Citation

Chand S, Dixit VV. Accid. Anal. Prev. 2018; 112: 30-38.

Affiliation

Research Centre for Integrated Transport Innovation (rCITI), School of Civil & Environmental Engineering, University of New South Wales, Sydney, NSW 2052 Australia; IAG Research Centre, IAG Research Labs, Sydney, NSW 2000 Australia. Electronic address: v.dixit@unsw.edu.au.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2017.12.023

PMID

29306686

Abstract

The repercussions from congestion and accidents on major highways can have significant negative impacts on the economy and environment. It is a primary objective of transport authorities to minimize the likelihood of these phenomena taking place, to improve safety and overall network performance. In this study, we use the Hurst Exponent metric from Fractal Theory, as a congestion indicator for crash-rate modeling. We analyze one month of traffic speed data at several monitor sites along the M4 motorway in Sydney, Australia and assess congestion patterns with the Hurst Exponent of speed (Hspeed). Random Parameters and Latent Class Tobit models were estimated, to examine the effect of congestion on historical crash rates, while accounting for unobserved heterogeneity. Using a latent class modeling approach, the motorway sections were probabilistically classified into two segments, based on the presence of entry and exit ramps. This will allow transportation agencies to implement appropriate safety/traffic countermeasures when addressing accident hotspots or inadequately managed sections of motorway.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Congestion; Crash rate; Hurst exponent; Latent class model; Random parameters; Tobit regression