as CV-based support of signalized intersection safety, CV-based support of unsignalized intersection safety, and CV-based hazard warning applications. The results of the Monte Carlo simulation analysis for a project case study indicated the cost-effectiveness of these applications. The results of the AHP analysis indicate that utilizing V2I applications is 41.3% more favorable than utilizing the investigated existing solutions to address safety concern on the arterial facility that is the subject of the case study.
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Zhao, Wei, Jiateng Yin, Xiaohan Wang, Jia Hu, Bozhao Qi, and Troy Runge. "Real-Time Vehicle Motion Detection and Motion Altering for Connected Vehicle: Algorithm Design and Practical Applications." Sensors 19, no. 19 (September 23, 2019): 4108. http://dx.doi.org/10.3390/s19194108.
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Real-time capturing of vehicle motion is the foundation of connected vehicles (CV) and safe driving. This study develops a novel vehicle motion detection system (VMDS) that detects lane-change, turning, acceleration, and deceleration using mobile sensors, that is, global positioning system (GPS) and inertial ones in real-time. To capture a large amount of real-time vehicle state data from multiple sensors, we develop a dynamic time warping based algorithm combined with principal component analysis (PCA). Further, the designed algorithm is trained and evaluated on both urban roads and highway using an Android platform. The aim of the algorithm is to alert adjacent drivers, especially distracted drivers, of potential crash risks. Our evaluation results based on driving traces, covering over 4000 miles, conclude that VMDS is able to detect lane-change and turning with an average precision over 76% and speed, acceleration, and brake with an average precision over 91% under the given testing data dataset 1 and 4. Finally, the alerting tests are conducted with a simulator vehicle, estimating the effect of alerting back or front vehicle the surrounding vehicles’ motion. Nearly two seconds are gained for drivers to make a safe operation. As is expected, with the help of VMDS, distracted driving decreases and driving safety improves.
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Do Nascimento, Douglas Aguiar, Yuzo Iano, Hermes José Loschi, Navid Razmjooy, Robert Sroufe, Vlademir De Jesus Silva Oliveira, Diego Arturo Pajuelo Castro, and Matheus Montagner. "Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges." Transactions on Environment and Electrical Engineering 3, no. 1 (March 18, 2019): 44. http://dx.doi.org/10.22149/teee.v3i1.130.
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This review addresses the intervehicular communication in Connected Vehicles (CV) by emphasizing V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications in terms of evolution, current standards, state-of-the-art studies, embedded devices, simulation, trends, challenges, and relevant legislation. This review is based on studies conducted from 2009 to 2019, government reports about the sustainable deployment of these technologies and their adoption in the Brazilian automotive market. Moreover, WAVE (Wireless Access in Vehicular Environment) and DSRC (Dedicated Short-range Communication) standards, the performance analysis of communication parameters and intervehicular available at the market are also described. The current status of ITS (Intelligent Transportation System) development in Brazil was reviewed, as well as the research institutes and governmental actions focused on introducing the concept of connected vehicles into the society. The Brazilian outlook for technological adoption concerning CVs was also discussed. Moreover, challenges related to technical aspects, safety and environmental issues, and the standardization for vehicle communication are also described. Finally, this review highlights the challenges and proposals from available technologies devoted to the roads and vehicular infrastructure communication, their evolution and upcoming trends.
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Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.
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The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
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Subedi, Biraj, Sherif M. Gaweesh, Guangchuan Yang, and Mohamed M. Ahmed. "Connected Vehicle Training Framework and Lessons Learned to Improve Safety of Highway Patrol Troopers." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 25, 2020): 447–63. http://dx.doi.org/10.1177/0361198120957309.
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The Wyoming Highway Patrol (WHP) investigates more than 7,000 vehicle crashes yearly, often as first-hand responders. They often drive at high speeds through difficult road/weather conditions and under enormous secondary workloads, leading to an increased risk of crash. Connected vehicle (CV) technology can communicate timely road and traveler information messages (TIMs) to troopers, which could significantly reduce the frequency, severity, or both, of these crashes. The majority of the troopers, however, might not be familiar with driving a CV. This paper developed a “first responder-specific” training program on safe interaction with the technology and an in-depth assessment of how these new technologies are perceived by the troopers. The training program contains an E-training module and a hands-on driving simulator training module. The E-training presents concept of various CV warnings and notifications, including forward collision warning (FCW), spot weather warnings, work zone warnings, and other TIMs. Two scenarios were developed to familiarize troopers to simulated driving, two single-alert scenarios to help mastering the two most important warnings (FCW and variable speed limit), and two multiple-alert scenarios to train the troopers to drive in a comprehensive connected environment. A quiz section in the E-training module and comprehensive pre- and post-training questionnaire surveys were performed to evaluate the effectiveness of the developed CV training program. According to the trainees from the WHP, the driving simulator provided impressively realistic real-life-like scenarios for the troopers to practice the CV warnings they learned during the E-training.
