  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;
