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Modeling Online Taxi Drivers’ Decision Making in Accepting or Rejecting Ride Offers

Fatemipour, Elahe | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53013 (44)
  4. University: Sharif University of Technology
  5. Department: Economics and Management
  6. Advisor(s): Madanizadeh, Ali; Joshaghani, Hossein
  7. Abstract:
  8. Online ride-hailing platforms aim to match drivers and riders instantly by receiving ride requests from riders and sending them to drivers nearby. However, the low acceptance rate of offers by drivers leads to friction in the process of driver and rider matching. Various factors influence drivers' decisions to accept or reject offers. Specifically, drivers are more likely to turn down a ride offer when they know that by rejecting it, they can quickly receive another offer or find a passenger traditionally. To examine the factors influencing drivers' decisions in online ride-hailing markets, we focus on the behavior of drivers employed by Tapsi ride-hailing platform, and by specifying a discrete dynamic programming model, we evaluate how drivers decide whether to accept or reject ride offers. In this model, drivers compare the value of each ride offer with the value of available outside options and the value of waiting for better offers and decide whether to reject or accept the offer. The model parameters are obtained using the simulated method of moments (SMM) with the help of Tapsi data in the period of January to June 2018. According to the results, what leads to a low driver acceptance rate in Tapsi is the existence of a variety of outside options. Therefore, even hiding information or imposing fines on drivers who reject ride offers cannot significantly motivate drivers to accept more trips. In other conditions, price increases can boost the acceptance rate of trips. For example, with a 50 percent price increase, the average acceptance rate increases by about 40 percent
  9. Keywords:
  10. Labor Supply ; Matching ; Two-sided Market ; Online Ride-Hailing ; Labor Supply Elasticity ; Information Disclosure

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