@article{ref1,
title="Accounting for previous events to model and predict traffic accidents at the road segment level: a study in Valencia (Spain)",
journal="Physica A: statistical mechanics and its applications",
year="2022",
author="Briz-Redón, Álvaro and Iftimi, Adina and Montes, Francisco",
volume="585",
number="",
pages="e126416-e126416",
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.<p /> <p>Language: en</p>",
language="en",
issn="0378-4371",
doi="10.1016/j.physa.2021.126416",
url="http://dx.doi.org/10.1016/j.physa.2021.126416"
}