Self-organizing urban transportation systems

TL;DR

This paper reviews how self-organizing principles enable adaptive urban transportation systems, improving traffic flow and stability by using local rules for global optimization in dynamic environments.

Contribution

It provides a comprehensive review of recent and future work on self-organizing transportation systems, highlighting their advantages over traditional optimization methods.

Findings

Self-organizing traffic lights significantly improve traffic flow.

Simple local rules can prevent headway instability in public transit.

Self-organizing methods are applicable to various urban transportation systems.

Abstract

Urban transportation is a complex phenomenon. Since many agents are constantly interacting in parallel, it is difficult to predict the future state of a transportation system. Because of this, optimization techniques tend to give obsolete solutions, as the problem changes before it can be optimized. An alternative lies in seeking adaptive solutions. This adaptation can be achieved with self-organization. In a self-organizing transportation system, the elements of the system follow local rules to achieve a global solution. Like this, when the problem changes the system can adapt by itself to the new configuration. In this chapter, I will review recent, current, and future work on self-organizing transportation systems. Self-organizing traffic lights have proven to improve traffic flow considerably over traditional methods. In public transportation systems, simple rules are being explored to prevent the "equal headway instability" phenomenon. The methods we have used can be also applied to other urban transportation systems and their generality is discussed.