Decongestion of urban areas with hotspot-pricing

TL;DR

This paper proposes a hotspot-pricing scheme that dynamically taxes vehicles at congested junctions, using real-time data and elasticity estimates, to effectively reduce urban traffic congestion and improve air quality.

Contribution

It introduces a novel congestion pricing method based on local congestion estimation and user response, demonstrating its effectiveness with real traffic data.

Findings

Hotspot pricing outperforms current congestion control mechanisms.

The scheme effectively reduces traffic congestion in simulated urban networks.

Results suggest potential for sustainable urban mobility improvements.

Abstract

The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion which in turn affects air pollution. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable to ameliorate and control congestion. Here, we analyze a local congestion pricing scheme, hotspot pricing, that surcharges vehicles traversing congested junctions. The proposed tax is computed from the estimation of the evolution of congestion at local level, and the expected response of users to the tax (elasticity). Results on cities' road networks, considering real-traffic data, show that the proposed hotspot pricing scheme would be more effective than current mechanisms to decongest urban areas, and paves the way towards sustainable congestion in urban areas.