Congestion by accident? A two-way relationship for highways in England. (April 2019)

• Record Type: Journal Article
• Authors: Pasidis, Ilias
• Is Part Of: Journal of transport geography. Volume 76(2019)
• Page Start: 301
• Page End: 314
• Publication Date: 2019-04
• Subjects: Accidents -- Traffic congestion -- Big data -- Highways -- England; R4; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2017.10.006
• Languages: English
• ISSNs: 0966-6923

Abstract

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.