System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability

TL;DR

This paper investigates a jam-absorption driving strategy for single-lane roads with a sag, analyzing how system size affects its efficiency in reducing travel time and fuel consumption using microscopic traffic models.

Contribution

It introduces a simple jam-absorption driving strategy for connected vehicles and examines its effectiveness across different system sizes, revealing optimal scales for reducing travel time and fuel use.

Findings

Travel time reduction rate slightly increases with system size.

Fuel consumption reduction rate decreases and stabilizes as system size grows.

Optimal scales for minimizing travel time and fuel consumption become constant.

Abstract

Sag is a road section where a downhill changes into an uphill, and is a highway bottleneck. We consider a system in which all vehicles are connected, and run on a single-lane road with a sag. We propose a simple strategy for removing each traffic jam caused by the sag. Our strategy assigns a vehicle upstream of the jam front to perform the jam-absorption driving (JAD): running toward the predicted goal, and finally removing the jam. We use a microscopic car-following model possessing the traffic instability, an acceleration model against the road gradient of a sag, and an instantaneous fuel consumption model. Our main goal is to elucidate the influence of the system size (the number of vehicles in the system) on our strategy. By increasing the system size from 500 to 10000 vehicles, we have found the following results for the average total travel time per vehicle, and the average total fuel consumption per vehicle. Our strategy can reduce the former with a slightly increasing rate of reduction. Our strategy can reduce the latter with a rate of reduction which decreases and becomes roughly constant. Optimal spatiotemporal scales of JAD for the former and the latter become roughly constant, respectively. Minimizing the former and the latter simultaneously is not possible. Not only vehicular traffic flow, but also the collective dynamics of other self-driven particles (such as ships, swarm robots, and pedestrians) lined up in a single column, and passing through a bottleneck can be modeled using our strategy.