Route optimization of vacant taxicab considering sequential dependence in abstract grid network based on quadtree

ABSTRACT

In this paper, we propose a method of route optimization of vacant taxicab based on the abstract grid network. Firstly, an abstract grid network is built by the quadtree technology with the vacant trajectory data embedded. Secondly, the Markov decision process framework considering the sequential dependence is used to model the problem. Finally, we solve this problem by the policy iteration and compare the method by taking the GPS trajectory data in different hours of 797 taxicabs in a typical working day in Shenzhen, comparing that with the optimal strategy without considering sequential dependence, MNP, local hotspot and random walk algorithm by simulation, respectively. The results show that: The use of quadtree can reduce the sparseness of matching probability from 80.46% to 30.18%. The average revenue per unit distance has increased by 6.10%, 8.75%, 33.24% and 60.06%. The vacant driving rate has decreased by 5.33%, 6.15%, 18.86%, and 23.18%.

KEYWORDS:

• Taxicab
• dynamic route optimization
• Markov decision process
• sequential dependence
• quadtree
• abstract grid network

Acknowledgments

This work was supported by the National Natural Science Foundation of China (General Program 52172318 & 52131203). Additionaly, here is a special acknowledgement to my love and friends, thanks for the support and concern from Zhaoxuan Li over the passed years.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Innovative Research Group Project of the National Natural Science Foundation of China [52172318]; Innovative Research Group Project of the National Natural Science Foundation of China [52131203].