Navigating pollution: A multimodal approach to traffic and exposure management

TL;DR

This study develops a dynamic traffic management model that considers exposure and emissions, revealing that pollution levels influence the effectiveness of traffic policies and highlighting the importance of context-specific measures.

Contribution

It introduces a novel day-to-day assignment model incorporating exposure costs and an emission-exposure algorithm, providing insights into traffic policy impacts under varying pollution scenarios.

Findings

Vehicle restrictions and fare reductions lower emissions in light pollution.

Heavy pollution scenarios see increased exposure due to modal shifts.

Timely pollution information can promote healthier travel choices.

Abstract

Few studies quantify how traffic management dynamically reshapes modal split and emission-exposure outcomes over pollution severities. This paper proposes a novel day-to-day assignment model integrating exposure cost, which includes exposure perception and emissions-dispersion-exposure algorithm. Numerical experiments reveal that and various levels of traffic-related measures have an air pollution scenario-dependent effect on the MT system. In light pollution scenarios, vehicle restrictions and reduced fares for buses or ridesharing help lower car usage and reduce emissions and exposure. However, under heavy pollution, higher-level restrictions and ridesharing fares paradoxically increase travelers' exposure by 18% and 6.3%, respectively, due to modal shift. Furthermore, timely pollution information updates could plausibly encourage healthier travel. This paper also proposes practical strategies for both routine and emergency traffic management, considering the trade-offs among travel cost, emission, and exposure, and emphasizes the need for measures tailored to different air pollution contexts to offer deeper insights for urban traffic policies.