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

通过开发出行行为和交通的微观模拟模型来评估 TDM 措施

基本信息

批准号：
11650542
负责人：
MATSUMOTO Shoji
金额：
$ 1.54万
依托单位：
Nagaoka University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2001
项目状态：
已结题

项目摘要

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.

对基于活动的旅行需求模型的研究应用于使用神经网络（NN），而不是实际分类logit模型。进行了问卷调查，以在Niigata的Niitsu地区收集活动日记数据。估计了三种不同类型的模型，一种简单类型和无反馈和反馈的序列类型，并开发了其微仿真模型以预测每个人的旅行。具有反馈的序列模型达到了这三种类型的最高打击比。下一项研究采用了由国家政府于1999年11月进行的Niigata的Nagaoka大都会地区的人行调查数据。神经网络的经验估计表明，活动和旅行并非独立，但在每日活动模式中密切相关。假设引入TDM测量值，例如灵活的工作时间或交错工作时间，它在许多条件下模拟了单独的酌处旅行模式。微观仿真显示其实际能力可以预测TDM测量对日常旅行模式的影响。对流量微仿真的研究开发了一个最佳控制系统，该系统可以通过改善现有的自适应信号控制，以实时计算最佳信号设置，以实现孤立的交集。开发了一种搜索排间隙并在其中选择最佳信号设置的启发式算法是为了加快计算时间的速度。一种启发式方法扩展到了两个带公交率的交叉点，不仅可以最大程度地减少车辆延迟，还可以通过更改参数来最大程度地减少乘客延迟。将模拟系统应用于评估HOV车道的总线优先级和Nagaoka的桥梁。该系统评估了车道调控，总线优先信号控制和流入限制信号控制的影响。