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Alzoubaidi, Mutasem, and Milan Zlatkovic. "Conventional Diamond, Diverging Diamond, and Single Point Diamond Interchanges: A Comparative Operational Performance Evaluation in the Era of Connected Vehicles." Put i saobraćaj 68, no. 3 (October 3, 2022): 1–9. http://dx.doi.org/10.31075/pis.68.03.01.
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This study evaluates the mobility impact of implementing Connected Vehicle (CV) technology at the Conventional Diamond Interchange (CDI), Diverging Diamond Interchange (DDI), and Single Point Diamond Interchange (SPDI). The evaluation is based on a microsimulation environment created in VISSIM combined with ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communication algorithms. The effect of varying CV-Market Penetration Rates (CV-MPRs) on traffic operations is taken into consideration. The study shows that the interchange design has a higher impact on traffic operations than does the CV-MPR. Particularly, a 100% CV-MPR has led to 6.4% reductions in delays compared to the 0% CV-MPR, without considering the effect of interchange design. Contrarily, the CDI would increase delays as high as 24.0% as opposed to the SPDI, without considering the effect of CVs. Similarly, the DDI would reduce delays by up to 60.6% compared to the SPDI, without considering the effect of CVs.
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Bahaaldin, Karzan, Ryan Fries, Parth Bhavsar, and Plaban Das. "A Case Study on the Impacts of Connected Vehicle Technology on No-Notice Evacuation Clearance Time." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6357415.
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No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. Connected vehicle (CV) technology, with real-time vehicle to vehicle and vehicle to infrastructure communications, can help emergency managers to develop efficient and cost-effective traffic management plans for such events. The objectives of this research were to evaluate the impacts of CVs on no-notice evacuations using a case study of a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated, and validated for studying the performance of traffic during the evacuation. The researchers evaluated system performance with different CV penetration rates (from 0 to 30 percent CVs) and measured average speed, average delays, and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent, albeit increases in delays during the beginning of the evacuation. Additionally, the benefits could be greater for evacuations that last longer and with higher proportions of CVs in the vehicle stream.
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Yang, Xianfeng (Terry), Gang-Len Chang, Zhao Zhang, and Pengfei (Taylor) Li. "Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 27, 2019): 61–71. http://dx.doi.org/10.1177/0361198119837242.
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The intent of this paper is to develop a system that can integrate connected vehicle (CV) data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collision, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials. The embedded safety function will integrate CV and roadside sensor data to compute the distribution of dilemma zones for vehicles of different approaching speeds in real-time. Such data fusion will enable the proposed system to offer the advice of either “stop” or “go” to both CVs and HVs so as to prevent rear-end collisions and side-angled crashes. Given the locations and speeds of CVs, and the number of vehicles monitored by sensors, the proposed system can further compute the time-varying intersection queue length. Then the embedded mobility function will optimize the arterial signal plan in real-time and produce the speed advisory for approaching vehicles to facilitate their progression through intersections. Results from extensive simulation experiments confirm the effectiveness of the proposed system in both reducing potential intersection crash rates and improving arterial progression efficiency. The proposed control framework also proves the effectiveness of using dilemma zone protection sensors for traffic mobility improvement.
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Mousa, Saleh R., Peter R. Bakhit, Osama A. Osman, and Sherif Ishak. "A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42 (June 11, 2018): 268–79. http://dx.doi.org/10.1177/0361198118780204.
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Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving classification problems due to its accuracy, scalability, and speed. This study implements the XGB in predicting the onset of lane changing maneuvers using CV trajectory data. The performance of XGB is compared to three other tree-based algorithms namely, decision trees, gradient boosting, and random forests. The Next Generation SIMulation trajectory data are used to represent the high-resolution CV data. The results indicate that XGB is superior to the other algorithms with a high accuracy value of 99.7%. This outstanding accuracy is achieved when considering vehicle trajectory data two seconds prior to a potential lane change maneuver. The findings of this study are promising for detection of lane change maneuvers in CV environments.
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Shin, Hyeon-Shic, Michael Callow, Seyedehsan Dadvar, Young-Jae Lee, and Z. Andrew Farkas. "User Acceptance and Willingness to Pay for Connected Vehicle Technologies: Adaptive Choice-Based Conjoint Analysis." Transportation Research Record: Journal of the Transportation Research Board 2531, no. 1 (January 2015): 54–62. http://dx.doi.org/10.3141/2531-07.
