This research firstly proposes a model to predict bus arrival time based on signal database and image processing sensor. Applying the prediction model to specific scenarios with different bus priority levels, the research makes a comparison among bus priority strategies. These priority strategies include bus signal priority strategy with timing techniques of early green or green extension; bus preemption strategy with exclusive lanes; and bus preemption strategy without exclusive lanes. The comparison's results show that the increases of bus priority level can improve the service level of buses significantly, which can make the bus turn-delays at the intersection reduce by up to 100% compared with that of the normal base case. However, high priority levels for buses may negatively affect non-bus vehicles at the intersection, causing an increase in the turn delay of non-bus vehicles, by up to 94.2% for the preemption strategy with exclusive lanes. The bus priority level not only shapes the specific characteristics of bus trajectory as well as impacts on vehicle travel times in the main and side streets but also figures out the role of bus occupancy in the intersection performance.

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.