Integration of Microscopic Big Traffic Data in Simulation-Based Safety Analysis

The main objectives of this study were to explore different vehicle detection systems, focusing on the most widely deployed infrastructure-based sensing technologies. This project used traffic data from two point-based over-roadway detection systems, namely the Microwave Vehicle Detection System (MVDS) and the Video Image Processing (VIP) system, and data from a segment-based probe-vehicle system, the Automatic Vehicle Identification (AVI) system, to conduct operation and safety evaluations. Applications of these types of data for efficiency and safety evaluation, and simulation, were investigated. To achieve the proposed objectives, several tasks were carried out. Task 1: Identify and collect continuous measurements of traffic conditions at different levels: segment-based, point-based, and vehicle based; Task 2: Validate the use of different microscopic data sources; Task 3: Measure traffic efficiency: congestion measurement and travel time reliability in real-time; Task 4: Evaluate traffic safety: relationship between traffic flow (speed, flow, density, congestion) and crash occurrence, crash precursors in real-time; Task 5: Simulate traffic flow under adverse weather conditions in microsimulation. Real-time traffic data (combined as needed with weather data) could be used to tune traffic flow under different conditions in micro-simulation; Task 6: Investigate the use of video-based data; Task 7: Develop a simulation model using video-based parameters; Task 8: Use the new simulation model to investigate dilemma zone decisions at signalized intersections.

[1]  Stephen Graham Ritchie,et al.  TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES , 1993 .

[2]  F. Hall.  TRAFFIC STREAM CHARACTERISTICS , 1997 .

[3]  David Mahalel,et al.  The Flashing Green Light Paradox , 2012 .

[4]  Asma Munir Khan.  Intelligent infrastructure-based queue-end warning system for avoiding rear impacts , 2007 .

[5]  Qi Shi,et al.  Feasibility of Incorporating Reliability Analysis in Traffic Safety Investigation , 2013 .

[6]  Ulrich Weidmann,et al.  Assessing the feasibility of transport Megaprojects; ; Transportation research record : journal of the Transportation Research Board; , 2007 .

[7]  Mohamed Abdel-Aty,et al.  Split Models for Predicting Multivehicle Crashes during High-Speed and Low-Speed Operating Conditions on Freeways , 2005 .

[8]  Soyoung Ahn,et al.  Impact of traffic oscillations on freeway crash occurrences. , 2010, Accident; analysis and prevention.

[9]  Mohamed Abdel-Aty,et al.  Exploring the Feasibility of Using Airport Data in Real-Time Risk Assessment , 2013 .

[10]  Mohamed Abdel-Aty,et al.  Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes. , 2013, Accident; analysis and prevention.

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

[12]  Elsevier Sdol.  Transportation Research Part F: Traffic Psychology and Behaviour , 2009 .

[13]  David L Schrank.  THE KEYS TO ESTIMATING MOBILITY IN URBAN AREAS , 2001 .

[14]  Mohamed Abdel-Aty,et al.  Split Models for Predicting Multivehicle Crashes during High-Speed and Low-Speed Operating Conditions on Freeways , 2005 .

[15]  Mohamed Abdel-Aty,et al.  Estimation of Real-Time Crash Risk , 2011 .

[16]  John Hourdos,et al.  Real-Time Detection of Crash-Prone Conditions at Freeway High Crash Locations , 2006 .

[17]  Judson S Matthias,et al.  EVALUATION OF DRIVER BEHAVIOR AT SIGNALIZED INTERSECTIONS (DISCUSSION) , 1983 .

[18]  Luis F. Laracuente,et al.  Analysis of Dilemma Zone Driver Behavior at Signalized Intersections , 2007 .

[19]  Xuedong Yan,et al.  Classification analysis of driver's stop/go decision and red-light running violation. , 2010, Accident; analysis and prevention.

[20]  Timothy J Lomax,et al.  SELECTING TRAVEL RELIABILITY MEASURES , 2003 .

[21]  Mohamed M. Ahmed,et al.  Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data , 2012 .

[22]  Khaled Hamad,et al.  Developing a Measure of Traffic Congestion: Fuzzy Inference Approach , 2002 .

[23]  Moinul Hossain,et al.  A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. , 2012, Accident; analysis and prevention.

[24]  C Newton,et al.  Evaluation of an alternative traffic light change anticipation system. , 1997, Accident; analysis and prevention.

[25]  D. L. Gerlough,et al.  Traffic flow theory : a monograph , 1975 .

[26]  Mohamed M. Ahmed,et al.  The Viability of Using Automatic Vehicle Identification Data for Real-Time Crash Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[27]  Mohamed Abdel-Aty,et al.  Real-time prediction of visibility related crashes , 2012 .

[28]  Peter T. Martin,et al.  DETECTOR TECHNOLOGY EVALUATION , 2003 .

[29]  Lawrence A Klein,et al.  SUMMARY OF VEHICLE DETECTION AND SURVEILLANCE TECHNOLOGIES USED IN INTELLIGENT TRANSPORTATION SYSTEMS , 2000 .

[30]  Haris N. Koutsopoulos,et al.  A Synthesis of emerging data collection technologies and their impact on traffic management applications , 2011 .

[31]  Mohamed Abdel-Aty,et al.  Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression , 2004 .

[32]  Panagiotis Papaioannou,et al.  Driver behaviour, dilemma zone and safety effects at urban signalized intersections in Greece. , 2007, Accident; analysis and prevention.

[33]  Matthew Day,et al.  Congestion Management Process: A Guidebook , 2011 .

[34]  Mohamed Abdel-Aty,et al.  Assessment of freeway traffic parameters leading to lane-change related collisions. , 2006, Accident; analysis and prevention.

[35]  H W McGee,et al.  ENGINEERING COUNTERMEASURES TO REDUCE RED LIGHT RUNNING , 2002 .

[36]  H Köll,et al.  Driver behaviour during flashing green before amber: a comparative study. , 2004, Accident; analysis and prevention.

[37]  Myung-Soon Chang,et al.  TIMING TRAFFIC SIGNAL CHANGE INTERVALS BASED ON DRIVER BEHAVIOR , 1985 .

[38]  John D. Riley.  Evaluation of Travel Time Estimates Derived From Automatic Vehicle Identification Tags in San Antonio, TX , 1999 .

[39]  Rahul Joshi,et al.  Surrogate Safety Assessment Model (SSAM): Software User Manual , 2008 .

[40]  David S. Hurwitz,et al.  Application of driver behavior and comprehension to dilemma zone definition and evaluation , 2009 .

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