Linking Crash Patterns to ITS-Related Archived Data: Phase IIVolume I: Real-time Crash Risk Assessment Models

Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. This report provides details of the research effort for developing a proactive traffic management system for a 36.25-mile instrumented corridor of Interstate-4 in Orlando metropolitan area. Complete research effort is documented in two separate volumes. This volume (Volume I) documents the work to develop real-time crash risk assessment models. Among available historical crashes used in the analysis; the rear-end crashes are analyzed first. They are the single most frequent type of crash on the freeway. It was found that rear-end crashes may be separated into two groups based on the speeds prevailing at surrounding stations 5-10 minutes before the crash. The authors have developed separate models for the rear-end crashes that occur during congested speed regime and those which occur under moderate to high speed regime. A classification tree model for regime identification is followed up with neural network models for each of the two groups of rear-end crashes. It was found that the proposed approach for real-time identification of crash prone conditions can ‘predict’ 3/4 of the rear-end crashes with reasonable false alarm rate. The analysis is then followed up by artificial intelligence based neural network models for lane-change related crashes. However, the authors did not find any groupings in these crashes analogous to the rear-end crashes belonging to two distinct regimes. These models for rear-end and lane-change related crashes are then put together in the form of a framework for real-time crash risk assessment.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Vikash V. Gayah.  Examining Route Diversion And Multiple Ramp Metering Strategies For Reducing Real-time Crash Risk On Urban Freeways , 2006 .

[3]  L Mussone,et al.  An analysis of urban collisions using an artificial intelligence model. , 1999, Accident; analysis and prevention.

[4]  S Madanat,et al.  A PROTOTYPE SYSTEM FOR REAL-TIME INCIDENT LIKELIHOOD PREDICTION , 1995 .

[5]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[6]  Jeremy Harvey Dilmore,et al.  Implementation Strategies For Real-time Traffic Safety Improvements On Urban Freeways , 2005 .

[7]  Chester G. Wilmot,et al.  EFFECT OF SPEED LIMITS ON SPEED AND SAFETY: A REVIEW , 1999 .

[8]  Kara M. Kockelman,et al.  Freeway Speeds and Speed Variations Preceding Crashes, Within and Across Lanes , 2010 .

[9]  T. Golob,et al.  A Method for Relating Type of Crash to Traffic Flow Characteristics on Urban Freeways , 2002 .

[10]  N. Garber,et al.  Effect of Speed, Flow, and Geometric Characteristics on Crash Frequency for Two-Lane Highways , 2000 .

[11]  Moshe Ben-Akiva,et al.  Evaluation of ramp control algorithms using microscopic traffic simulation , 2002 .

[12]  Markos Papageorgiou,et al.  ALINEA: A LOCAL FEEDBACK CONTROL LAW FOR ON-RAMP METERING , 1990 .

[13]  T. Golob,et al.  Relationships Among Urban Freeway Accidents, Traffic Flow, Weather and Lighting Conditions , 2001 .

[14]  Baher Abdulhai,et al.  Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network , 1999 .

[15]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[16]  Gang-Len Chang,et al.  AN EMPIRICAL INVESTIGATION OF MACROSCOPIC LANE-CHANGING CHARACTERISTICS ON UNCONGESTED MULTILANE FREEWAYS , 1991 .

[17]  Fedel Frank Saccomanno,et al.  Assessing Safety Benefits of Variable Speed Limits , 2004 .

[18]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[19]  Sherif Ishak,et al.  Impact of Freeway Geometric and Incident Characteristics on Incident Detection , 1996 .

[20]  Timothy Masters,et al.  Advanced algorithms for neural networks: a C++ sourcebook , 1995 .

[21]  R. Jayakrishnan,et al.  Emergence of Private Advanced Traveler Information System Providers and Their Effect on Traffic Network Performance , 2002 .

[22]  N Stamatiadis,et al.  Quasi-induced exposure: methodology and insight. , 1997, Accident; analysis and prevention.

[23]  K. M. Tao,et al.  A closer look at the radial basis function (RBF) networks , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[24]  P. Rämä.  Effects of Weather-Controlled Variable Speed Limits and Warning Signs on Driver Behavior , 1999 .

[25]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[26]  Yorgos J. Stephanedes,et al.  Implementation of on-line zone control strategies for optimal ramp metering in the Minneapolis Ring Road , 1994 .

