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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.

以神经网路取代传统的非集计logit模型，应用于“旅游概念”之活动导向交通需求模型之研究。在新泻的新津地区进行了一项问卷调查，以收集活动日记数据。三种不同类型的模型，一个同时型，和类型的序列没有反馈和反馈，估计和他们的微观模拟模型的开发，以预测每个人的旅行。在三种类型中，带反馈的序列模型的命中率最高。接下来的研究使用了1999年11月由国家政府进行的新泻长冈大都市区的人旅行调查数据。神经网络的经验估计表明，一个活动和旅游不是独立的，但密切相关的日常活动模式。它模拟了一个人的自由裁量权的出行模式下的一些条件下，假设引入TDM措施，如弹性工作时间或错开工作时间。微观模拟表明，它的实用能力，预测TDM措施对日常出行模式的影响。通过交通微观仿真的研究，开发了一种最优控制系统，该系统通过改进现有的自适应信号控制，可以真实的实时计算出单个交叉口的最佳信号设置。为加快计算速度，提出了一种启发式算法，该算法通过搜索编队之间的间隙，选择最佳信号设置。将启发式算法扩展到两个交叉口，通过改变参数，使车辆延误和乘客延误同时最小化。模拟系统被应用于评估HOV车道和长冈桥的公交优先措施。评价了车道调整、公交优先信号控制和流入限制信号控制的效果。