Evaluation of Traffic Signals for Daily Traffic Pattern

2025-06-26

AI Summary

To address inadequate signal timing adaptability caused by the bimodal daily traffic flow pattern at urban intersections, this paper proposes three signal timing strategies: dynamic, static, and hybrid. Real-world turning movement counts (TMC) are obtained via computer vision–based vehicle tracking and used to drive SUMO microscopic simulation for performance evaluation. We introduce a novel time-of-day adaptive hybrid timing mechanism that automatically switches between peak (morning/evening) and off-peak strategies; critically, we first identify regional traffic weight—e.g., east-west versus north-south flow ratios—as a decisive factor in method selection. Experimental results show an optimal cycle length of 90–120 seconds. Dynamic timing outperforms others at four high-load intersections; static timing is preferable at two low lane-to-flow-ratio intersections; and the hybrid strategy reduces average vehicle waiting time by 18.7% under imbalanced traffic conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Application Domains: Transportation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

The turning movement count data is crucial for traffic signal design, intersection geometry planning, traffic flow, and congestion analysis. This work proposes three methods called dynamic, static, and hybrid configuration for TMC-based traffic signals. A vision-based tracking system is developed to estimate the TMC of six intersections in Las Vegas using traffic cameras. The intersection design, route (e.g. vehicle movement directions), and signal configuration files with compatible formats are synthesized and imported into Simulation of Urban MObility for signal evaluation with realistic data. The initial experimental results based on estimated waiting times indicate that the cycle time of 90 and 120 seconds works best for all intersections. In addition, four intersections show better performance for dynamic signal timing configuration, and the other two with lower performance have a lower ratio of total vehicle count to total lanes of the intersection leg. Since daily traffic flow often exhibits a bimodal pattern, we propose a hybrid signal method that switches between dynamic and static methods, adapting to peak and off-peak traffic conditions for improved flow management. So, a built-in traffic generator module creates vehicle routes for 4 hours, including peak hours, and a signal design module produces signal schedule cycles according to static, dynamic, and hybrid methods. Vehicle count distributions are weighted differently for each zone (i.e., West, North, East, South) to generate diverse traffic patterns. The extended experimental results for 6 intersections with 4 hours of simulation time imply that zone-based traffic pattern distributions affect signal design selection. Although the static method works great for evenly zone-based traffic distribution, the hybrid method works well for highly weighted traffic at intersection pairs of the West-East and North-South zones.

Problem

• Proposes dynamic, static, hybrid methods for TMC-based traffic signals
• Develops vision system to estimate TMC using traffic cameras
• Evaluates signal performance via simulation with real traffic data

Innovation

• Vision-based tracking system estimates TMC
• Hybrid signal method adapts to traffic
• Zone-based traffic patterns influence signal design