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Introduction
This article compares observed driving behavior in a city, a town, and a village.Method
Unobtrusive observations were made at intersections in each residential type. Five violation types were observed: (a) not wearing a seat belt (seat belt violation); (b) not using a safety seat for a child (safety seat violation for children); (c) not using a speaker while speaking on the phone (on-phone violation); (d) failing to comply with a ‘give way’ sign (‘give way’ sign violation); and (e) stopping in an undesignated area (undesignated stop violation). It was expected that in accordance with the anonymity hypothesis that the bigger residential areas' rate of traffic violations would be higher. The effects of the residential type, drivers' gender, and age were assessed using the multiple regression model. The stepwise method of evaluation was employed. The model converged on step 3 (Adjusted R square = 0.039). Residential type and gender contributed significantly to the model. Results: Consistent with prior research, male drivers committed more violations than female drivers. Chi-square analyses were used to test the distribution of violations by the settlement types. Overall, more drivers committed violations in the two small residential areas than in the city, with 30% of city drivers, 43% of town drivers, and 51% of village drivers committing at least one violation (χ2 (2) = 37.65, p < 0.001). Moreover, in the town and the village, a combination of one or more violations was committed more often than in the city (χ2 (1) = 34.645, p < 0.001). Accordingly, more drivers committed violations in the two small settlements (48.4%) than in the city (30.6%). Possible explanations for the observed results were provided in the Discussion section.Impact on Industry
The conclusions of this paper are that drivers in small villages tend to disobey traffic laws. Therefore, efforts have to be made in companies to take this issue in consideration while running fleets in companies located in small places far from the center. 相似文献Method: Police-reported crashes from 2 Australian jurisdictions were used to calculate a fatal crash rate by speed limit and crash type. Example safe speed limits were defined using threshold risk levels.
Results: A positive exponential relationship between speed limit and fatality rate was found. For an example fatality rate threshold of 1 in 100 crashes it was found that safe speed limits are 40 km/h for pedestrian crashes; 50 km/h for head-on crashes; 60 km/h for hit fixed object crashes; 80 km/h for right angle, right turn, and left road/rollover crashes; and 110 km/h or more for rear-end crashes.
Conclusions: The positive exponential relationship between speed limit and fatal crash rate is consistent with prior research into speed and crash risk. The results indicate that speed zones of 100 km/h or more only meet the objectives of the Safe System, with regard to fatal crashes, where all crash types except rear-end crashes are exceedingly rare, such as on a high standard restricted access highway with a safe roadside design. 相似文献