Real-time Traffic Signal Control for Isolated Intersection, using Car-following Logic under Connected Vehicle Environment

Chandan, Kancharla Kamal Keerthi; Seco, Álvaro; Silva, Ana Bastos · 2017 · OpenAlex-citations

DOI: 10.1016/j.trpro.2017.05.207

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Summary

This paper addresses the challenge of optimizing traffic signal control at isolated intersections by leveraging Connected Vehicle (CV) technology. Traditional adaptive signal control systems rely on infrastructure-based sensors, such as inductive loop detectors, which provide only point-specific data and incur high installation and maintenance costs. In contrast, CV technology enables wireless communication between vehicles and infrastructure, providing continuous, detailed data on vehicle speed, position, and acceleration. The authors propose a Connected Vehicle Signal Control (CVSC) strategy designed to minimize travel time delays and the average number of stops per vehicle. The study assumes a 100% GPS market penetration rate, positing that future mandates and decreasing technology costs will make this scenario viable. The proposed CVSC algorithm is a rule-based method that operates on a one-second time step. It utilizes two primary metrics: "Flow-ratio" and "Speed-ratio." Flow-ratio is defined as the ratio of cumulative departure flow to cumulative arrival flow across all intersection approaches, measured from the start of the control period. To predict future vehicle positions and speeds for calculating these metrics, the algorithm employs the Wiedemann’74 car-following model, which classifies vehicle states (free driving, following, closing, emergency) based on headway and relative velocity. The control strategy proceeds in steps: first, it extends the green light to clear any queue formed during the red interval ("Zero-speed queue service time"). Second, it continues the green phase until the "Speed-ratio"—the ratio of actual to desired speeds of approaching vehicles—reaches 90%. Finally, it utilizes any remaining "Reserve-time" to optimize the Flow-ratio, aiming to keep it close to 1.0 to maximize throughput without causing congestion. The performance of the CVSC strategy was evaluated using the VISSIM 8 microscopic simulation tool under various traffic demand scenarios. The results were compared against an adaptive signal control solution developed by PTV EPICS, which relies on fixed detector data. The findings indicate that the CVSC strategy demonstrated outstanding performance, significantly reducing both travel time delays and the average number of stops per vehicle compared to the EPICS adaptive control. The algorithm proved responsive to fluctuations in arrival flows and different traffic demands. The significance of this research lies in demonstrating the potential of CV data to replace or augment traditional infrastructure-based detection systems for real-time traffic management. By utilizing continuous vehicle data and car-following logic, the proposed method offers a computationally effective and deployable solution that improves intersection efficiency. The study supports the transition toward intelligent transportation systems where vehicle-to-infrastructure communication enables more precise and responsive traffic signal timing, ultimately reducing congestion and improving overall network performance.

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• v2x connected vehicle