Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic

TL;DR

This paper proposes a data-driven framework using financial incentives to optimize taxi fares, balancing infection risk and congestion, thereby promoting safe and efficient transportation during and after the COVID-19 pandemic.

Contribution

It introduces a novel network optimization model combined with a data-driven approach to learn user preferences for fare setting to reduce congestion and infection risk.

Findings

Framework effectively minimizes congestion.

Framework reduces infection risk.

User preferences are accurately captured and utilized.

Abstract

The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a re-opening stage, some effects of the pandemic on people's behaviors are expected to last much longer, including how they choose between different transport options. Experts predict considerably delayed recovery of the public transport options, as people try to avoid crowded places. In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway. In this paper, we propose to use financial incentives to set the tradeoff between risk of infection and congestion to achieve safe and efficient transportation networks. To this end, we formulate a network optimization problem to optimize taxi fares. For our framework to be useful in various cities and times of the day without much designer effort, we also propose a data-driven approach to learn human preferences about transport options, which is then used in our taxi fare optimization. Our user studies and simulation experiments show our framework is able to minimize congestion and risk of infection.