During COVID-19, the suspension of the dine-in option at restaurants had significantly increased online food delivery crashes in Taiwan. Nevertheless, the majority of current studies remain focused on the common motorcycle, which has distinct driving habits and routes than a delivery motorcycle. Even though some recent studies identified the variables contributing to delivery motorcycle crashes, they still restricted in defining crash severity model and did not account for spatial dependences. In this study, two different models were used in this study: the generalized linear model (GLM), and the geographically weighted negative binomial model (GWNBR) to estimate crash frequency in a non-stationary pattern. In 2020, there were 2314 delivery motorcycle crashes in Taipei, according to the study area. Besides that, the point of interests data from 456 villages in Taipei city was considered as related crash factors for further analysis. According to the results, GWNBR showed the best performance in terms of log-likelihood, Akaike Information Criterion (AIC), and Root Mean Square Error (RMSE). Furthermore, this research reveals that commercial areas and bus stations had a significant impact on delivery motorcycle crashes. As per the coefficient distribution, the effect is exacerbated in rural areas where the traffic policy is still a major concern. As the popularity of delivery food services grows, this topic will become even more important in the future.
|頁（從 - 到）||163-169|
|期刊||International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives|
|出版狀態||Published - 2022 5月 30|
|事件||2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV - Nice, France|
持續時間: 2022 6月 6 → 2022 6月 11
All Science Journal Classification (ASJC) codes