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

[28]  Chris Lee,et al.  Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic , 2002 .

[29]  Haitham Al-Deek,et al.  New Algorithms for Filtering and Imputation of Real-Time and Archived Dual-Loop Detector Data in I-4 Data Warehouse , 2004 .

[30]  Mohamed Abdel-Aty,et al.  Identifying crash propensity using specific traffic speed conditions. , 2005, Journal of safety research.

[31]  Albinder Dhindsa.  Evaluating Ramp Metering And Variable Speed Limits To Reduce Crash Potential On Congested Freeways Using Micro-simulation , 2005 .

[32]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[33]  Jonathan P Masinick,et al.  An analysis on the impact of rubbernecking on urban freeway traffic. , 2004 .

[34]  M. Abdel-Aty,et al.  Linking Roadway Geometrics and Real-Time Traffic Characteristics to Model Daytime Freeway Crashes: Generalized Estimating Equations for Correlated Data , 2004 .

[35]  Donald F. Specht,et al.  Probabilistic neural networks and general regression neural networks , 1996 .

[36]  Stephen J. Roberts,et al.  Supervised and unsupervised learning in radial basis function classifiers , 1994 .

[37]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[38]  Bruce N. Janson,et al.  Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques , 1998 .

[39]  Will Recker,et al.  Using Microscopic Simulation to Evaluate Potential Intelligent Transportation System Strategies Under Nonrecurrent Congestion , 2004 .

[40]  S Y Sohn,et al.  Pattern recognition for road traffic accident severity in Korea , 2001, Ergonomics.

[41]  A Vorko,et al.  Multiple attribute entropy classification of school-age injuries. , 2000, Accident; analysis and prevention.

[42]  Ryuichi Kitamura,et al.  Impact Of ATIS On Drivers' Decisions And Route Choice: A Literature Review , 1993 .

[43]  Okyay Kaynak,et al.  An algorithm for fast convergence in training neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[44]  J. D. Powell,et al.  Radial basis function approximations to polynomials , 1989 .

[45]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[46]  Thomas F. Golob,et al.  A Tool to Evaluate the Safety Effects of Changes in Freeway Traffic Flow , 2002 .

[47]  Sherif Ishak,et al.  Performance of Automatic ANN-Based Incident Detection on Freeways , 1999 .

[48]  Wilfred W Recker,et al.  Freeway safety as a function of traffic flow. , 2002, Accident; analysis and prevention.

[49]  Harilaos N. Koutsopoulos,et al.  A microscopic traffic simulator for evaluation of dynamic traffic management systems , 1996 .

[50]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

[51]  C. Lee,et al.  2003. Real-time crash prediction model for the application to crash prevention in freeway traffic , 2003 .

[52]  V. Sisiopiku,et al.  Relationship Between Volume-to-Capacity Ratios and Accident Rates , 1997 .

[53]  Anurag Pande,et al.  Classification of real-time traffic speed patterns to predict crashes on freeways , 2003 .

[54]  Michael Georgiopoulos,et al.  Applications of Neural Networks in Electromagnetics , 2001 .

[55]  Mohamed Abdel-Aty,et al.  Comprehensive Analysis of the Relationship between Real-Time Traffic Surveillance Data and Rear-End Crashes on Freeways , 2006 .

[56]  D. Collett,et al.  Modelling Binary Data. , 1994 .

[57]  Hani S. Mahmassani,et al.  System performance and user response under real-time information in a congested traffic corridor , 1991 .

[58]  Markos Papageorgiou,et al.  SERIES OF NEW LOCAL RAMP METERING STRATEGIES , 2003 .

[59]  Chris Lee,et al.  Analysis of Crash Precursors on Instrumented Freeways , 2002 .

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

[61]  Robert L. Bertini,et al.  D R A F T Application of PARAMICS Simulation At a Diamond Interchange , 2002 .

[62]  Lester A Hoel,et al.  Using Simulation to Predict Safety and Operational Impacts of Increasing Traffic Signal Density , 2002 .

[63]  Mohamed Abdel-Aty,et al.  Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections , 2001 .

[64]  Mohamed Abdel-Aty,et al.  Evaluation of variable speed limits for real-time freeway safety improvement. , 2006, Accident; analysis and prevention.

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