@article{ref1,
title="Congestion by accident? A two-way relationship for highways in England",
journal="Journal of transport geography",
year="2019",
author="Pasidis, Ilias",
volume="76",
number="",
pages="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.<p /> <p>Language: en</p>",
language="en",
issn="0966-6923",
doi="10.1016/j.jtrangeo.2017.10.006",
url="http://dx.doi.org/10.1016/j.jtrangeo.2017.10.006"
}