Safety benefits of arterials’ crash risk under connected and automated vehicles

Abstract 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 microsimulation (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.

[1]  Mitra Pourabdollah,et al.  Calibration and evaluation of car following models using real-world driving data , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[2]  Mohamed Abdel-Aty,et al.  Safety Analysis of Access Zone Design for Managed Toll Lanes on Freeways , 2018 .

[3]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[4]  Alireza Talebpour,et al.  Influence of connected and autonomous vehicles on traffic flow stability and throughput , 2016 .

[5]  Michael Frankfurter,et al.  Numerical Recipes In C The Art Of Scientific Computing , 2016 .

[6]  Ralf Blossey,et al.  A Dynamical Model of Oocyte Maturation Unveils Precisely Orchestrated Meiotic Decisions , 2012, PLoS Comput. Biol..

[7]  Seri Park,et al.  A method for identifying rear-end collision risks using inductive loop detectors. , 2006, Accident; analysis and prevention.

[8]  Ye Li,et al.  Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways. , 2017, Accident; analysis and prevention.

[9]  Dirk Helbing,et al.  GENERALIZED FORCE MODEL OF TRAFFIC DYNAMICS , 1998 .

[10]  W. Wu,et al.  Modelling and Simulation of Vehicle Speed Guidance in Connected Vehicle Environment , 2015 .

[11]  Ardalan Vahidi,et al.  Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic , 2016 .

[12]  Wei Wang,et al.  Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways. , 2017, Accident; analysis and prevention.

[13]  Research on the Impacts of Connected and Autonomous Vehicles ( CAVs ) on Traffic Flow Stage 1 : Evidence Review , .

[14]  Fabien Moutarde,et al.  Priority-based coordination of autonomous and legacy vehicles at intersection , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[15]  Mohamed Abdel-Aty,et al.  Longitudinal safety evaluation of connected vehicles' platooning on expressways. , 2017, Accident; analysis and prevention.

[16]  Wei Wang,et al.  Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system , 2016, Traffic injury prevention.

[17]  Urbano Nunes,et al.  Platooning of autonomous vehicles with intervehicle communications in SUMO traffic simulator , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[18]  Guoyuan Wu,et al.  Improving traffic operations using real-time optimal lane selection with connected vehicle technology , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[19]  Zhixia Li,et al.  Modeling Reservation-Based Autonomous Intersection Control in VISSIM , 2013 .

[20]  M M Minderhoud,et al.  Extended time-to-collision measures for road traffic safety assessment. , 2001, Accident; analysis and prevention.

[21]  Tarek Sayed,et al.  Can Time Proximity Measures be Used as Safety Indicators in All Driving Cultures? , 2015 .

[22]  Omar Bagdadi,et al.  Jerky driving--An indicator of accident proneness? , 2011, Accident; analysis and prevention.

[23]  Wei Wang,et al.  Development of a variable speed limit strategy to reduce secondary collision risks during inclement weathers. , 2014, Accident; analysis and prevention.

[24]  Mohamed Abdel-Aty,et al.  Temporal and spatial analyses of rear-end crashes at signalized intersections. , 2006, Accident; analysis and prevention.

[25]  Ling Wang,et al.  Assessment of the safety benefits of vehicles' advanced driver assistance, connectivity and low level automation systems. , 2018, Accident; analysis and prevention.

[26]  Lily Elefteriadou,et al.  Efficient control of fully automated connected vehicles at freeway merge segments , 2017 .

[27]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[28]  S. Ilgin Guler,et al.  Using connected vehicle technology to improve the efficiency of intersections , 2014 .

[29]  Alireza Talebpour,et al.  Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability , 2017 .

[30]  Li-Dong Zhang,et al.  An original traffic flow model with signal effect for energy dissipation , 2014 .

[31]  Michel Basset,et al.  Safety assessment of mixed traffic based on accident scenario , 2012 .

[32]  Santokh Singh,et al.  Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey , 2015 .

