Comprehensive Data Analysis Approach for Appropriate Scheduling of Signal Timing Plans

• Published in: Future transportation 2022-06, Vol.2 (2), p.482-500
• Main Authors: Dobrota, Nemanja, Mitrovic, Nikola, Gavric, Slavica, Stevanovic, Aleksandar
• Format: Article
• Language: English
• Subjects: Big Data; Clustering; Data analysis; k-means clustering; medium-resolution data; Methods; Schedules; Scheduling; signal timing plans; Traffic control; Traffic flow; traffic profiles; visual analytics
• ISSN: 2673-7590; 2673-7590
• DOI: 10.3390/futuretransp2020027

Improperly scheduled signal timing plans are one of the main reasons for reduced efficiency of traffic signals at coordinated urban arterials. Recently, most urban arterial roads are equipped with intelligent transportation systems devices capable of reporting the collected data on high temporal and spatial resolution, which gives us the opportunity to overcome traditional signal timing planning flaws. Previous studies have proposed methods for scheduling signal timing plans based on small quantities of data combined with various optimization approaches that ultimately require domain expert intervention to fine-tune proposed solutions. Consequently, the signal timing plans scheduling problem is still being addressed without a comprehensive approach. In this study, we propose a novel data-driven procedure based on visual analytics principles to identify the dominant traffic profiles and appropriate scheduling of signal timing plans. The medium-resolution volume data collected over a one-year period on a real-world corridor consisting of 12 signalized intersections were used to validate the proposed methodology. Applied principles from the visual analytics domain allow for a better understanding of traffic characteristics and ultimately alleviate the development of appropriate signal timing schedules. The results show that the proposed method more reliably schedules signal timing plans when compared to current practice.