WORKING PRINCIPLE AND PERFORMANCE EVOLUTION OF CAMERA-BASED INTELLIGENT SIGNALIZED INTERSECTIONS: SAMSUN CITY, TÜRKIYE EXAMPLE

AYDIN, Metin Mutlu; POTOGLOU, Dimitris; CIPCIGAN, Liana · 2023 · Crossref

DOI: 10.20858/sjsutst.2023.121.1

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Summary

This study evaluates the working principles and performance impacts of camera-based Intelligent Signalized Intersections (ISIs) within the "Smart City Traffic Safety" project in Samsun, Türkiye. The research addresses the limitations of fixed-time traffic signals, which often fail to manage dynamic urban traffic flows effectively, leading to congestion and inefficiency. As cities face increasing vehicle volumes, the authors investigate how adaptive, real-time traffic control systems can reduce delays, improve safety, and lower emissions. The study focuses on the transition from traditional or non-signalized intersections to intelligent systems that utilize computer vision and artificial intelligence to dynamically adjust signal timing based on real-time traffic data. The methodology involves a before-and-after performance analysis of six pilot intersections selected from a larger initiative to transform 72 intersections in Samsun. The ISI system employs single fish-eye cameras mounted on high poles, an Image Processing Device (IPD) for vehicle detection and tracking, and an Intelligent Traffic Controller (ITC) that uses AI algorithms to regulate green light durations. The system classifies vehicles into four categories (passenger cars, minibuses, buses, and trucks) and monitors traffic parameters such as volume, speed, and queue length using virtual loops defined in specialized software. Data was collected from real-time recordings, allowing for a comparative assessment of traffic metrics prior to and following the implementation of the intelligent systems. The results demonstrate significant improvements in traffic efficiency and environmental impact. On average, the implementation of ISIs led to a 16% decrease in control delays and a 19% reduction in average vehicle speeds across the six examined intersections. Specific intersections showed varying degrees of improvement, with the highest reductions in delay and speed observed at the intersection with the highest average daily traffic volume. Additionally, the system contributed to substantial reductions in emissions, averaging a daily decrease of 1,232 grams of CO2 and 1,542 grams of PM10. The study also noted behavioral changes in drivers; initial impatience during the system's learning phase evolved into more observant and patient driving behaviors as the AI-regulated signal cycles stabilized. The findings underscore the potential of camera-based ISIs to enhance urban mobility and support smart city initiatives. By reducing traffic chaos, minimizing delays, and lowering pollutant emissions, these systems offer a viable solution for managing complex urban road networks. The authors conclude that the integration of such intelligent transportation systems not only improves operational efficiency but also contributes to public health and environmental sustainability. As the remaining intersections in the project are completed, the authors anticipate further city-wide benefits, including integrated traffic management and reduced complexity for citizens. This research provides empirical evidence supporting the adoption of adaptive traffic control technologies in developing smart urban infrastructures.

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• automated enforcement cameras