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In recent years, there are more and more applications of traffic violation monitoring in some countries. The present work aims to analyze the vehicle speeds nearby road traffic violation monitoring area on urban main roads and find out the impact of road traffic violation monitoring on the vehicle speeds. A representative urban main road section was selected and the traffic flow was recorded by camera method. The vehicle speeds before, within, and after the road traffic violation monitoring area were obtained by the calculation method. The speed data was classified and processed by SPSS software and mathematical method to establish the vehicle speed probability density models before, within, and after the road traffic violation monitoring area. The results show that the average speed and maximum speed within the traffic violation monitoring area are significantly slower than those before and after the traffic violation monitoring area. 70.1% of the vehicles before the road traffic violation monitoring area were speeding, and 80.2% of the vehicles after the road traffic violation monitoring area were speeding, while within the road traffic violation monitoring area, the speeding vehicles were reduced to 15.9%. When vehicles pass through the road traffic violation monitoring area, the vehicle speeds tend to first decrease and subsequently increase. In its active area, road traffic violation monitoring can effectively regulate driving behaviors and reduce speeding, but this effect is limited to the vicinity of the traffic violation monitoring. The distribution of vehicle speeds can be calculated from vehicle speed probability density models., 1. Introduction In many countries, with the development of the social economy, the number of automobiles has increased yearly, and a series of problems such as road congestion mess in [...]

For the current situation of the large error in civil satellite positioning data resulting in the calculation of smaller mileage by the polyline method than the actual mileage, a new method of mileage statistics has been proposed in this article. First, the original trajectory data are preprocessed to eliminate data errors. Second, based on the principle of shape approximation, it is preferred to implement the quadratic B-spline curve to accurately fit the mileage trajectory curve, comparing various curve fitting methods. Then, based on the trajectory curve control point data, the mileage statistics formula is derived, and the accurate mileage statistics method for non-precision satellite positioning signals is realized. Finally, the road test is carried out by using the photoelectric non-contact five-wheel instrument and GPS equipment. The polyline method and curve fitting method are used to generate contrastive curve and calculate the mileage, respectively. Taking photoelectric five-wheel data as the accurate mileage, the error analysis is carried out. The results show that the deviation between calculated and actual mileage values is less than 1%. Therefore, this method can meet the user's requirements for fleet management. [ABSTRACT FROM AUTHOR]