Title

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

Authors

Authors

A. Pande;M. Abdel-Aty

Comments

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Abbreviated Journal Title

Accid. Anal. Prev.

Keywords

lane-change crashes; freeway crashes; sideswipe collisions; loop; detector data; data mining; real-time traffic; CRASH; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

Abstract

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. (c) 2006 Elsevier Ltd. All rights reserved.

Journal Title

Accident Analysis and Prevention

Volume

38

Issue/Number

5

Publication Date

1-1-2006

Document Type

Article

Language

English

First Page

936

Last Page

948

WOS Identifier

WOS:000239528200013

ISSN

0001-4575

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