This study proposes a novel multinomial generalized Poisson model with error components and spatiotemporal dependence (ST-EMGP) to analyze multi-period crash frequency and severity data. The proposed model not only simultaneously models crash frequency and severity, but also accommodates spatial and temporal dependence (spatiotemporal dependence) by specifying a spatiotemporal function. To demonstrate the applicability of the proposed model, a case study is conducted on five consecutive years' (2004-2008) crash data of Taiwan's Freeway No. 1. Estimation results show that ST-EMGP model performs better than the models without considering spatiotemporal dependence in terms of adjusted likelihood ratio index, consistent Akaike information criterion and log-likelihood test. Additionally, the estimated ST-EMGP model shows that spatial and temporal dependences exist and correlate mutually. Spatial dependence may overstate its impact magnitude, but underestimate its impact range when temporal dependence is ignored. According to the distribution and regression results of spatiotemporal effects, temporal effects are higher in crash frequency and are mainly affected by traffic characteristics; while spatial effects are higher in severe crash severity levels and are mainly affected by geometric configuration. Obviously, the proposed model can successfully elucidate the sources of spatiotemporal dependence as well as their effects on crash frequency and severity.
- Crash frequency and severity modeling
- Multi-period accident data
- Multinomial generalized Poisson
- Spatiotemporal dependence