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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.

通过使用神经网络（NN）而不是实际的分解逻辑模型来研究应用于“旅游概念”的基于活动的旅行需求模型。为了收集新泻县新津地区的活动日记数据，进行了问卷调查。评估了三种不同类型的模型，即同时类型、无反馈和有反馈的序列类型，并开发了它们的微观模拟模型来预测每个人的旅行。带反馈的序列模型在三种类型中实现了最高的命中率。接下来的一项研究采用了国家政府于1999年11月对新泻县长冈市市区进行的人员旅行调查数据。通过神经网络的实证估计表明，活动和旅行在日常活动模式中并不是独立的，而是密切相关的。它模拟了在多种条件下的个人自主出行模式，假设引入了 TDM 措施，例如灵活工作时间或错开工作时间。微观模拟显示了其预测 TDM 措施对日常出行模式影响的实用能力。交通微观模拟研究开发了一种最优控制系统，可以通过改进现有的自适应信号控制来实时计算孤立交叉口的最佳信号设置。开发了一种启发式算法，可以搜索排的间隙并选择其中的最佳信号设置，以加快计算时间。将启发式方法扩展到具有公交车抢占功能的两个交叉路口，不仅可以最大限度地减少车辆延误，还可以通过改变参数来最大限度地减少乘客延误。该仿真系统用于评估长冈 HOV 车道和一座桥梁的公交优先措施。该系统对车道调节、公交优先信号控制、限流信号控制的效果进行了评估。