Synergistic Traffic Assignment

TL;DR

This paper introduces synergistic traffic assignment (STA), a novel approach where road costs decrease with usage due to shared transit, enabling efficient equilibrium computation and potential resource savings in transportation systems.

Contribution

The paper presents a new synergistic traffic assignment model, demonstrating its game-theoretic properties, convergence behavior, and practical application in bus line planning.

Findings

STA converges rapidly to equilibrium in realistic scenarios.

Compared to traditional ATA, STA allows for more efficient computation.

STA can significantly reduce vehicle resources in transit planning.

Abstract

Traffic assignment analyzes traffic flows in road networks that emerge due to traveler interaction. Traditionally, travelers are assumed to use private cars, so road costs grow with the number of users due to congestion. However, in sustainable transit systems, travelers share vehicles s.t. more users on a road lead to higher sharing potential and reduced cost per user. Thus, we invert the usual avoidant traffic assignment (ATA) and instead consider synergistic traffic assignment (STA) where road costs decrease with use. We find that STA is significantly different from ATA from a game-theoretical point of view. We show that a simple iterative best-response method with simultaneous updates converges to an equilibrium state. This enables efficient computation of equilibria using optimized speedup techniques for shortest-path queries. In contrast, ATA requires slower sequential updates or more complicated iteration schemes that only approximate an equilibrium. Experiments with a realistic scenario for the city of Stuttgart indicate that STA indeed quickly converges to an equilibrium. We envision STA as a part of software-defined transportation systems that dynamically adapt to current travel demand. As a first demonstration, we show that an STA equilibrium can be used to incorporate traveler synergism in a simple bus line planning algorithm to potentially greatly reduce the required vehicle resources.