Operational and Safety Evaluation of Freeways with Posted Minimum Speed Limit
Victor Muchuruza, Renatus Mussa
Abstract
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Victor Muchuruza, Renatus Mussa
Abstract
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An operational and safety evaluation was conducted in relation to the posting of the minimum speed limit of 40 mph and the maximum speed limit of 70 mph on the Florida rural interstate freeway system. The results showed that 57% of the recorded vehicles exceeded the maximum speed limit. Additionally, while only 0.14% of recorded vehicles had speeds below the 40 mph posted minimum speed limit, 9% of crash-involved vehicles were estimated to have speeds below 40 mph. The overrepresentation of slow-moving vehicles in the crash data suggests that even a small proportion of under-40-mph vehicles can have negative implications on safety. Thus, regulation of vehicle speeds at the lower end of the speed distribution is equally important. The second order polynomial model developed to estimate the risk of a vehicle being involved in a crash as a function of the speed deviation from the mean speed of traffic indicated that the minimum risk occurred when the driving speed was 8 mph above the mean speed, equal to the 85th percentile speed observed in the field. Further, the Poisson regression modeling indicated that the difference between the 85th and 15th percentile speeds had a positive effect on crashes.
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An operational and safety evaluation was conducted in relation to the posting of the minimum speed limit of 40 mph and the maximum speed limit of 70 mph on the Florida rural interstate freeway system. The results showed that 57% of the recorded vehicles exceeded the maximum speed limit. Additionally, while only 0.14% of recorded vehicles had speeds below the 40 mph posted minimum speed limit, 9% of crash-involved vehicles were estimated to have speeds below 40 mph. The overrepresentation of slow-moving vehicles in the crash data suggests that even a small proportion of under-40-mph vehicles can have negative implications on safety. Thus, regulation of vehicle speeds at the lower end of the speed distribution is equally important. The second order polynomial model developed to estimate the risk of a vehicle being involved in a crash as a function of the speed deviation from the mean speed of traffic indicated that the minimum risk occurred when the driving speed was 8 mph above the mean speed, equal to the 85th percentile speed observed in the field. Further, the Poisson regression modeling indicated that the difference between the 85th and 15th percentile speeds had a positive effect on crashes.
Key concepts: Speed limit, Percentile, Poisson regression, Crash, Statistics, Poison control, Poisson distribution, Transport engineering