Data-Driven Methods for Travel Time Estimation: A Survey

Zhipeng Zheng1; Yongchao Ye1; Yuanshao Zhu1; Shiyao Zhang1; James J.Q. Yu2

10.1109/ITSC57777.2023.10422502

2023

IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

2153-0009

979-8-3503-9947-9

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

1292-1299

24-28 Sept. 2023

Bilbao, Spain

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Travel time estimation is a crucial component of intelligent transportation systems, affecting various applications such as navigation, ride-hailing, and route planning. Traditional methods for travel time estimation rely on subjective judgments, limited data sources, and straightforward modeling techniques. Owing to recent advances in data mining and machine learning, numerous data-driven methods are adopted to address the problem that occurred in traditional schemes, which demonstrate exceptional performance. In this paper, we present a comprehensive survey of data-driven methods for travel time estimation, encompassing application scenarios, spatial-temporal modeling approaches, and data representation learning techniques. To support and promote further research in this field, we provide a valuable list of open data sources and source codes, offering researchers a solid foundation for their future endeavors. Furthermore, this survey discusses emerging trends and key challenges faced by the research community, such as the integration of real-time data streams and the use of uncertainty estimation. We also explore the potential impact of these advancements on transportation systems, highlighting opportunities for improvement and innovation. To the best of our knowledge, this work is among the first to offer a comprehensive, in-depth review of data-driven methods for travel time estimation, providing researchers and practitioners with a valuable reference in the field.

Surveys Technological innovation Uncertainty Estimation Transportation Data models Streams

第一

英语

CPCI-S

Stable Support Plan Program of Shenzhen Natural Science Fund[20220815111111002]

Automation & Control Systems ; Computer Science ; Transportation

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Transportation Science & Technology

WOS:001178996701045

IEEE

1.Department of Computer Science and Engineering, Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, China
2.Department of Computer Science, University of York, United Kingdom

Zhipeng Zheng,Yongchao Ye,Yuanshao Zhu,et al. Data-Driven Methods for Travel Time Estimation: A Survey[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1292-1299.