Macro-spatial approach for evaluating the impact of socio-economics, land use, built environment, and road facility on pedestrian safety

With the increasing demand for sustainability, walking is being encouraged as a main active mode of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes, which can discourage road users from walking. Therefore, studying the factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-vehicle crashes and various zone characteristics in the city of Vancouver. The goal is to assess the impact of socio-economics, land use, built environment, and road facility on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometres travelled were used as the main traffic exposure variables in the models. The safety models showed that pedestrian-motorist crashes were non-linearly positively associated with the increase in traffic exposure. The crashes were also found positively associated with the socio-economic variables (i.e., employment and household densities), some built environment variables (transit stop, traffic signal, and light pole densities), commercial area density, and arterial-collector roads proportion. On the other hand, the models revealed a decline in the pedestrian-motorist crashes associated with the increase in the proportions of pedestrian-actuated signals and local roads, as well as the increase in the recreational and residential areas' densities. The spatial effects were accounted for in the full Bayes models and were found significant, which imply the importance of considering spatial correlation when developing macro-level pedestrian safety models.