  Vehicle mobility optimization in urban areas is a long-standing problem in
smart city and spatial data analysis. Given the complex urban scenario and
unpredictable social events, our work focuses on developing a mobile sequential
recommendation system to maximize the profitability of vehicle service
providers (e.g., taxi drivers). In particular, we treat the dynamic route
optimization problem as a long-term sequential decision-making task. A
reinforcement-learning framework is proposed to tackle this problem, by
integrating a self-check mechanism and a deep neural network for customer
pick-up point monitoring. To account for unexpected situations (e.g., the
COVID-19 outbreak), our method is designed to be capable of handling related
environment changes with a self-adaptive parameter determination mechanism.
Based on the yellow taxi data in New York City and vicinity before and after
the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate
the effectiveness of our method. The results show consistently excellent
performance, from hourly to weekly measures, to support the superiority of our
method over the state-of-the-art methods (i.e., with more than 98% improvement
in terms of the profitability for taxi drivers).
