Microscopic Behavioral and Psychological Analysis of Road User Interactions in Shared Spaces
Bibliographic record
Abstract
The concept of shared space is proposed to improve the safety and health of vulnerable road users (VRUs) by promoting walking and cycling. However, despite the documented benefits of shared spaces, concerns were raised about the frequency and severity of road user interactions in shared spaces. Thus, the objective of this study is to investigate the microscopic behaviors and psychological characteristics of vulnerable road user interactions (i.e., pedestrian–e-bike interactions and pedestrian–cyclist interactions) in non-motorized shared spaces and their interplay mechanisms. We identify a total of 334 interactions in the same- and opposite-direction using the Dutch Objective Conflict Technique for Operation and Research (DOCTOR) method at four locations in Shenzhen city, China. Trajectories of road users involved in these interactions were extracted to identify key points in trajectories and interaction phases, considering both microscopic behaviors and psychological factors synthetically. The study also compared lateral and longitudinal decision distances, maneuvering distances, maneuvering time, and safety zones across different characteristics, including severity levels, road user types, genders, and whether road users carry large items or not. The results show that the main characteristic of the interaction’s starting and ending points changes in the lateral direction. Road users have a stronger sense of security in swerve-back phases. The average lateral psychological safety distance in shared spaces is about 1.125 m. Moreover, the average safety zone area for road users in opposite and same-direction interactions are 4.83 m2 and 9.36 m2, respectively. Road users carrying large items perceived a higher risk in shared spaces and required longer lateral psychological safety distances and larger safety zones. The findings of this study can be used to better design shared space facilities, considering the perceived risk of road users and their interactions and psychological behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".