Trend shifts in road traffic collisions

An application of Hidden Markov Models and Generalised Additive Models to assess the impact of the 20 mph speed limit policy in Edinburgh

Datos Bibliográficos

ID21246830
AutoresValentin Popov (0000-0003-1079-7992, University of St Andrews, autor de correspondencia), Glenna Nightingale (0000-0002-0110-3939, University of Edinburgh), Andrew James Williams (0000-0002-2175-8836, University of St Andrews), Paul Kelly (0000-0003-1946-9848, University of Edinburgh), Ruth Jepson (0000-0002-9446-445X, University of Edinburgh), Karen Milton (0000-0002-0506-2214, University of East Anglia), Michael P Kelly (0000-0002-2029-5841, University of Cambridge), Michael Kelly (0009-0009-2238-0546, University of Cambridge)
Año2021
Volumen48
Número9
Páginas2590-2606
Fecha de publicación2021-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Identificadores de la revistaISSN: 2399-8083 • E-ISSN: 2399-8091
EditorialSAGE Publications (PUBLISHER • US)
DOI10.1177/2399808320985524
OpenAlexW3118607057
IdiomaEN
Citas recibidas2
Referencias citadas28

Empirical study of road traffic collision (RTCs) rates is challenging at small geographies due to the relative rarity of collisions and the need to account for secular and seasonal trends. In this paper, we demonstrate the successful application of Hidden Markov Models (HMMs) and Generalised Additive Models (GAMs) to describe RTCs time series using monthly data from the city of Edinburgh (STATS19) as a case study. While both models have comparable level of complexity, they bring different advantages. HMMs provide a better interpretation of the data-generating process, whereas GAMs can be superior in terms of forecasting. In our study, both models successfully capture the declining trend and the seasonal pattern with a peak in the autumn and a dip in the spring months. Our best fitting HMM indicates a change in a fast-declining-trend state after the introduction of the 20 mph speed limit in July 2016. Our preferred GAM explicitly models this intervention and provides evidence for a significant further decline in the RTCs. In a comparison between the two modelling approaches, the GAM outperforms the HMM in out-of-sample forecasting of the RTCs for 2018. The application of HMMs and GAMs to routinely collected data such as the road traffic data may be beneficial to evaluations of interventions and policies, especially natural experiments, that seek to impact traffic collision rates

Collision · Econometrics · Generalized additive model · Geography · Hidden Markov model · Markov model · Markov process · Speed limit · Statistics · Computer Science · Mathematics · Traffic and Road Safety · Traffic Prediction and Management Techniques · Urban Transport and Accessibility · Artificial Intelligence