A discrete choice latent class method for capturing unobserved heterogeneity in cyclist crossing behaviour at crosswalks
Bibliographic record
Abstract
Conflicts between cyclists and motorized vehicles at crosswalks often lead to severe collisions. The varied behaviour of cyclists at these crossings introduces unobserved heterogeneity. Despite this, there is a notable research gap in studying the cyclist behaviour at roundabout crosswalks. To address this gap, we propose a discrete choice latent class method to capture the multi-level latent heterogeneity in cyclists' crossing behaviour at roundabout crosswalks. Latent heterogeneity can be captured at multiple levels: site-level, interaction-level, choice-attribute level, and individual-level. This method, rooted in behavioural theory, aims to provide a deeper understanding of cyclists' crossing decisions, enhancing safety measures at these intersections. We present an application of the proposed method to two publicly available drone datasets of naturalistic road user trajectories at roundabouts, including 8 roundabout sites that exhibit some level of similarity to minimize site heterogeneity. We capture the latent heterogeneity in the cyclists' membership to a distinct behavioural class at two levels using these datasets: the individual level, represented by the speed of the cyclist as they enter the crosswalk, and the interaction level, defined by the presence of vehicles approaching the cyclist. Our findings align with previous studies that emphasize the significance of the initial speed variable in influencing cyclists' subsequent behaviour and decisions. We identified two distinct classes of cyclists. We hypothesize that Class 1 cyclists, whom we refer to as passers, tend to bypass or overtake other road users at the crosswalk, especially in the absence of vehicles, prioritizing speed and efficiency. We also hypothesize that Class 2 cyclists, referred to as followers, exhibit more cautious behaviour, preferring to maintain a steady pace and avoid overtaking, particularly when vehicles are present. The proposed latent class model effectively captures this behavioural distinction, offering a more granular view of cyclists' decision-making processes at roundabout crosswalks. A key finding is that the discrete choice model with a latent class structure outperforms the basic model without it, despite having more degrees of freedom, as it achieves a lower BIC and AIC but improved model fit statistic. This demonstrates that latent heterogeneity can be effectively captured, leading to improved predictions and outperforming the basic non-latent class model. Classifying cyclists into distinct behavioural classes not only enhances cyclist safety at crosswalks but also provides valuable insights for the development of autonomous vehicle-cyclist interactions.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".