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

本研究旨在完善新交通服务的出行需求预测。重点研究了非集合型离散选择模型，该模型以其易处理、结构灵活等特点被广泛应用于出行需求分析。首先，在传统的四步需求预测方法的基础上，对新公交系统的需求预测进行了项目后评价。结果表明，需求模型，特别是模式分解模型的时间可转换性较低，对出行效果的评价可能是新公交系统需求预测的关键因素。因此，我们使用重复的横断面人员出行数据检验了非集计模式选择模型的可转移性，并考察了影响时间可转移性的因素。此外，我们还研究了区划制度对模式选择模型估计结果的影响程度，并开发了…其次，研究了不同数据源对需求预测的适用性，以提高需求预测的准确性。开发了一个综合的出行需求模型，该模型利用多个数据源的组合估计，如显示的偏好、陈述的偏好(SP)和汇总数据。将该模型应用于城际高铁的需求预测，结果表明，SP和聚合数据的利用可以非常有效地反映用户对新的交通服务的意图，并捕捉到全国范围内出行需求的变化。接下来，我们对SP数据进行了深入的分析，并提出了准确捕捉个体偏好和出行行为变化的建模方法和调查方案。此外，我们调查了从GPS设备等车载设备收集的Proven-Vehicle数据的可用性，结果表明它在理解路径选择行为的动态方面具有巨大的潜力。再次，我们重点研究了建模方法，以改进ITS和TDM政策的需求预测。基于有限理性的概念，建立了嵌入半有序词典决策规则的出行行为模型。将该模型应用于实际数据和模拟数据，与传统的随机效用最大化模型相比，该模型具有较高的数据重复性和预测精度，但其模型估计的稳定性较低。因此，我们利用数据挖掘方法开发了估计方法，实证结果表明，在模型中指定阈值参数可能有助于提高模型估计的稳定性。较少

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