论文标题
调查纽约市的出租车和优步竞赛：通过加强学习的多代理建模
Investigating Taxi and Uber competition in New York City: Multi-agent modeling by reinforcement-learning
论文作者
论文摘要
出租车业务已经过度监管了数十年。法规应确保在受控竞争环境中的安全和公平性。通过影响驾驶员和骑手的选择和行为，新兴的电子出租服务（例如Uber和Lyft）在过去几年中一直在重塑现有竞争。这项研究调查了纽约市主流呼唤服务（即黄色和绿色出租车）和电子出租服务（即Uber）之间的现有竞争。他们的竞争是根据市场细分，新兴需求和法规进行了调查的。使用数据可视化技术来找到旅行行为中的现有和新模式。在这项研究中，我们开发了一个多代理模型，并应用了加强学习技术来模仿驾驶员的行为。通过我们的数据可视化结果中识别的模式来验证该模型。然后，该模型用于评估多种新法规和竞争方案。我们的研究结果表明，e-bailers主导着低旅行密度区域（例如居民区），并且e-bailers迅速识别并对旅行需求的变化做出反应。这导致冰雹的市场规模减少。此外，我们的结果证实了绿色出租车法规对现有竞争的间接影响。这项调查以及我们提出的方案可以帮助政策制定者和当局制定可以有效解决需求的政策，同时确保对庆祝和电子高度驱动部门的健康竞争。 关键字：出租车； Uber，政策； e-hailing;多代理模拟；强化学习；
The taxi business has been overly regulated for many decades. Regulations are supposed to ensure safety and fairness within a controlled competitive environment. By influencing both drivers and riders choices and behaviors, emerging e-hailing services (e.g., Uber and Lyft) have been reshaping the existing competition in the last few years. This study investigates the existing competition between the mainstream hailing services (i.e., Yellow and Green Cabs) and e-hailing services (i.e., Uber) in New York City. Their competition is investigated in terms of market segmentation, emerging demands, and regulations. Data visualization techniques are employed to find existing and new patterns in travel behavior. For this study, we developed a multi-agent model and applied reinforcement learning techniques to imitate drivers behaviors. The model is verified by the patterns recognized in our data visualization results. The model is then used to evaluate multiple new regulations and competition scenarios. Results of our study illustrate that e-hailers dominate low-travel-density areas (e.g., residential areas), and that e-hailers quickly identify and respond to change in travel demand. This leads to diminishing market size for hailers. Furthermore, our results confirm the indirect impact of Green Cabs regulations on the existing competition. This investigation, along with our proposed scenarios, can aid policymakers and authorities in designing policies that could effectively address demand while assuring a healthy competition for the hailing and e-haling sectors. Keywords: taxi; Uber, policy; E-hailing; multi-agent simulation; reinforcement learning;
