A study on Travel Demand Forecasting for Various New Transportation Services Utilizing Multiple Data Sources

利用多种数据源的各种新型交通服务的出行需求预测研究

• 批准号: 15360275
• 负责人: MORIKAWA Takayuki
• 金额: $ 3.65万
• 依托单位: Nagoya University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15360275/
• 关键词: Travel Demand Forecasting; Disaggregate Discrete Choice Model; Stated Preference Data; Aggregate Data; Probe-vehicle; Route Choice Analysis; Bounded Rationality; Data Mining; プローブカーデータ; パーソントリップ調査; RP; SP融合推定法; 幹線旅客純流動データ; 都市間交通

This research aims to improve the travel demand forecasting for new transportation services. We especially focused on disaggregate discrete choice models which have been widely applied for travel demand analysis due to its tractability and flexible structure.Firstly, we conducted a post-project evaluation of the demand forecast for a new transit system which was based on the conventional four-step demand forecasting procedure. The result showed that the temporal transferability of the demand model, especially modal split model, seems to be low and the evaluation of the effect of access travel might be a crucial factor in demand forecasting for a new transit system. We, therefore, examined the transferability of disaggregate mode choice model using repeated cross-sectional person trip data and investigated factors which affects the temporal transferability. Also, we investigated to what extent the zoning system may affect the estimation result of mode choice model and then developed the … More methodologies for complementing accuracy of observations on origin and destination locations.Secondly, we investigated the applicability of the various data sources toward the demand forecasting to improve its predictive accuracy. An integrated travel demand model which utilizes combined estimation across multiple data sources such as revealed preference, stated preference(SP), and aggregate data was developed. The model was empirically applied for the demand forecast of intercity high speed rail, and results showed that the utilization of SP and aggregate data might be very effective to reflect the user's intention toward new transportation services and to capture the nation-wide changes in travel demand. Next, we conducted in-depth analysis on SP data and proposed the modeling methodologies and the survey schemes to precisely capture the individual's preference and travel behavior changes. Furthermore, we investigated the availability of prove-vehicle data, which is collected from the on-board equipment such as GPS devices, and the results showed that it has huge potentials to understand the dynamic aspects of route choice behavior.Thirdly, we focused on the modeling methodologies to improve the demand forecasting for ITS and TDM policies. Based on the concept of "bounded rationality", we developed the travel behavior model in which the semi-ordered lexicographic decision rule was embedded. The proposed model was empirically applied for the actual and simulated data and showed high data reproducibility and predictive accuracy compared to the conventional random utility maximization model while its stability in model estimation was quite low. We, therefore, developed the estimation methodologies utilizing data mining method and the empirical results showed that it might be useful to specify the threshold parameters in the model resulting in the improvement of stability in model estimation. Less

这项研究旨在改善新运输服务的旅行需求预测。我们特别关注分类离散选择模型，该模型由于其易干性和柔性结构而被广泛应用于旅行需求分析。首先，我们对新运输系统的需求预测进行了项目后评估，该预测是基于常规的四步需求预测程序。结果表明，需求模型的暂时可传递性，尤其是模态分型模型似乎很低，并且评估访问效果可能是新运输系统需求预测的关键因素。因此，我们使用重复的横截面人跳闸数据研究了分类模式选择模型的可传递性，并研究了影响暂时转移性的因素。此外，我们研究了分区系统在多大程度上可能影响模式选择模型的估计结果，然后开发了……更多的方法来完成对来源和目的地位置的观察准确性。第二，我们研究了各种数据源对需求预测的适用性，以提高其预测性准确性。一个综合的旅行需求模型，该模型利用了跨多个数据源（例如揭示的偏好，偏好（SP）和汇总数据）的组合估计。该模型迫切适用于Intercity高速导轨的需求预测，结果表明，SP和总数据的利用可能非常有效，可以反映用户对新运输服务的意图并捕获全国性的旅行需求变化。接下来，我们对SP数据进行了深入的分析，并提出了建模方法和调查方案，以准确捕获个人的偏好和旅行行为改变。此外，我们研究了从船上设备（例如GPS设备）收集的Prove-Vehicle数据的可用性，结果表明，它具有了解路线选择行为的动态方面的巨大潜力。我们专注于建模方法，以改善其和TDM策略的需求预测。基于“有限理性”的概念，我们开发了旅行行为模型，其中半序列的词汇决策规则被嵌入。与常规的随机效用最大化模型相比，该模型紧急应用于实际和模拟数据，并显示出高的数据可重复性和预测精度，而模型估计中的稳定性则很低。因此，我们开发了利用数据挖掘方法的估计方法，经验结果表明，指定模型中的阈值参数可能会有用，从而导致模型估计中稳定性的提高。较少的

项目成果

プローブカーデータを利用したドライバーの経路選択行動に関する研究

基于探测车数据的驾驶员路径选择行为研究

• 发表时间: 2003
• 期刊: 土木計画学研究・講演集 Vol.27(CD-ROM)
• 作者: 三輪富生;森川高行
• 通讯作者: 森川高行

Post-project evaluation of the demand forecast for a new transit system

新交通系统需求预测的项目后评估

• 发表时间: 2004
• 期刊: Transport Policy Studies' Review, ITPS Vol.7, No.2
• 作者: Morikawa;T.;Nagamatsu;Y.;Sanko;N.
• 通讯作者: N.

Latent class model for complementing accuracy of observation on locations of origin and destination in trip mode choice analysis

潜在类模型，用于补充出行模式选择分析中出发地和目的地位置观察的准确性

• 发表时间: 2004
• 期刊: Proc.of Infrastructure Planning, JSCE Vol.30(CD-ROM)
• 作者: Yamamoto;T.;Komori;R.
• 通讯作者: R.

Yao, E., Morikawa, T.: "A Study on Integrated Intercity Travel Demand Model"Proc of 10th International Association for Travel Behaviour Research Conference. (CD-ROM). (2003)

Yao, E., Morikawa, T.：“综合城际出行需求模型研究”第十届国际出行行为研究协会会议论文。

プローブカーデータを用いた経路特定手法と旅行時間推定に関する研究

基于探测车数据的路径识别方法及行程时间估算研究

• 发表时间: 2003
• 期刊: 第2回ITSシンポジウム2003 Proceedings
• 作者: 三輪富生;境隆晃;森川高行
• 通讯作者: 森川高行