01657940

Component

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, the Hurst Exponent metric from Fractal Theory was used as a congestion indicator for crash-rate modeling. One month of traffic speed data at several monitor sites along the M4 motorway in Sydney, Australia was utilized and congestion patterns were assessed 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. The empirical results show that the latent class tobit model outperforms the random parameters model, in terms of goodness-of-fit. Furthermore, 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.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

18-00248

English

19p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Crash rates; Highway safety; Mathematical models; Traffic crashes

Latent class models

Sydney (Australia)

Highways; Planning and Forecasting; Safety and Human Factors

Transportation Research Board Annual Meeting 2018 Paper #18-00248

TRIS, TRB, ATRI

Jan 8 2018 10:05AM