A Novel Surrogate Safety Indicator Based on Constant Initial Acceleration and Reaction Time Assumption

The development of surrogate safety measures has drawn significant research interest in the field of traffic safety analysis. Innovative data sources such as video-based traffic surveillance systems have made it possible to collect large amounts of microscopic traffic data. By deriving traffic safety indicators such as the Deceleration Rate to Avoid a Crash (DRAC) statements concerning traffic safety over a determined road section can be made. This work presents the derivation of a novel surrogate safety indicator based on a Constant Initial Acceleration and reaction time assumption which considers the interaction between vehicles and describes the traffic safety of a road section. The evaluation is based on a video-based microscopic traffic data collection. To examine the efficiency, the new developed indicator is compared to the original Deceleration Rate to Avoid a Crash (DRAC) and the modified indicator (MDRAC) which includes the reaction time. The results showed that the new indicator is more sensitive in detecting critical situations than the other indicators and in addition describes the conflict situations more realistically.

[1]  Jeffery Archer,et al.  Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling : a study of urban and suburban intersections , 2005 .

[2]  A. Horst A time-based analysis of road user behaviour in normal and critical encounters , 1990 .

[3]  Xiaobo Qu,et al.  A Review of Crash Surrogate Events , 2014 .

[4]  Chen Wang,et al.  Surrogate Safety Measure for Simulation-Based Conflict Study , 2013 .

[5]  Yan Kuang,et al.  A tree-structured crash surrogate measure for freeways. , 2015, Accident; analysis and prevention.

[6]  Xiaobo Qu,et al.  How Does the Driver’s Perception Reaction Time Affect the Performances of Crash Surrogate Measures? , 2015, PloS one.

[7]  T F Larwin SAN DIEGO'S LIGHT RAIL SYSTEM: A SUCCESS STORY. 1988 TRANSPORTATION ACHIEVEMENT AWARD (OPERATIONS) , 1989 .

[8]  Kay Fitzpatrick,et al.  New Stopping Sight Distance Model for Use in Highway Geometric Design , 1998 .

[9]  Amir Reza Mamdoohi,et al.  Comparative Analysis of Safety Performance Indicators Based on Inductive Loop Detector Data , 2014 .

[10]  Giuseppe Guido,et al.  A new microsimulation model for the evaluation of traffic safety performances , 2012 .

[11]  Marc Green,et al.  "How Long Does It Take to Stop?" Methodological Analysis of Driver Perception-Brake Times , 2000 .

[12]  Yizhen Zhang,et al.  A new threat assessment measure for collision avoidance systems , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[13]  Larry Head,et al.  Surrogate Safety Measures from Traffic Simulation Models , 2003 .

[14]  Giuseppe Guido,et al.  Safety performance measures: a comparison between microsimulation and observational data , 2011 .

[15]  Nikiforos Stamatiadis,et al.  Evaluation of a simulation-based surrogate safety metric. , 2014, Accident; analysis and prevention.

[16]  Chen Wang,et al.  Derivation of a New Surrogate Measure of Crash Severity , 2014 .

[17]  Keping Li,et al.  A stochastic computational model for yellow time determination and its application , 2015 .

[18]  Hesham Rakha,et al.  Characterizing Driver Behavior on Signalized Intersection Approaches at the Onset of a Yellow-Phase Trigger , 2007, IEEE Transactions on Intelligent Transportation Systems.

[19]  B E Peterson Proceedings: first Workshop on Traffic Conflicts, Oslo, 1977 , 1977 .

[20]  B. Greenshields Reaction time in automobile driving. , 1936 .

[21]  Christer Hydén,et al.  USE OF SPEED LIMITERS IN CARS FOR INCREASED SAFETY AND A BETTER ENVIRONMENT , 1991 .

[22]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Graham Currie,et al.  How Many Simulation Runs are Required to Achieve Statistically Confident Results: A Case Study of Simulation-Based Surrogate Safety Measures , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[24]  S. M. Sohel Mahmud,et al.  Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs , 2017 .

[25]  Giuseppe Guido,et al.  Comparing Safety Performance Measures Obtained from Video Capture Data , 2011 .

[26]  T. Okada,et al.  Factors with the Greatest Influence on Drivers' Judgment of When to Apply Brakes , 2006, 2006 SICE-ICASE International Joint Conference.

[27]  Qiang Meng,et al.  Evaluation of rear-end crash risk at work zone using work zone traffic data. , 2011, Accident; analysis and prevention.

[28]  Transportation Officials,et al.  A policy on geometric design of highways and streets, 1984 , 1984 .

[29]  G T Taoka BREAK REACTION TIMES OF UNALERTED DRIVERS , 1989 .

[30]  Heikki Summala,et al.  Brake reaction times and driver behavior analysis , 2000 .