Temporal Margins and Behavioral Features for Early Risk Assessment in Left-Turn Vehicle and Bicycle Conflicts at Signalized Intersections
Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a foundation for determining appropriate timing of future in-vehicle early warning systems. Trajectories reconstructed from seven hours of camera footage yielded six spatio-temporal and behavioral indicators for 37 events with a post-encroachment time (PET) ≤ 3 s. Indicators—PET, time-to-crossing (TTC), right-of-way, urgent braking, deceleration to avoid a crash, and Kalman-based trajectory variance—were statistically related to a composite risk index, R. Approximately 80% of events fell within PETs of 2–3 s, while urgent braking occurred in 50% of cases with PETs of ≤2 s. Each 1 s reduction in PET increased R by 0.18 (R2 = 0.55). PETs ≤ 2.5 s or TTCs ≤ 1.5 s flagged 95% of high-risk events 0.5 s in advance. Joint thresholds involving urgent braking and high variance raised coverage to 100%, with lead times of 0–1.4 s and a false alarm rate of 8%. These findings provide an innovative multi-indicator framework based on real-world trajectories, offering quantitative scenario-specific thresholds for effective in-vehicle warnings at urban intersections.
Authors
- Manabu Hashimoto (ORCID: https://orcid.org/0000-0001-9909-3531)
- Toshiya Hirose (ORCID: https://orcid.org/0000-0002-3642-971X)
- Shuncong Shen
- Yasuhiro Matsui (ORCID: https://orcid.org/0000-0003-1221-4252)
- Shoko Oikawa
Institutions
- Shibaura Institute of Technology (JP)
- National Traffic Safety and Environment Laboratory (JP)
Publication Details
- Journal
- Machines
- Published
- 2025-08-10
- DOI
- https://doi.org/10.3390/machines13080709
- Primary Topic
- Traffic and Road Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00