Can motorcyclist behavior in traffic conflicts be modeled? A deep reinforcement learning approach for motorcycle-pedestrian interactions
Bibliographic record
Abstract
Traffic simulation models have been utilized recently in safety evaluations by calculating traffic conflict indicators from simulated road user trajectories. However, existing simulation models (1) do not accurately capture road users’ behaviour and evasive actions, and (2) do not consider road users’ intelligence and rationality. This study proposes a safety-oriented microsimulation framework for modeling conflict interactions between motorcyclists and pedestrians in mixed traffic conditions. Motorcyclists are modeled as utility-based rational and intelligent decision-makers using a Markov Decision Process (MDP). Continuous Inverse Reinforcement Learning (IRL) is proposed to recover the motorcyclists’ reward function using their actual trajectories. The recovered motorcyclists’ reward function provides inferences into their behavior in conflict interactions. The motorcyclists’ optimal policies (sequences of decisions) are estimated using the Actor-Critic Deep Reinforcement Learning. The results show that the model simulated motorcyclist trajectories and the evasive actions with high accuracy. Moreover, the predicted PETs correlated well with corresponding field-measured conflicts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".