The intelligent agent model -- a fully two-dimensional microscopic traffic flow model

TL;DR

This paper introduces a versatile two-dimensional microscopic traffic flow model that generalizes previous models to include various human-driven traffic types, demonstrating accident-free flow and self-organization in diverse environments.

Contribution

The paper presents a novel intelligent-agent model that unifies lane-based, lane-free, bicycle, and pedestrian traffic modeling using car-following principles.

Findings

Produces accident-free traffic flow

Reproduces observed self-organization phenomena

Applicable to various traffic environments

Abstract

Recently, a fully two-dimensional microscopic traffic flow model for lane-free vehicular traffic flow has been proposed [Physica A, 509, pp. 1-11 (2018)]. In this contribution, we generalize this model to describe any kind of human-driven directed flow including lane-based vehicular flow, lane-free mixed traffic, bicycle traffic, and pedestrian flow. The proposed intelligent-agent model (IAM) has the same philosophy as the well-known social-force model (SFM) for pedestrians but the interaction and boundary forces are based on car-following models making this model suitable for higher speeds. Depending on the underlying car-following model, the IAM includes anticipation, response to relative velocities, and accident-free driving. When adding a suitable floor field, the IAM reverts to an integrated car-following and lane-changing model with continuous lane changes. We simulate this model in several lane-based and lane-free environments in various geometries with and without obstacles. We observe that the model produces accident-free traffic flow reproducing the observed self-organisation phenomena.