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

TL;DR

This paper introduces a novel traffic simulation tool that uses real-world turning movement count data from Toronto to accurately model and evaluate intersection traffic flows in SUMO, aiding urban traffic planning.

Contribution

It is the first to integrate Toronto TMC data into SUMO with an easy GUI, enabling realistic, data-driven intersection traffic simulations for planning and analysis.

Findings

Simulation closely matches real intersection flows

Real data-driven simulations can replace synthetic data

Supports evaluation of traffic signal strategies

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.