Fukasawa, Yuta (Toyota Motor Corporation), Yamada, Kota (IVIS Inc. ATD Department), Ishii, Yoshinao (Toyota Motor Corporation), Sasai, Takeyuki (TOYOTA MOTOR CORPORATION), Fukushima, Shintaro (TOYOTA MOTOR CORPORATION)

Estimating Reduction in Travel Time Based on Large Scale Driving Data from Connected Vehicles

Scheduled for presentation during the Regular Session "Traffic prediction and estimation II" (WeBT6), Wednesday, September 25, 2024, 15:50−16:10, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 22, 2024

Abstract

This study estimates the potential reduction in travel time owing to the selection of detour routes with shorter travel times. This is a central problem in intelligent transportation systems. However, the number of vehicles and drivers employed in the previous studies was severely limited. Consequently, to strengthen the evidence of a reduction in travel time, we quantitatively estimate the reduction by leveraging large-scale vehicle driving data collected from connected vehicles on metropolitan expressways in Japan. The scale of our dataset is overwhelming in its size, that is, 153,480 trips extracted from 611,480 passenger vehicles over one month, compared with one in previous studies, up to 200 passenger vehicles. The experimental results yield the following outcomes. (i) The reduction rate in travel time exceeded 5% for approximately 15.7% of all trips. (ii) A consistent trend of significant travel time reduction was observed across consecutive years. The findings of this study provide an important foundation for the further consideration of transportation measures for data-driven urban traffic management.