This work analyzes the relationship between crash frequency N (crashes per hour) and exposure Q (cars per hour) on the macroscopic level of a whole city. As exposure, the traffic flow is used here. Therefore, it analyzes a large crash database of the city of Berlin, Germany, together with a novel traffic flow database. Both data display a strong weekly pattern, and, if taken together, show that the relationship N(Q) is not a linear one. When Q is small, N grows like a second-order polynomial, while at large Q there is a tendency towards saturation, leading to an S-shaped relationship. Although visible in all data from all crashes, the data for the severe crashes display a less prominent saturation. As a by-product, the analysis performed here also demonstrates that the crash frequencies follow a negative binomial distribution, where both parameters of the distribution depend on the hour of the week, and, presumably, on the traffic state in this hour. The work presented in this paper aims at giving the reader a better understanding on how crash rates depend on exposure.
This paper demonstrates an approach that makes it easy to find patterns in traffic crash databases, and to specify their statistical significance. The detected patterns might help to prevent traffic crashes from happening, since they may be used to tailor campaigns to the community at hand. Unfortunately, the approach described here comes at a cost: it identifies a considerable amount of patterns, not all of them are being useful. The second disadvantage is that is needs a certain size of the database: here it has been applied to a database of the city of Berlin that contains about 1.6 Million (M) crashes from the years 2001 to 2016, of which about 0.9M had been used in the analysis.
This paper presents a preliminary study on behalf of the Bavarian Red Cross (BRK). Its focus is on the traffic safety impact of additional blue lights for ambulance vehicles of the Bavarian Red Cross (BRK). The study examines if and to what extent a traffic safety impact can be measured. The high crash risk particular during emergency drives has been reported in numerous studies. The BRK endeavors to decrease the crash frequency of their ambulance vehicles by improving their visibility especially at intersections and narrow gateways. Therefore, additional side flashing lights have been proposed. The purpose of this study is to evaluate the effectiveness of these flashing lights. In this context, emergency drives conducted with equipped and unequipped ambulance vehicles were compared. More precisely, the exit of a BRK station and the adjacent road segment was observed for 14 days by a video camera, which enables computer-vision aided analysis of the traffic. Within this time frame, 38 traffic situations of unequipped and 13 situations of equipped ambulance vehicles were observed. The trajectories of interacting road users in these situations were analyzed. Indicators for the adaption of road users to ambulance vehicles leaving the BRK station were used, like deceleration, position and time of braking as well as time of reaching walking speed. The indicators showed, that road users entered the observation area slower encountering equipped ambulance vehicles-probably due to prior braking-than was measured at emergency drives without additional flash lights. Furthermore, road users on average were breaking 3.5 meters earlier, less intensely and reached walking speed 4 meters earlier when ambulance vehicles were equipped with additional flash lights. The interpretation of these results is that earlier reaction implies earlier perception of the ambulance vehicle.
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