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Regulating Transportation Network Companies with a Mixture of Autonomous Vehicles and For-Hire Human Drivers

Di Ao, Jing Gao, Zhijie Lai, Sen Li

Abstract

This paper investigates the equity impacts of autonomous vehicles (AV) on for-hire human drivers and passengers in a ride-hailing market, and examines regulation policies that protect human drivers and improve transport equity for ride-hailing passengers. We consider a transportation network companies (TNC) that employs a mixture of AVs and human drivers to provide ride-hailing services. The TNC platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and TNC over the transportation network. The overall problem is formulated as a non-concave program, and an algorithm is developed to derive its approximate solution with a theoretical performance guarantee. Our study shows that TNC prioritizes AV deployment in higher-demand areas to make a higher profit. As AVs flood into these higher-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. To mitigate these concerns, we consider: (a) a minimum wage for human drivers; and (b) a restrictive pickup policy that prohibits AVs from picking up passengers in higher-demand areas. In the former case, we show that a minimum wage for human drivers will protect them from the negative impact of AVs with negligible impacts on passengers. However, there exists a threshold beyond which the minimum wage will trigger the platform to replace the majority of human drivers with AVs.

Regulating Transportation Network Companies with a Mixture of Autonomous Vehicles and For-Hire Human Drivers

Abstract

This paper investigates the equity impacts of autonomous vehicles (AV) on for-hire human drivers and passengers in a ride-hailing market, and examines regulation policies that protect human drivers and improve transport equity for ride-hailing passengers. We consider a transportation network companies (TNC) that employs a mixture of AVs and human drivers to provide ride-hailing services. The TNC platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and TNC over the transportation network. The overall problem is formulated as a non-concave program, and an algorithm is developed to derive its approximate solution with a theoretical performance guarantee. Our study shows that TNC prioritizes AV deployment in higher-demand areas to make a higher profit. As AVs flood into these higher-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. To mitigate these concerns, we consider: (a) a minimum wage for human drivers; and (b) a restrictive pickup policy that prohibits AVs from picking up passengers in higher-demand areas. In the former case, we show that a minimum wage for human drivers will protect them from the negative impact of AVs with negligible impacts on passengers. However, there exists a threshold beyond which the minimum wage will trigger the platform to replace the majority of human drivers with AVs.
Paper Structure (20 sections, 2 theorems, 34 equations, 34 figures, 1 table, 1 algorithm)

This paper contains 20 sections, 2 theorems, 34 equations, 34 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Management of TNC Platforms
  4. Management of Mixed Fleet
  5. Regulations on TNC Platforms
  6. Mathematical Model for the Ride-Hailing Network
  7. Problem Setup
  8. Incentives of Passengers
  9. Incentives of the Mixed Fleet
  10. Incentives of TNC Platform
  11. The Solution Algorithm
  12. Constraint relaxation and upper bound
  13. The solution algorithm
  14. Case Studies
  15. Evaluation of Transport Equity
  16. ...and 5 more sections

Key Result

Lemma 1

The profit-maximization problem of the TNC platform (optimalpricing_trip)-(profit_constraints) is non-concave.

Figures (34)

  • Figure 1: Postal codes and the corresponding zone number in San Francisco. Some zones are neglected in this research due to the negligible trip volume. (Figure courtesy: https://www.usmapguide.com/california/san-francisco-zip-code-map/)
  • Figure 2: Number of TNC vehicles under different values of AV cost
  • Figure 3: Comparison of TNC profit for the original and relaxed problem under varying AV cost
  • Figure 4: Number of idle AVs in each zone under varying cost of AV
  • Figure 5: Number of idle human drivers in each zone under varying cost of AV
  • ...and 29 more figures

Theorems & Definitions (9)

  • Remark 1
  • Remark 2
  • Remark 3
  • Lemma 1
  • proof
  • Remark 4
  • Remark 5
  • Proposition 1
  • Remark 6