Microscopic agent-based modeling and simulation of cyclists on off-street paths
Bibliographic record
Abstract
Inclusion of bicycle traffic in microsimulation tools is essential for evaluating bicycle-accessible infrastructure projects. However, the representation of bicycles in microsimulation models is still at an early stage of development. A better understanding of cyclist behaviour during various interactions is needed to enhance bicycle microsimulation models, which is a pre-requisite for accurate microscopic modeling of bicycle traffic operations. Due to the limited availability of detailed data, the inherent complexity of cyclist decision-making, and the substantial heterogeneity in cycling behaviour, modeling cyclist operation behaviour requires novel methods and techniques. This thesis aims first to characterize cyclist maneuvers in following and overtaking interactions using multivariate finite mixture model-based clustering. Second, an agent-based bicycle simulation method is proposed to model cyclists as intelligent agents making operational and tactical decisions based on their observations of the operating environment. Cyclist position data associated with time stamps are used to infer state and future decisions. The data are extracted from videos collected in Vancouver, BC, Canada using computer vision techniques. For segmenting behavioural states, observations of cyclists in following interactions are clustered into constrained and unconstrained states. Observations of overtaking cyclists are clustered into initiation, merging and post-overtaking states. Generative adversarial imitation learning (GAIL) is used to infer the uncertain intentions and preferences of cyclists from observational data. The model is validated by comparing multivariate distributions of variables such as speed, direction, and spacing of observed and simulated cyclist trajectories. The model performs well in comparison to two other cyclist simulation models from the literature. The proposed approach to miscrosimulation is a significant advancement in agent-based modeling methods, with continuous, non-linear, and stochastic representation of states, decisions, and actions. By modeling cyclist heterogeneity, the proposed approach can enhance applications in bicycle facility planning and design, safety modeling, and energy modeling with consideration of the full diversity of cyclists. Such an advancement is necessary for developing bicycle networks for all ages and abilities of riders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".