From Sensing to Decision: A Generic Architecture for Freight Signal Priority Systems

TL;DR

This paper reviews Freight Signal Priority systems, proposing a generic architecture that links sensing modalities and uncertainties to decision reliability, aiming to improve robustness and scalability.

Contribution

It introduces a two-layer architecture for FSP systems, systematically compares sensing modalities, and highlights challenges in sensing reliability affecting decision-making.

Findings

Compared sensing modalities across key performance metrics.

Analyzed how sensing uncertainties impact ETA and priority decisions.

Identified research gaps in reliability-aware sensing-to-decision design.

Abstract

Freight Signal Priority (FSP) systems have emerged as a promising strategy to enhance freight mobility and reduce corridor delays in urban networks. While extensive research has focused on priority control algorithms and operational performance evaluation, comparatively limited attention has been devoted to the architectural design of sensing processes that shape reliable priority decisions. In practice, uncertainties in vehicle detection, communication, and estimated time of arrival (ETA) may propagate within the sensing-to-decision process, affecting priority timing and downstream signal performance. This paper presents a systematic review of FSP systems from a sensing-to-decision perspective. We propose a generic two-layer architecture consisting of a sensing-to-decision layer and a control execution layer. The sensing-to-decision layer transforms sensing inputs into priority decisions, while the control execution layer implements approved actions within traffic controllers. Within this architecture, we systematically compare major sensing modalities, including loop detectors, vision sensors, and V2I, across dimensions such as classification capability, state estimation accuracy, latency, and information richness. We further examine representative FSP systems to analyze how modality-specific characteristics and uncertainties influence ETA computation, priority triggering, and decision reliability. By linking sensing design to decision outcomes, this review identifies key deployment challenges and research gaps in reliability-aware sensing-to-decision design. Ultimately, this work provides a conceptual foundation for developing scalable and robust FSP systems that explicitly account for sensing imperfections rather than assuming idealized inputs.