Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Taxicab service plays an essential and irreplaceable role in urban traffic system [Ji et al., 2020]. For example, in New York City, there are more than 21,000 taxi drivers and more than 80,000 ride-sharing drivers. Compared to other means of daily transportation, such as bus and subway, taxis usually offers a better trip experience in terms of comfort, convenience, and travel time accommodation. Thus, it has been a long-standing central issue to improve the efficiency of vehicle mobility by optimizing the route recommendation for drivers for taxi services in big cities like New York, Tokyo, and Beijing [Yuan et al., 2011, Zheng et al., 2014]. Based on large-scale taxi trace data, there is an extensive literature on route recommendation systems. Some studies focus on the traditional optimization method. For example, Qu et al. [2014] proposed a cost-efficient objective function and developed a greedy method to maximize the potential net profit. Similar methods can be found in [Ding et al., 2013, Zhou et al., 2016]. Stochastic optimization methods (e.g., simulated annealing -SA-) and parallel computing techniques have also been applied to route recommendation problems to speed up the route searching tasks (see [Ye This manuscript has been accepted by ACM Transactions on Intelligent Systems and Technology on April 25, 2021.