Issues and Strategies Involved in Developing Agent-Based Multimodal Network Simulation Model for Transportation Planning: Lessons from a Case Study on the Troronto and Hamilton Area
Bibliographic record
Abstract
The paper presents the issues and strategies involved in developing an agent-based multimodal network simulation model for the Greater Toronto and Hamilton Area (GTHA). The model was developed by using a Java-based open source simulation platform: MATSim. The issues and strategies presented utilize a geocoded automobile network and General Transit Feed Specification (GTFS) data of multiple transit agencies within the study area. While network simulation model for automobile network is common, an integrated multimodal network that combines auto and transit network (physical network and daily transit schedules) has not been developed for large study area, such as the GTHA by anyone. A key challenge is to integrate the GTFS data seamlessly in the multimodal framework. The GTFS data allowed meshing the auto network and the transit network together, creating a fully functioning multimodal network. The main challenge associated with this task is the determination of network resolution. The auto network is at times at too low of a resolution relative to the transit network, while the transit network often contained too much detail to be relevant for traffic simulation for a region as large as the GTHA. The paper presents guidelines and example of resolving these issues and overcoming the challenges. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".