Balancing Fairness and Efficiency in Transport Network Design through Reinforcement Learning

Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos

Abstract: Designing well-functioning and fair transport networks is not a trivial task, given the large space of solutions and constraints one must satisfy. Moreover, different spatial segregation sources can render some transportation network interventions unfair to specific groups. It is thereby crucial to optimize the transportation system while mitigating the disproportional benefits it can lead to. In this paper, we explore the trade-off between efficiency and fairness in the Transport Network Design Problem (TNDP), via the use of Deep Reinforcement Learning (Deep RL). We formulate different fairness definitions as reward functions - inspired by Equal Sharing of Benefits, Narrowing the Gap, and Rawl's justice theory. We apply our method to Amsterdam (The Netherlands) and Xi'an (China) and show that vanilla Deep RL can lead to biased outcomes. By considering different fair rewards, however, we can shed light on possible compromises between fairness and efficiency in the TNDP.