Can CAV Reduce Non-Recurrent Urban Road Congestion?

TL;DR

This paper explores how Connected Autonomous Vehicles (CAV) can mitigate non-recurrent urban road congestion caused by events like accidents, demonstrating potential benefits through preliminary simulation results.

Contribution

It introduces the idea that CAVs can help reduce non-recurrent congestion, extending their known benefits beyond recurrent traffic management.

Findings

CAVs can improve traffic flow during non-recurrent congestion

Preliminary simulation shows potential for CAVs to mitigate NRC effects

Further research needed on fuel, emissions, and safety impacts

Abstract

A well-designed resilient and sustainable urban transportation system can recover quickly from the non-recurrent road congestion (NRC), which is often caused by en-route events (e.g., road closure due to car collisions). Existing solutions, such as on-board navigation systems and temporary rerouting road signs, are not effective due to delayed responses. Connected Autonomous Vehicles (CAV) can be helpful in improving recurrent traffic as they can autonomously adjust their speed according to their real-time surrounding traffic, sensed by vehicular communications. Preliminary simulation results in this short paper show that CAV can also improve traffic when non-recurrent congestion occurs. Other results in fuel consumption, CO2 emission, and traditional traffic safety indicators are open for future discussions.