[33]  Mohamed Abdel-Aty,et al.  Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[34]  Yanyong Guo,et al.  Exploring Evasive Action–Based Indicators for PTW Conflicts in Shared Traffic Facility Environments , 2018, Journal of Transportation Engineering, Part A: Systems.

[35]  Steven E Shladover,et al.  Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data , 2014 .

[36]  Jaeyoung Lee,et al.  Safety Impact of Weaving Distance on Freeway Facilities with Managed Lanes using Both Microscopic Traffic and Driving Simulations , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[37]  Ali Hajbabaie,et al.  Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment , 2018, Transportation Research Part C: Emerging Technologies.

[38]  Mohamed Abdel-Aty,et al.  Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures , 2017 .

[39]  Tarek Sayed,et al.  Developing evasive action‐based indicators for identifying pedestrian conflicts in less organized traffic environments , 2016 .

[40]  Wei Wang,et al.  Longitudinal safety evaluation of electric vehicles with the partial wireless charging lane on freeways. , 2018, Accident; analysis and prevention.

[41]  Long T. Truong,et al.  Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures , 2018 .

[42]  John C Hayward,et al.  NEAR-MISS DETERMINATION THROUGH USE OF A SCALE OF DANGER , 1972 .

[43]  Byungkyu Brian Park,et al.  Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment , 2012, IEEE Transactions on Intelligent Transportation Systems.

[44]  Dirk Helbing,et al.  Influence of Reaction Times and Anticipation on Stability of Vehicular Traffic Flow , 2007 .

[45]  Laurence R. Rilett,et al.  Calibration of Microsimulation Models Using Nonparametric Statistical Techniques , 2005 .

[46]  Al-Ahad Ekram,et al.  Effects of Connected and Autonomous Vehicles on Contraflow Operations for Emergency Evacuation: a Microsimulation Study , 2018 .

[47]  Salima Hassas,et al.  How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies , 2016 .

[48]  Saiedeh Razavi,et al.  Impact of Connected Vehicle on Work Zone Network Safety through Dynamic Route Guidance , 2016 .

[49]  Tarek Sayed,et al.  Surrogate Safety Assessment Model and Validation: Final Report , 2008 .

[50]  Hani S. Mahmassani,et al.  Influence of Autonomous and Connected Vehicles on Stability of Traffic Flow , 2015 .

[51]  Mohamed Abdel-Aty,et al.  Crash Estimation at Signalized Intersections Along Corridors: Analyzing Spatial Effect and Identifying Significant Factors , 2006 .

[52]  Kara M. Kockelman,et al.  Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report , 2016 .

[53]  Mohamed Abdel-Aty,et al.  Approach-level real-time crash risk analysis for signalized intersections. , 2018, Accident; analysis and prevention.

[54]  Zhu Wen-xing,et al.  A new car-following model for autonomous vehicles flow with mean expected velocity field , 2018 .

[55]  Dirk Helbing,et al.  Adaptive cruise control design for active congestion avoidance , 2008 .

[56]  Katsuhiro Nishinari,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2014 .

[57]  Ali Hajbabaie,et al.  Collision Mitigation at Signalized Intersection Using Connected Vehicles Data and Technologies , 2018 .

[58]  Jaeyoung Lee,et al.  Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials , 2018, Transportation Research Part C: Emerging Technologies.

[59]  Mohamed Abdel-Aty,et al.  Understanding the Safety Benefits of Connected and Automated Vehicles on Arterials’ Intersections and Segments , 2019 .

[60]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[61]  Wei Wang,et al.  Using VISSIM simulation model and Surrogate Safety Assessment Model for estimating field measured traffic conflicts at freeway merge areas , 2013 .

[62]  Mohamed Abdel-Aty,et al.  Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. , 2005, Accident; analysis and prevention.

[63]  Tarek Sayed,et al.  Use of Drivers’ Jerk Profiles in Computer Vision–Based Traffic Safety Evaluations , 2014 .

[64]  Guoyuan Wu,et al.  Platoon-based multi-agent intersection management for connected vehicle , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[65]  Hans Steiner,et al.  State and Trait Emotions in Delinquent Adolescents , 2007, Child psychiatry and human development.