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In this study, we compared the injury severity of occupants according to the seating position and the crashing direction in motor vehicle accidents. In the driver's point of view, it was separated the seating position as "Near-side" and "Far-side". The study subjects were targeted by people who visited 4 regional emergency centers following motor vehicle accidents. Real-world investigation was performed by direct and indirect methods after patient- consent. The information of the damaged vehicle was informed by Collision Deformation Classification (CDC) code and the information of the injury of patients was informed by using the Abbreviated Injury Score (AIS) and Injury Severity Score (ISS). When the column 3 in CDC code was P, damaged at the middle part of lateral side, the average point of AIS 3 was 1.91-±1.72 in near-side and 1.02-±1.31 in far-side (p<0.01). The average point of maximum AIS (MAIS) was 2.78-±1.39 in near-side and 2.02-±1.11 in far-side (p<0.01). The average point of ISS was 15.74-±14.71 in near-side and 8.11-±8.39 in far-side (p<0.01). Also, when the column 3 in CDC code was D, damaged at the whole part of lateral side, it was significant that the average point of AIS 3 and MAIS in near-side was bigger than in far-side (p=0.02).
Cyclists are more likely to be injured in fatal crashes than motorised vehicles. To gain detailed and precise behavioural data of road users, i.e. trajectories, a measuring campaign was conducted. Therefore, a black-spot for accidents with cyclists in Berlin, Germany was selected. The traffic has been detected by a fully automated traffic video analysis system continuously for twelve hours. The video surveillance system is capable of automatically extracting trajectories, classifying road user types and precise determining and positioning of conflicts and accidents. Additionally, pre-conflict and pre-accident situations could be analysed to provide further in-depth understanding of accident causation. The evaluation of the measuring campaign comprised the investigation of traffic parameters, e.g. traffic flow, as well as traffic-safety related parameters based on Surrogate Safety Measures (SSM). Furthermore, the spatial and temporal distributions of conflicts involving cyclists were determined. As a result, three possible conflict clusters could be identified, of which one cluster could be confirmed by detailed video analysis, showing conflicts caused by right turning vehicles.