A data driven approach to assessing the reliability of using taxicab as probes for Real-Time route selections

Abstract

Taxi service is one of the most important modes for urban transportation. In recent years, many taxi companies have been routinely collecting data to track the movement of each taxi for improving security, coordination, and service performance. This paper is intended to use the GPS vehicle positioning data to assess the route choice behavior of taxi drivers and explore if the routes selected by taxi drivers can be incorporated into a traveler information system. It is often perceived that taxi drivers have the ability to select quality routes assuming that: (1) they tend to be more knowledgeable about alternative routes and time-dependent traffic conditions than general public, including some publicly available route guidance systems due to the nature of their profession; and (2) they are typically more motivated to incorporate their knowledge about traffic conditions into their route choice decisions. An experimental study is conducted to examine the validity of these two assumptions. We have developed a framework that can effectively process the data into information about routes selected by taxi drivers and their associated travel times. The performance of the routes selected by taxi drivers is compared with the performance of those recommended by e-maps. Our results indicate that the routes selected by taxi drivers are generally more efficient than the routes recommended by some major e-maps, suggesting that taxi drivers are more active in selecting routes to avoid congestion.

KEYWORDS:

• Data driven approach
• route choice
• taxi service
• travel time estimation

Acknowledgments

Authors acknowledge use of the services and facilities of the Integrated Logistics and Transportation Systems Lab at the University of Arizona. We also like to thank Xiamen Department of Transportation for supplying the taxi data used in this study.

Additional information

Funding

Partial financial supports by grants from UCCONNECT (00008606), the National Natural Science Foundation of China (71421001 and 71531002), and Major Program of Key Disciplines in Dalian (2018J11CY023) are gratefully acknowledged.