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Towards smart transportation: A learning‐based data‐driven optimization approach for electric taxi dispatch problem
Author(s) -
Li Xiaoming,
Zhao Liang
Publication year - 2020
Publication title -
internet technology letters
Language(s) - English
Resource type - Journals
ISSN - 2476-1508
DOI - 10.1002/itl2.164
Subject(s) - computer science , mathematical optimization , context (archaeology) , kernel density estimation , stochastic programming , stochastic optimization , monte carlo method , parametric statistics , optimization problem , economic dispatch , key (lock) , electric power system , algorithm , mathematics , power (physics) , paleontology , statistics , computer security , estimator , biology , physics , quantum mechanics
Electric taxi dispatch problem (ETDP) is one of the key issues in smart transportation. Existing study in the context of centralized optimization adopts either deterministic optimization, regular stochastic programming (SP) or simulation technique. Nevertheless, in data‐driven environment, the real passenger demands normally follow complicating probability distribution which cannot be described exactly by the parametric approaches. Hence, we propose a novel data‐driven optimization framework that integrates robust kernel density estimation (RKDE) and the two‐stage SP modeling technique. In particular, the probability distributions of customer demands are derived from historical data by RKDE, and the ETDP is formulated as a two‐stage SP model with the input parameters from RKDE. Meanwhile, a Monte Carlo method called sample average approximation is introduced to reformulate and solve the SP model. Finally, the experimental results show that the proposed approach outperforms the deterministic counterpart with the average demands as the input.