Jiaze Sun
ABSTRACT
In this paper, for two objectives of the minimum driving distance and the maximum number of passengers compatible, a landmark-based multi-objective route planning algorithm (LB-MORPA) is proposed. LB-MORP incorporates the idea of a bi-directional random walk into the generation of the initial population, designs a new crossover and mutation operator for the evolutionary process, and predicts a set of the nondominated solution set to provide drivers with multiple route options. The comparison experiments with other classical multi-objective evolutionary algorithms prove that the LB-MORP has good performance.
- Brian Donovan. 2016. New York City Taxi Trip Data (2010-2013) University of Illinois at Urbana-Champaign. Google Scholar
Cross Ref
- Michael Garey and David Johnson. 1979. Computers and Intracdtability: A Guide to the Theory of NP-Completeness.Google Scholar
- Matthias Grossglauser Michal Piorkowski, Natasa Sarafijanovic-Djukic. 2009. CRAWDAD dataset epfl/mobility (v. 2009-02-24). Google ScholarCross Ref
- Tanvi Verma, Pradeep Varakantham, Sarit Kraus, and Hoong Chuin Lau. 2017. Augmenting Decisions of Taxi Drivers through Reinforcement Learning for Improving Revenues. In International Conference on Automated Planning & Scheduling.Google Scholar
Index Terms
- Landmark-based multi-objective route planning for large-scale road net
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