A Hybrid Microscopic Model for Multimodal Traffic with Empirical Observations from Aerial Footage

TL;DR

This paper introduces a hybrid microscopic traffic model that captures the complex interactions of multimodal traffic, including motorcycles, based on empirical data from aerial footage, improving understanding of mixed traffic dynamics.

Contribution

The paper presents a novel hybrid microscopic traffic model inspired by pedestrian flow, incorporating empirical observations of multimodal traffic interactions, especially motorcycles.

Findings

Model reproduces observed traffic patterns from aerial footage

Captures mode-dependent lane discipline behaviors

Enhances understanding of mixed traffic dynamics

Abstract

Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an oversimplification and it would be more accurate to speak of hybrid situations, where lane discipline can be made mode-dependent. Empirical evidence shows that cars follow the lanes as defined by the infrastructure, while motorcycles do not necessarily adhere to predefined norms and may participate in self-organized formation of virtual lanes. This phenomenon is the result of complex interactions between different traffic participants competing for limited space. In order to better understand the dynamics of modal interaction microscopically, we first analyze empirical data from detailed trajectories obtained by the pNEUMA experiment and observe patterns of mixed traffic. Then, we propose a hybrid model for multimodal vehicular traffic. The hybrid model is inspired by the pedestrian flow literature, featuring collision-free and anticipatory properties, and we demonstrate that it is able to reproduce empirical observations from aerial footage.