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

本研究旨在改善新交通服务的出行需求预测。本文重点研究了非集计离散选择模型，该模型因其易处理性和结构灵活性而被广泛应用于交通需求分析中。首先，我们对一个新的公交系统的需求预测进行了项目后评估，该模型是基于传统的四步需求预测程序。研究结果表明，需求模型尤其是交通方式划分模型的时间可移植性较低，评价交通方式对交通出行的影响可能是新公交系统需求预测的关键因素。因此，我们使用重复的横截面个人出行数据来检验非集计方式选择模型的可转移性，并调查影响时间可转移性的因素。此外，我们还调查了分区制度在多大程度上会影响模式选择模型的估计结果，然后开发了 ...更多信息 第二，为了提高预测精度，我们研究了各种数据源对需求预测的适用性。综合旅游需求模型，利用多个数据源，如显示偏好，陈述偏好（SP）和聚合数据的组合估计开发。将该模型应用于城际高速铁路需求预测，结果表明，利用SP和聚合数据可以有效地反映用户对新交通服务的意向，并捕捉全国范围内的出行需求变化。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，以准确地捕捉个人的偏好和旅行行为的变化。此外，我们还研究了从车载设备（如GPS设备）收集的验证车辆数据的可用性，结果表明，它在了解路径选择行为的动态方面具有巨大的潜力。第三，我们重点研究了提高ITS和TDM政策需求预测的建模方法。基于“有限理性”的概念，建立了嵌入半序字典决策规则的出行行为模型。所提出的模型经验应用于实际和模拟数据，并显示出较高的数据再现性和预测精度相比，传统的随机效用最大化模型，而其稳定性模型估计是相当低的。因此，我们开发的估计方法，利用数据挖掘方法和实证结果表明，它可能是有用的，以指定的阈值参数在模型中，从而提高模型估计的稳定性。少

项目成果

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.

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

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.

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

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。