On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces

TL;DR

This paper develops a systematic approach to create a generalizable motion model for diverse road users in shared traffic spaces, enhancing realism and adaptability across different environments.

Contribution

It introduces a novel process for formulating general motion models and extends the Game-Theoretic Social Force Model to better simulate varied pedestrian and vehicle behaviors.

Findings

Model can realistically simulate diverse motion behaviors.

Adding pedestrian motion patterns improves model performance.

Model validated on three shared space datasets.

Abstract

Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport modes, and the dynamic nature of mixed-traffic zones like shared spaces make realistic modeling of such environments challenging. This paper focuses on the generalizability of the motion model, i.e., its ability to generate realistic behavior in different environmental settings, an aspect which is lacking in existing works. Specifically, our first contribution is a novel and systematic process of formulating general motion models and application of this process is to extend our Game-Theoretic Social Force Model (GSFM) towards a general model for generating a large variety of motion behaviors of pedestrians and cars from different shared spaces. Our second contribution is to consider different motion patterns of pedestrians by calibrating motion-related features of individual pedestrian and clustering them into groups. We analyze two clustering approaches. The calibration and evaluation of our model are performed on three different shared space data sets. The results indicate that our model can realistically simulate a wide range of motion behaviors and interaction scenarios, and that adding different motion patterns of pedestrians into our model improves its performance.