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The preferences of drivers and their willingness to pay (WTP) for connected vehicle (CV) technologies were estimated with the use of adaptive choice-based conjoint (ACBC) analysis, the newest such method available. More than 500 usable surveys were collected through an online survey. Respondents were asked to choose from variously priced CV technology bundles (e.g., collision prevention, roadway information system). The study found that the acceptance level of the CV technologies was high, given that an absolute majority of survey respondents had the highest preferences for the most comprehensive technology bundle in each attribute. However, a comparison of the average importance of each attribute, including bundle prices, implied that price would be an important constraint and would influence CV deployment rates. At the attribute level, collision prevention technology received the highest importance score (i.e., the safety benefits most appealed to drivers). The ACBC analysis seemed to mimic well the trade-offs that people would consider in their actual purchasing decisions. The difference between WTP and self-explicated prices obtained before preferences were estimated was statistically significant (i.e., participants chose bundles after they considered product attributes and prices). This finding also affirmed that the ACBC analysis was a more appropriate method than the direct questioning methods used in past studies. Finally, certain socioeconomic characteristics were positively related to WTP. Those respondents that were knowledgeable about CV technologies and showed more innovativeness had higher WTP as well.
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Mutasem, Alzoubaidi, and Milan Zlatkovic. "Safety Performance Evaluation of Continuous Flow Intersections in the Era of Connected Vehicles: A Microsimulation Modelling Approach." Put i saobraćaj 68, no. 4 (December 17, 2022): 1–10. http://dx.doi.org/10.31075/pis.68.04.01.
Full textAbstract:
This study employed Federal Highway Administration’s Surrogate Safety Assessment Model (SSAM) to investigate the safety of implementing Connected Vehicles (CVs) at the Continuous Flow Intersection (CFI), by reproducing a real-world corridor, that has multiple successive implementations of CFIs, in VISSIM. Econolite’s ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communications were embedded in VISSIM. Additionally, the effect of CV-Market Penetration Rate (CV-MPR) on safety is taken into consideration. The study shows that CV deployments at partial and full CFIs leads to notable reductions in crash likelihoods and severities. The total number of conflicts, rear-end and lane change conflicts dropped by 23.8%, 23.6% and 24.4%, respectively at full CFIs and 100% MPR, whereas those were reduced by 6.4%, 4.8% and 17.9%, respectively at partial CFIs and 100% MPR. It was also found that at least a 50% MPR of CVs is required for safety improvements to be influential.
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Wang, Jiangfeng, Chao Wang, Jiarun Lv, Zhiqi Zhang, and Cuicui Li. "Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2415312.
Full textAbstract:
Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s.
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Aljamal, Mohammad A., Hossam M. Abdelghaffar, and Hesham A. Rakha. "Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches." Sensors 20, no. 15 (July 22, 2020): 4066. http://dx.doi.org/10.3390/s20154066.
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The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.
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Li, Ning, Shukai Chen, Jianjun Zhu, and Daniel Jian Sun. "A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology." Computational Intelligence and Neuroscience 2020 (June 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/2764576.
Full textAbstract:
One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.
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Luo, Xia, Bo Liu, Peter J. Jin, Yang Cao, and Wansgu Hu. "Arterial Traffic Flow Estimation Based on Vehicle-to-Cloud Vehicle Trajectory Data Considering Multi-Intersection Interaction and Coordination." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (April 29, 2019): 68–83. http://dx.doi.org/10.1177/0361198119842826.
Full textAbstract:
Conventional detection methods for intersection traffic flow heavily rely on fixed-location inductive loop, video image processing, infared, and microwave radar detectors. The emerging connected vehicles (CV) technologies can potentially reduce such dependencies on conventional vehicle detectors with the vehicle-to-cloud (V2C) CV data. This paper proposes an analytical method for traffic flow estimation in urban arterial corridors based on CV trajectories collected through V2C communication. Different from the existing single-intersection models, the proposed model considers traffic states and the traffic signal coordination among adjacent intersections, therefore, can capture the delay and queuing dynamics in arterial corridors. The queue spillback phenomenon is explicitly considered by applying the shockwave theory. The proposed model is evaluated based on real-world vehicle trajectory data from the DiDi platform collected on an arterial network in Chengdu, China with a penetration rate of less than 10% of the overall traffic. The flow estimation results are compared with traffic counts collected from video detectors. The model parameters are calibrated with more than 300,000 GPS points during a typical workday and tested on a different workday. The evaluation results show a mean absolute percentage error within the range of 4–7% among all intersections, outperforming the results generated by the existing single-intersection model. The results indicate the promising potential of using the proposed methods to evaluate intersection performance without heavy investment in on-site detectors.