Mr. Ge Qian received the Kikkawa-Yamaguchi Prize!
Mr. Ge Qian, a research fellow, received the Kikkawa-Yamaguchi Prize, which is given to the best doctoral thesis in the civil engineering field at Tokyo Institute of Technology. Congratulations!
The main results of his doctoral thesis, “DATA-DRIVEN AND MACROSCOPIC APPROACHES FOR ESTIMATING ORIGIN-DESTINATION TRAVEL DEMAND,” are as follows: (1) The traffic flow tables collected from conventional traffic surveys in a static framework (OD tables) were used to develop the latest OD tables. (2) Under a dynamic framework in which OD traffic varies with time, we have developed a computationally efficient dynamic OD traffic estimation method by integrating macroscopic and data-oriented approaches. As for (1), we updated static OD tables using the entropy maximization principle, and confirmed the validity of the proposed method through empirical analysis using the 2008 Tokyo Metropolitan Area Person Trip Survey and the 22012 mesh unit population statistics data. As for (2), the entire urban area was considered as an input-output system consisting of multiple reservoirs, and the flow between different reservoirs (inter-area OD traffic) was described in a manner consistent with traffic flow theory. These research results have been published in Transportation Research Part C and Part B, respectively, which are the highest ranked journals in the field of transportation engineering.
This dissertation concentrates on the problem of inferring origin-destination (OD) travel demand from multiple sources of data. This dissertation attempts to extend previous studies in using new data, building macroscopic dynamic network loading for complex multi-reservoir urban transportation network and developing novel method that incorporates perceived and observed data for estimating path flow. We present a maximum entropy based updating method to yield static OD matrices using aggregate mobile phone data. We adopt this dataset for the following reasons: low cost, easy to collect, and privacy-free. The proposed approach calculates trip flows of each OD pair using two sequential sub-models. Performance of the proposed methodology is validated through a numerical example and confirmed by case study using the data of Tokyo. However, the aggregate mobile phone data is not sufficient for dynamic OD demand estimation (DODE) problem. We build a dynamic network loading model upon the macroscopic characteristics of traffic flow depicted by Macroscopic Fundamental Diagram (MFD). The dynamic net- work loading model for multi-reservoir system (MRDNL) is specified in terms of a system of partial differential equations following the conservation law. Spatial discretization method and numerical scheme are also developed for tracking the vehicles’ behavior guided by this model. To identifying reservoirs in large network more efficiently without using the demand data, we present a community detection method that yields neighborhoods in which the links are closely related and share similar traffic characteristics. Finally, we emphasize on developing a methodology framework for estimating dynamic OD demand using traffic counts with incorporation of observed path cost.