Title

A Novel Visible Network Approach For Freeway Crash Analysis

Keywords

Crash data; Freeway safety; Safety analysis; Visible network

Abstract

Freeway crashes have attracted considerable attention in recent years leading to the development of various methodologies to unveil the crash occurrence mechanisms including two general modeling approaches: parametric and non-parametric. In this paper, a novel visible network approach has been proposed to analyze crash characteristics with real-time traffic and weather data. In the suggested model, traffic states prior to crash occurrence have been extracted from real-time data; and crashes are mapped as nodes on the network. Each node contains information for the most hazardous factors relate to crash occurrence selected by random forest algorithm. With the help of transferring technology, links are connected between the nodes according to the state values. Therefore, complete freeway crash evolution networks can be obtained by analyzing one year crash data (including real-time weather and traffic variables) on I-70 in the state of Colorado. Additionally, the suggested method is also used to analyze single- and multi-vehicle crashes separately to identify their distinct characteristics. Compared with the traditional analysis methods, the proposed visible approach has the advantages of easy to be extended, transferred, and applied; easy to identify the effects of the various contributing factors on a traffic crash and to visually inspect the model. Moreover, the crash contributing factors identified in this study is beneficial for designing advanced early-warning and risk assessment systems in the context of real-time highway management. © 2013 Elsevier Ltd.

Publication Date

1-1-2013

Publication Title

Transportation Research Part C: Emerging Technologies

Volume

36

Number of Pages

72-82

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.trc.2013.08.005

Socpus ID

84884267363 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84884267363

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