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Congestion by accident? A two-way relationship for highways in England

Ilias Pasidis

Journal of Transport Geography, 2019, vol. 76, issue C, 301-314

Abstract: This paper aims to estimate the causal effect of accidents on traffic congestion and vice versa. In order to identify both effects of this two-way relationship, I use dynamic panel data techniques and open access ‘big data’ of highway traffic and accidents in England for the period 2012–2014. The research design is based on the daily-and-hourly specific mean reversion pattern of highway traffic, which can be used to define a recurrent congestion benchmark. Using this benchmark, I am able to identify the causal effect of accidents on non-recurrent traffic congestion. A positive relationship between traffic congestion and road accidents would yield multiplicative benefits for policies that aim at reducing either of these issues. Additionally, I explore the duration of the effect of an accident on congestion, the ‘rubbernecking’ effect, as well as heterogeneous effects in the most congested highway segments. Then, I test the use of methods which employ the bulk of information in big data and other methods using a very reduced sample. In my application, both approaches produce similar results. Finally, I find a non-linear negative effect of traffic congestion on the probability of an accident.

Keywords: Accidents; Traffic congestion; Big data; Highways; England (search for similar items in EconPapers)
JEL-codes: R4 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.jtrangeo.2017.10.006

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