The Accuracy of Cell-based Dynamic Traffic Assignment: Impact of Signal Control on System Optimality

TL;DR

This paper introduces a new signal control model for dynamic traffic assignment that balances accuracy and computational efficiency by using a flexible cycle length, improving realism and resilience under extreme traffic conditions.

Contribution

The paper proposes the SCRC model, a novel continuous linear signal control approach that overcomes previous trade-offs, enhancing accuracy and computational speed in DTA frameworks.

Findings

SCRC achieves accuracy comparable to existing models.

SCRC is more resilient under extreme traffic conditions.

The approach significantly reduces computational complexity.

Abstract

Dynamic Traffic Assignment (DTA) provides an approach to determine the optimal path and/or departure time based on the transportation network characteristics and user behavior (e.g., selfish or social). In the literature, most of the contributions study DTA problems without including traffic signal control in the framework. The few contributions that report signal control models are either mixed-integer or nonlinear formulations and computationally intractable. The only continuous linear signal control method presented in the literature is the Cycle-length Same as Discrete Time-interval (CSDT) control scheme. This model entails a trade-off between cycle-length and cell-length. Furthermore, this approach compromises accuracy and usability of the solutions. In this study, we propose a novel signal control model namely, Signal Control with Realistic Cycle length (SCRC) which overcomes the trade-off between cycle-length and cell-length and strikes a balance between complexity and accuracy. The underlying idea of this model is to use a different time scale for the cycle-length. This time scale can be set to any multiple of the time slot of the Dynamic Network Loading (DNL) model (e.g. CTM, TTM, and LTM) and enables us to set realistic lengths for the signal control cycles. Results show, the SCRC model not only attains accuracy comparable to the CSDT model but also more resilient against extreme traffic conditions. Furthermore, the presented approach substantially reduces computational complexity and can attain solution faster.