The objective of this study is twofold: (a) to compare and interpret vehicle speed patterns derived from telematics and video-based data, and (b) to explore how these datasets can jointly support the identification of high-risk driving behaviors. Ultimately, this work contributes to the design of proactive safety indicators that go beyond traditional crash-based approaches. These methods allow for a more holistic understanding of how drivers interact with their surroundings and how risky situations emerge in real-world traffic.
| ID | pc635 |
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