Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

This study analyzed the main travel routes of shared autonomous vehicles by using DTG (Digital Tachograph) data of Daegu Metropolitan City under the premise that the driving mode of the shared autonomous vehicle is similar to that of a taxi. Based on the taxi DTG data that recorded the driving route from the taxi user’s entry point to the exit point, the ride rate analysis result was visualized as a heat map. The main travel routes of taxi users were found through the driving route. As a result of the loading rate analysis, it was confirmed that the loading rate was high in Dongseong-ro, the downtown area of Daegu Metropolitan City, and the nearby area of Dongdaegu Station. The following high passenger rate region was analyzed as the region with a high proportion of commercial facilities. In the case of movement routes, it was confirmed that they appear mainly along the same axis as subway lines and major arterial roads in the city. And through this, it was possible to obtain the implication that the infrastructure for shared autonomous vehicles could be built along arterial roads, auxiliary arterial roads, and subway lines in the future. In addition, this study established a regression model using machine learning techniques such as Random forest and LightGBM to confirm the variables that affect the user’s choice to use a taxi. The distance to the subway station, the arterial road, the number of floating population, the card sales performance, and the distance to the bus stop were derived as the primary variables that affect the decision of the taxi route. As a result of model estimation, it was found that both regression models secured the fit. It is expected that the results of this study can be utilized to strategically arrange essential infrastructure related to the driving of shared autonomous vehicles in consideration of changes in these influencing variables.