TY  - JOUR
PY  - 2022//
TI  - Accounting for previous events to model and predict traffic accidents at the road segment level: a study in Valencia (Spain)
JO  - Physica A: statistical mechanics and its applications
A1  - Briz-Redón, Álvaro
A1  - Iftimi, Adina
A1  - Montes, Francisco
SP  - e126416
EP  - e126416
VL  - 585
IS  - 
N2  - 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>
LA  - en
SN  - 0378-4371
UR  - http://dx.doi.org/10.1016/j.physa.2021.126416
ID  - ref1
ER  -