Classification and Unification of the Microscopic Deterministic Traffic Models with Identical Drivers

TL;DR

This paper unifies various microscopic deterministic traffic models with identical drivers into a single master framework, enabling comparison and analysis of different models and their implications for human and autonomous driving behaviors.

Contribution

It introduces a unifying master model that encompasses existing two-phase and three-phase traffic models, including the IDM and generalized OV models, facilitating systematic comparison.

Findings

All existing models are special cases of the master model.

Three-phase models show vanishing expansion orders in certain densities.

IDM is equivalent to a generalized OV model.

Abstract

We show that all existing deterministic microscopic traffic models with identical drivers (including both two-phase and three-phase models) can be understood as special cases from a master model by expansion around well-defined ground states. This allows two traffic models to be compared in a well-defined way. The three-phase models are characterized by the vanishing of leading orders of expansion within a certain density range, and as an example the popular intelligent driver models (IDM) is shown to be equivalent to a generalized optimal velocity (OV) model. We also explore the diverse solutions of the generalized OV model that can be important both for understanding human driving behaviors and algorithms for autonomous driverless vehicles.