| Co-Investigator(Kenkyū-buntansha) |
YAMAMOTO Toshiyuki Nagoya University, Grad.School of Engineering, Associate Professor, 大学院・工学研究科, 助教授 (80273465)
KURAUCHI Shinya Nagoya University, Grad.School of Engineering, Research Associate, 大学院・工学研究科, 助手 (90314038)
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| Budget Amount *help |
¥5,700,000 (Direct Cost: ¥5,700,000)
Fiscal Year 2004: ¥2,800,000 (Direct Cost: ¥2,800,000)
Fiscal Year 2003: ¥2,900,000 (Direct Cost: ¥2,900,000)
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| Research Abstract |
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
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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
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