Abstract. 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.

Abstract. At the beginning of the COVID-19 pandemic, most scholars focused on how international transportation (such as airlines) spread the virus to different countries. At this point, scholars have begun to pay more attentions on how COVID-19 locally transmission via ground transportation systems. Because many people use these ground services to commute in urban areas, a high passenger volume may lead to a domestic large outbreak. Without detailed disease spreading path, healthcare professionals are still not sure where and how to apply these anti-epidemic measures. Therefore, this study chose the Taipei metro system as our study area to investigate the relationship between metro station passenger volume and COVID-19 transmission. By using the electric metro ticket data, we know the movement of metro passengers in Taipei, and this OD movement dataset was used to estimate the spreading path of the COVID-19. In order to simulate possible Covid-19 spreading cases in the real world, two different methods (the agent-based model (a microlevel simulation) and the effective distance method (a macro-level estimator)) were applied. Then, we compared the COVID-19 arrival order for each station. In our result, the average infectious order of stations of agent based model and shortest path effective distance is similar. Among all stations, Taipei Main Station is the first infectious station, and the top 15 infectious stations are similar according to result of the two method. Our result may help the authority choose proper methods to simulate the epidemic local transmission and then allocate resources effectively in the future.