DETERMINING TRAVEL DELAY OF VEHICLES QUEUE AT A TRAFFIC SIGNAL

Cahyono, Setiyo Daru; Tristono, Tomi; Aji, Seno; Utomo, Pradityo · 2020 · DOAJ

DOI: 10.30598/barekengvol14iss3pp321-332

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Summary

This study addresses the limitations of traditional mathematical models in estimating vehicle travel delay at signalized intersections. Conventional approaches often assume a uniform arrival pattern or Poisson distribution, treating traffic flow as a continuous function. However, the authors argue that real-world vehicle arrivals are stochastic and better represented by a normal distribution pattern, which can manifest as randomized arrivals, group/platoon arrivals, or mixed arrivals. The research aims to develop a precise algorithm for determining travel delay by modeling these arrivals as discrete functions rather than continuous flows, thereby improving the accuracy of Level of Service (LoS) assessments. The methodology employs a numerical simulation using a discrete-time approach with three-second intervals. The model incorporates a normal distribution for vehicle arrivals, validated against empirical data showing a 90% significance in normality tests. The simulation assumes a multi-channel, single-phase queue system corresponding to road lanes, operating under a First-Come-First-Serve discipline. Travel delay is defined as the difference between free-flow travel time and actual blocked travel time, calculated using specific formulas that account for arrival times, departure times, and stop durations. The study tested three distinct scenarios: randomized arrivals, platoon arrivals occurring at the start of the red signal, and platoon arrivals occurring just before the green signal. Parameters included a traffic volume of 2,500 Passenger Car Units (PCUs) per hour and a 63-second traffic light cycle. The results demonstrate that the timing of platoon arrivals significantly impacts travel delay and intersection performance. When platoons arrived at the beginning of the red signal (simulating uncoordinated adjacent intersections), the average delay was 29.8 seconds per PCU, resulting in a Level of Service D, indicating unstable flow. In contrast, when platoons arrived at the end of the red signal (simulating coordinated signals), the average delay dropped to 3.1 seconds per PCU, achieving Level of Service A, which represents free-flow conditions. Randomized arrivals yielded an intermediate average delay of 18.9 seconds per PCU, corresponding to Level of Service C. The study concludes that the discrete function model provides a more precise estimation of delay than traditional uniform pattern assumptions. The significance of this research lies in its validation of normal distribution patterns for vehicle arrivals and the critical importance of traffic signal coordination. The findings suggest that uncoordinated signals lead to high delays and discomfort due to frequent stops, while coordinated signals minimize delay. Furthermore, the authors propose a future IoT-based system where vehicles equipped with sensors communicate with roadside units to record precise individual delay data. This technology could enable real-time monitoring of intersection performance, fuel consumption, and travel efficiency, supporting smarter urban traffic management and adaptive control systems.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

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• traffic density