Modelling vehicle and pedestrian collective dynamics: Challenges and advances

TL;DR

This paper reviews the challenges in modeling vehicle and pedestrian collective dynamics, highlighting limitations of classical models and discussing recent advances in long-term anticipation and multiscale approaches.

Contribution

It provides a comprehensive review of classical and modern modeling approaches for collective traffic and pedestrian behaviors, emphasizing the need for anticipation and multiscale methods.

Findings

Classical force-based models have limitations in capturing complex behaviors.

Long-term anticipation mechanisms improve modeling accuracy.

Multiscale modeling approaches are necessary for better predictions.

Abstract

In our urbanised societies, the management and regulation of traffic and pedestrian flows is of considerable interest for public safety, economic development, and the conservation of the environment. However, modelling and controlling the collective dynamics of vehicles and pedestrians raises several challenges. Not only are the individual entities self-propelled and hard to describe, but their complex nonlinear physical and social interactions makes the multi-agent problem of crowd and traffic flow even more involved. In this chapter, we purport to review the suitability and limitations of classical modelling approaches through four examples of collective behaviour: stop-and-go waves in traffic flow, lane formation, long-term avoidance behaviour, and load balancing in pedestrian dynamics. While stop-and-go dynamics and lane formation can both be addressed by basic reactive models (at least to some extent), the latter two require anticipation and/or coordination at the level of the group. The results highlight the limitations of classical force-based models, but also the need for long-term anticipation mechanisms and multiscale modelling approaches. In response, we review new developments and modelling concepts.