2022 年 14 巻 p. 530-541
The paper introduces a method for acquiring trip behaviors within walking distance by means of multiple big data. First, an optimal set of big data is selected from possible sets of big data in the transport sector to estimate the trip behaviors. Second, the authors propose a method of estimating trip volume and trip modes. Finally, the proposed method is applied to a case study that has been carried out at Tachikawa Station of the Japan Railway Central Line in Tokyo in order to validate the proposed method. A field test with the use of the Wi-Fi packet sensor was conducted at 11 locations including stores and traffic nodes on 1st of September 2018．The estimated trips were nearly the same as the actual situations. The authors have demonstrated the possibility of using Wi-Fi packet sensor data and Mobile Phone Location Data to acquire trip behaviors within walking distance.
