Dynamic Taxi Service Planning by Minimizing Cruising Distance Without Passengers

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This article studies route recommendation to a group of taxis by minimizing the overall mileage spent without customers.

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12 p.

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Luo, Zhongwen; Lv, Huimin; Fang, Fang; Zhao, Yishi; Liu, Yuanyuan; Xiang, Xiuqiao et al. November 14, 2018.

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  • © 2018 IEEE

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Description

This article studies route recommendation to a group of taxis by minimizing the overall mileage spent without customers.

Physical Description

12 p.

Notes

Abstract: It is a long-standing challenge to recommend routes to a group of taxis at different locations
to minimize the overall mileage spent without customs. This paper studies route recommendation to a group
of taxis by minimizing the overall mileage spent without customers. A new recommendation algorithm and
evaluation model are proposed. The algorithm recommends the current optimal route and updates both the
capacity and the probability along the route after getting a passenger. The new evaluation model is adapted to
estimate the performance of each candidate route. A simulator that imitates the recommendation process on
the real-world datasets and virtual taxis based on our recommendation system is developed. The experimental
results demonstrate the effectiveness of the proposed evaluation model. The proposed method using the
potential-cruising-distance model effectively reduces the global cruising distance of multiple taxis, especially
in the applications with a large number taxis.

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  • IEEE Access, 2018. Piscataway, NJ: IEEE

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  • Publication Title: IEEE Access
  • Volume: 6
  • Page Start: 70005
  • Page End: 70016
  • Peer Reviewed: Yes

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UNT Scholarly Works

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Submitted Date

  • October 30, 2018

Accepted Date

  • November 11, 2018

Creation Date

  • November 14, 2018

Added to The UNT Digital Library

  • Jan. 12, 2019, 5:20 p.m.

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Luo, Zhongwen; Lv, Huimin; Fang, Fang; Zhao, Yishi; Liu, Yuanyuan; Xiang, Xiuqiao et al. Dynamic Taxi Service Planning by Minimizing Cruising Distance Without Passengers, article, November 14, 2018; Piscataway, NJ. (https://digital.library.unt.edu/ark:/67531/metadc1404231/: accessed March 26, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.