Evaluation of TDM measures by developing microsimulation models for travel behavior and traffic

点击翻译按钮获取中文摘要

A research on an activity-based travel demand model applied to the "tour concept" by using the neural network (NN) rather than the practical disaggregate logit model. A questionnaire survey was carried out for gathering activity diary data in the region of Niitsu, Niigata. Three different kinds of model, a simultaneous type, and types of sequence without feedback and with feedback, were estimated and their microsimulation models were developed to predict each person's travel. The sequence model with feedback achieved the highest Hitting Ratio among the three types. A next study employed the person-trip survey data for the metropolitan area of Nagaoka, Niigata conducted by the national government in November 1999. The empirical estimation by neural networks revealed that an activity ＆ tour were not independent but closely interrelated among a daily activity pattern. It simulated an individual discretionary travel pattern under a number of conditions assuming the introduction of TDM measures such as flexible work times or staggered work hours. The microsimulation showed its practical capability to predict the impacts of TDM measures on daily travel patterns. A study of traffic microsimulation developed an optimal control system that could compute best signal settings in real time for an isolated intersection by improving the existing adaptive signal control. A heuristic algorithm that searched gaps of platoons and selected the best signal setting among them was developed to speed up a calculating time. A heuristic method extended to two intersections with bus-preemption, and could minimize not only vehicle delay, but also passenger delay by changing parameters. The simulation system was applied to evaluate bus priority measures for HOV lanes and a bridge in Nagaoka. The effects of lane regulation, bus priority signal control, and inflow restriction signal control were evaluated by the system.