Self-Driven Particle Model for Mixed Traffic and Other Disordered Flows

TL;DR

This paper introduces a multi-particle model for mixed traffic in developing countries, capturing the disordered, lane-less flow of diverse vehicles and drivers, and extends it to autonomous vehicle control.

Contribution

It presents a novel generalized force-based multi-particle model that reproduces mixed traffic dynamics and integrates lane-changing and autonomous vehicle control.

Findings

Model reproduces observed mixed traffic characteristics

Incorporates lane-changing and cooperative behaviors

Can be used as a controller for autonomous vehicles

Abstract

Vehicles in developing countries have widely varying dimensions and speeds, and drivers tend to not follow lane discipline. In this flow state called "mixed traffic", the interactions between drivers and the resulting maneuvers resemble more that of general disordered self-driven particle systems than that of the orderly lane-based traffic flow of industrialized countries. We propose a general multi particle model for such self-driven "high-speed particles" and show that it reproduces the observed characteristics of mixed traffic. The main idea is to generalize a conventional acceleration-based car-following model to a two-dimensional force field. For in-line following, the model reverts to the underlying car-following model, for very slow speeds, it reverts to an anisotropic social-force model for pedestrians. With additional floor fields at the position of lane markings, the model reverts to an integrated car-following and lane-changing model with continuous lateral dynamics including cooperative aspects such as zip merging. With an adaptive cruise control (ACC) system as underlying car-following model, it becomes a controller for the acceleration and steering of autonomous vehicles in mixed or lane-based traffic.