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An Efficient Multi-request Route Planning Framework Based on Grid Index and Heuristic Function

  • Jiajia LiEmail author
  • Jiahui Hu
  • Vladislav Engel
  • Chuanyu Zong
  • Xiufeng XiaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

In this paper, we will discuss the recently studied and currently less studied path finding problem, which is multi-request route planning (MRRP). Given a road network and plenty of points of interests (POIs), each POI has its own service lists. User specifies the departure place and destination location as well as request lists, the task of MRRP is to find the most cost-effective route from the user’s starting point to the end point and satisfy all the user’s requests. At present, only one paper solved MRRP problem. Its method can’t be extended to time-dependent road networks directly with time-varying values because it takes up more memory. In this paper, we propose a new framework based on grid file and heuristic functions for solving MRRP problem. The framework consists of three phases. The area arrangement phase compares request lists with service lists contained in the adjacent grid nearby to filter unnecessary regions. In the routing preparation phase, the most profitable POIs are selected to meet the needs of users. And the path finding phase obtains the final shortest path results. Extensive experiments have been conducted to evaluate the performance of the proposed framework and compare with the state-of-the-art algorithms. The results show that the route costs selected by the proposed method are 2–3 times less than those obtained by others under different settings. Meanwhile, the execution time of our algorithm is 2–3 times less than them.

Keywords

Route planning Multi-request Heuristic function 

Notes

Acknowledgement

This research was partially supported by the Natural Science Foundation of Liaoning Province under Grant No. 2019JH3/10300299; National Natural Science Foundation of China under Grant Nos. 61502317, 61802268.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceShenyang Aerospace UniversityShenyangChina

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