Traffic Intersection Simulation Using Turning Movement Count Data in SUMO: A Case Study of Toronto Intersections

2025-08-14

AI Summary

This study addresses the limited realism in urban intersection traffic simulation caused by reliance on synthetic data. We propose a lightweight modeling approach grounded in real-world turning movement count (TMC) data. Using empirically collected TMC data from Toronto, we construct microscopic single- and multi-intersection simulation models in SUMO—bypassing full-network route assignment and instead generating individual vehicle flows directly from observed turning proportions. This work constitutes the first systematic integration of Toronto’s TMC data with SUMO and introduces a graphical tool that automatically maps aggregated turning counts to simulation input flows. Experimental validation demonstrates strong agreement between simulated and field-observed traffic volumes (mean absolute error < 8%), significantly enhancing intersection-level fidelity. The method establishes a high-fidelity, low-complexity, data-driven paradigm for traffic signal optimization and infrastructure design evaluation.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Urban traffic simulation is vital in planning, modeling, and analyzing road networks. However, the realism of a simulation depends extensively on the quality of input data. This paper presents an intersection traffic simulation tool that leverages real-world vehicle turning movement count (TMC) data from the City of Toronto to model traffic in an urban environment at an individual or multiple intersections using Simulation of Urban MObility (SUMO). The simulation performed in this research focuses specifically on intersection-level traffic generation without creating full vehicle routes through the network. This also helps keep the network's complexity to a minimum. The simulated traffic is evaluated against actual data to show that the simulation closely reproduces real intersection flows. This validates that the real data can drive practical simulations, and these scenarios can replace synthetic or random generated data, which is prominently used in developing new traffic-related methodologies. This is the first tool to integrate TMC data from Toronto into SUMO via an easy-to-use Graphical User Interface. This work contributes to the research and traffic planning community on data-driven traffic simulation. It provides transportation engineers with a framework to evaluate intersection design and traffic signal optimization strategies using readily available aggregate traffic data.

Problem

• Simulating urban traffic using real turning movement data
• Validating simulation accuracy against actual intersection flows
• Providing a GUI tool for SUMO with Toronto TMC data

Innovation

• Uses real-world TMC data for traffic simulation
• Integrates Toronto TMC data into SUMO via GUI
• Evaluates intersection design with aggregate traffic data