Application of Travel Behavior Model with Intelligent Information Processing Application of Travel Behavior Model with Intelligent Information Processing

智能信息处理出行行为模型的应用 智能信息处理出行行为模型的应用

• 批准号: 14550528
• 负责人: AKIYAMA Takamasa
• 金额: $ 1.73万
• 依托单位: Gifu University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2002
• 资助国家: 日本
• 起止时间: 2002 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14550528/
• 关键词: Travel Behavior Analysis; Fuzzy Reasoning; Soft-Computing; Time Space Constraints; Transportation Policy; Congestion Pricing; Artificial Intelligence; Machine Learning; Travel Behavior Analysis; Fuzzy Reasoning; Soft-Computing; Time Space Constraints; Transportation Policy; Congestion Pricing; Artificial Intelligence; Machine Learning

The travel behavior model with intelligent information processing is constructed. The model is calibrated for evaluation of the urban transportation policy. In particular, the decision process of trip makers is described explicitly in detail. The travel behavior model can be applied for the impact analysis of the urban transportation policy.The elemental technologies of intelligent information processing are integrated to the decision process of trip makers. The sequential estimation of travel behavior has been formulated based on fuzzy logic as a soft-computing technique. Since the essential components of the estimation model should be mentioned, the advantages of fuzzy logic modeling can be summarized.The advantages of the decision tree algorithms can be summarized through the analysis of modal choice for commuters in urban area with empirical data. The artificial life approach make clear the existence of self-organized phenomena with efficiency according to the individual decision results even if social optimization techniques cannot be introduced as transport policies.The evaluation of road pricing policy can be realized with the particular assumption for urban area with established fuzzy travel behavior model. The options of trip maker to the transport policy are classified quite obviously. The essential changes of travel patterns are summarized with example cases through the estimation of fuzzy travel behavior model. It would be known from the observation that several alternative types of travel pattern might be realized in road pricing installation on the urban network.

构建具有智能信息处理的旅行行为模型。该模型经过校准以评估城市运输政策。特别是，Trip Makers的决策过程被详细描述。旅行行为模型可以应用于城市运输政策的影响分析。智能信息处理的基本技术已集成到Trip Makers的决策过程中。行进行为的顺序估计是基于模糊逻辑作为软计算技术制定的。由于应提及估计模型的基本组成部分，因此可以总结模糊逻辑建模的优势。可以通过分析具有经验数据的Urban地区通勤者的模态选择来总结决策树算法的优势。人造生命方法清楚地表明，即使不能将社会优化技术作为运输策略引入社会优化技术，也可以根据个人决策结果的效率清楚地表明，对道路定价政策的评估可以通过具有既定的模糊旅行行为模型的特定假设来实现。很明显，Trip Maker的选项对运输政策进行了分类。通过估计模糊旅行行为模型，总结了旅行模式的基本变化。从这样的观察中可以知道，在城市网络上的道路定价安装中可能会实现几种替代类型的旅行模式。