Accounting for previous events to model and predict traffic accidents at the road segment level: A study in Valencia (Spain)

Álvaro Briz-Redón, Adina Iftimi and Francisco Montes

Physica A: Statistical Mechanics and its Applications, 2022, vol. 585, issue C

Abstract: Predicting the occurrence of traffic accidents is essential for establishing preventive measures and reducing the impact of traffic accidents. In particular, it is fundamental to make predictions using fine spatio-temporal units. In this paper, the daily risk of traffic accident occurrence across the road network of Valencia (Spain) is modeled through logistic regression models. The spatio-temporal dependence between the observations is accounted for through the inclusion of lagged binary covariates representing the previous occurrence of a traffic accident within a spatio-temporal window centered at each combination of day and segment of the network. A temporal distance of 28 days and a fifth-order spatial distance are set as the limits of such dependence. Furthermore, the models include fixed effects in terms of several socio-demographic, network-related, and weather-related covariates. Temporal (month and day of the week) and spatial (borough-level) effects are also considered. The predictive quality of the models is examined through the Matthews correlation coefficient and the prediction accuracy index. The results indicate that the incorporation of spatio-temporal dependence improves the predictive ability of the models. However, while the inclusion of temporally-lagged covariates representing short-and mid-term temporal dependence yields more accurate predictions, the higher-order spatial lags barely alter model performance.

Keywords: Traffic accidents; Logistic model; Spatio-temporal effects; Segment-level analysis; Predictive accuracy (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:585:y:2022:i:c:s0378437121006890

DOI: 10.1016/j.physa.2021.126416

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