The Use of ITS for Improving Bus Priority at Traffic Signals

Morellato, Camilla; Sdun, Mathias · 2020 · OpenAlex-citations

DOI: 10.5278/ojs.td.v1i1.5504

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Summary

This paper addresses the challenge of reducing urban traffic congestion and pollution by encouraging a modal shift from private cars to public transport. The authors argue that increasing the attractiveness of bus services—specifically by improving travel time, speed, punctuality, and reliability—is essential for this shift. Since waiting at traffic signals constitutes a significant portion of bus travel time, the study focuses on the potential of Intelligent Transport Systems (ITS) to implement bus priority at traffic signals. The research aims to classify existing bus priority technologies, analyze their strengths and weaknesses, and provide a framework for evaluating their effectiveness. The study is based on a literature review and an analysis of national and international experiences with bus priority systems. The authors categorize these systems into three types: non-adaptive (passive measures like bus lanes and pre-determined green waves), detector-based (active priority using fixed roadside detectors such as inductive loops, infrared, or microwave sensors), and GPS-based (active priority using virtual detectors via GPS technology). The paper compares these technologies, noting that while detector-based systems offer active priority, they require high-cost physical infrastructure and are inflexible. In contrast, GPS-based systems reduce construction and maintenance costs, offer high flexibility, and enable differential priority (prioritizing only delayed buses) and integration with real-time passenger information systems, though they face challenges regarding positioning accuracy. To evaluate these systems, the authors propose a framework using Key Performance Indicators (KPIs) that balance benefits for bus service against disruptions to general traffic. Bus-related KPIs include travel time savings, delay reduction, headway regularity, operational savings (vehicles needed), environmental impact (fuel, emissions, noise), and user satisfaction. General traffic KPIs include travel time delays, queue lengths, and emissions for other vehicles. The paper recommends simulation models (e.g., SIMBOL, HUTSIM, VISSIM) for evaluating future implementations and on-site measurements for monitoring existing systems. International case studies indicate that bus travel time savings of up to 20% can be achieved. Simulation results from London highlight that detector distance significantly impacts delay savings, while GPS error has a minimal effect. Helsinki simulations show significant tram delay savings with negligible disruption to other traffic unless traffic volumes are high. The significance of this work lies in its comprehensive classification of ITS technologies for bus priority and its standardized evaluation framework. The authors conclude that GPS-based systems offer the most advantageous balance of cost, flexibility, and effectiveness, despite technical complexities. Future research directions identified include establishing interoperability standards for cross-regional bus operations, developing advanced strategies for differential priority to maximize regularity, and improving synchronization methods for coordinated traffic signals to prevent disruption to green waves. These findings support the use of ITS as a critical tool for optimizing existing infrastructure and enhancing public transport efficiency.

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