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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.18148 |
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| _version_ | 1866918216075313152 |
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| author | Liang, Shixiao Ma, Chengyuan Li, Pei Shi, Haotian Liu, Jiaxi Zhou, Hang Long, Keke Cao, Bofeng Szymkowski, Todd Li, Xiaopeng |
| author_facet | Liang, Shixiao Ma, Chengyuan Li, Pei Shi, Haotian Liu, Jiaxi Zhou, Hang Long, Keke Cao, Bofeng Szymkowski, Todd Li, Xiaopeng |
| contents | Real-time traffic crash detection is critical in intelligent transportation systems because traditional crash notifications often suffer delays and lack specific, lane-level location information, which can lead to safety risks and economic losses. This paper proposes a real-time, lane-level crash detection approach for freeways that only leverages sparse telematics trajectory data. In the offline stage, the historical trajectories are discretized into spatial cells using vector cross-product techniques, and then used to estimate a vehicle intention distribution and select an alert threshold by maximizing the F1-score based on official crash reports. In the online stage, incoming telematics records are mapped to these cells and scored for three modules: transition anomalies, speed deviations, and lateral maneuver risks, with scores accumulated into a cell-specific risk map. When any cell's risk exceeds the alert threshold, the system issues a prompt warning. Relying solely on telematics data, this real-time and low-cost solution is evaluated on a Wisconsin dataset and validated against official crash reports, achieving a 75% crash identification rate with accurate lane-level localization, an overall accuracy of 96%, an F1-score of 0.84, and a non-crash-to-crash misclassification rate of only 0.6%, while also detecting 13% of crashes more than 3 minutes before the recorded crash time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Lane-Level Crash Detection on Freeways Using Sparse Telematics Data Liang, Shixiao Ma, Chengyuan Li, Pei Shi, Haotian Liu, Jiaxi Zhou, Hang Long, Keke Cao, Bofeng Szymkowski, Todd Li, Xiaopeng Systems and Control Real-time traffic crash detection is critical in intelligent transportation systems because traditional crash notifications often suffer delays and lack specific, lane-level location information, which can lead to safety risks and economic losses. This paper proposes a real-time, lane-level crash detection approach for freeways that only leverages sparse telematics trajectory data. In the offline stage, the historical trajectories are discretized into spatial cells using vector cross-product techniques, and then used to estimate a vehicle intention distribution and select an alert threshold by maximizing the F1-score based on official crash reports. In the online stage, incoming telematics records are mapped to these cells and scored for three modules: transition anomalies, speed deviations, and lateral maneuver risks, with scores accumulated into a cell-specific risk map. When any cell's risk exceeds the alert threshold, the system issues a prompt warning. Relying solely on telematics data, this real-time and low-cost solution is evaluated on a Wisconsin dataset and validated against official crash reports, achieving a 75% crash identification rate with accurate lane-level localization, an overall accuracy of 96%, an F1-score of 0.84, and a non-crash-to-crash misclassification rate of only 0.6%, while also detecting 13% of crashes more than 3 minutes before the recorded crash time. |
| title | Real-Time Lane-Level Crash Detection on Freeways Using Sparse Telematics Data |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.18148 |