Recently, with the rapid expansion of cities, optimizing taxi driving routes for improving taxi revenue efficiency has become the core issue of taxi system. However, most current research focuses on increasing platform revenue instead of improving drivers’ revenue in a centralized dispatch taxi system just like DiDi, which results in a slower driver income growth and greater difficulties for recruiting drivers. To solve this problem, we propose a strategy of deep reinforcement learning based on driver mode. Firstly, the sequence selection process of drivers is modeled as markov decision-making process in driver mode. Then, we propose a learning scheme based on deep Q network to optimize the driver's decision-making strategy. We know that the real selection of historical taxi drivers is very helpful to the selection of current taxi drivers, so we choose the historical record of the current location as the edge data to update the edge network. Finally, we used a real data set generated by more than 1,400 taxis in Changsha. The simulation experiments show that our scheme reduced cruising time of taxis and improved the driver's income by 4-5%. The carbon emissions are obviously reduced by saving almost 6% fuel consumption, which contributes significantly to green mobility.

中文翻译：

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好还是平庸？出租车收入效率优化的深度强化学习方法

近年来，随着城市的快速发展，优化出租车的行驶路线以提高出租车的收入效率已成为出租车系统的核心问题。但是，当前的大多数研究都集中在增加平台收入上，而不是像DiDi那样在集中调度出租车系统中提高驾驶员的收入，这导致驾驶员的收入增长缓慢，招募驾驶员的困难更大。为了解决这个问题，我们提出了一种基于驾驶员模式的深度强化学习策略。首先，将驾驶员的顺序选择过程建模为驾驶员模式下的马尔可夫决策过程。然后，我们提出了一种基于深度Q网络的学习方案，以优化驾驶员的决策策略。我们知道，真正选择历史悠久的出租车司机对选择当前的出租车司机非常有帮助，因此我们选择当前位置的历史记录作为边缘数据以更新边缘网络。最后，我们使用了由长沙1,400多辆出租车生成的真实数据集。仿真实验表明，我们的方案减少了出租车的出行时间，并使驾驶员的收入提高了4-5％。通过节省近6％的燃料消耗，明显减少了碳排放，这极大地促进了绿色出行。