Traffic Intersection Simulation Using Turning Movement Count Data in SUMO: A Case Study of Toronto Intersections
Bibliographic record
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 realworld 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".