Assessment of freeway traffic parameters leading to lane-change related collisions.

This study aims at 'predicting' the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2]  anurag pande,et al.  Estimation Of Hybrid Models For Real-time Crash Risk Assessment On Freeways , 2005 .

[3]  Jonathan Baxter,et al.  Learning internal representations , 1995, COLT '95.

[4]  Tao Xiong,et al.  A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

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

[7]  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).

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

[9]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

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

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

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

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

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

[15]  M. Abdel-Aty,et al.  Potential Real-Time Indicators of Sideswipe Crashes on Freeways , 2006 .

[16]  Ronald R Knipling,et al.  LANE CHANGE/MERGE CRASHES: PROBLEM SIZE ASSESSMENT AND STATISTICAL DESCRIPTION. TECHNICAL REPORT. FINAL REPORT , 1994 .

[17]  I. Burgert,et al.  A comprehensive analysis of the relation of cellulose microfibril orientation and lignin content in the S2 layer of different tissue types of spruce wood (Picea abies (L.) Karst.) , 2008 .

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

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

[20]  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.

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

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

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

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