Improving Urban Mobility by Understanding its Complexity

TL;DR

This paper advocates for a complex systems approach to urban mobility, emphasizing adaptive, real-time, and agent-based strategies to improve system resilience and efficiency.

Contribution

It introduces five novel recommendations rooted in complex systems science to enhance urban mobility management.

Findings

Adaptive strategies outperform prediction-based methods.

Regulating interactions reduces system friction.

Real-time sensors enable better decision-making.

Abstract

Urban mobility systems are composed multiple elements with strong interactions, i.e. their future is co-determined by the state of other elements. Thus, studying components in isolation, i.e. using a reductionist approach, is inappropriate. I propose five recommendations to improve urban mobility based on insights from the scientific study of complex systems: use adaptation over prediction, regulate interactions to avoid friction, use sensors to recover real time information, develop adaptive algorithms to exploit that information, and deploy agents to act on the urban environment.