Collective traffic of agents that remember

TL;DR

This paper introduces a social force model for agents with memory in traffic systems, revealing how memory influences individual movement and collective dynamics, including clogging behavior and efficiency.

Contribution

It presents a novel social force model incorporating memory effects, demonstrating their complex impact on traffic flow and clogging phenomena in agent collectives.

Findings

Memory improves individual movement towards desired states.

Memory can increase clogging in narrow exits.

Large memory reduces clogging and enhances flow efficiency.

Abstract

Traffic and pedestrian systems consist of human collectives where agents are intelligent and capable of processing available information, to perform tactical manoeuvres that can potentially increase their movement efficiency. In this study, we introduce a social force model for agents that possess memory. Information of the agent's past affects the agent's instantaneous movement in order to swiftly take the agent towards its desired state. We show how the presence of memory is akin to an agent performing a proportional-integral control to achieve its desired state. The longer the agent remembers and the more impact the memory has on its motion, better is the movement of an isolated agent in terms of achieving its desired state. However, when in a collective, the interactions between the agents lead to non-monotonic effect of memory on the traffic dynamics. A group of agents with memory exiting through a narrow door exhibit more clogging with memory than without it. We also show that a very large amount of memory results in variation in the memory force experienced by agents in the system at any time, which reduces the propensity to form clogs and leads to efficient movement.