Research on the high-accuracy mileage statistics method based on the civil satellite data

Zunmin Liu,Xilong Zhang,Chuijie Yi,Zhihong Liu

doi:10.1177/1550147719852029

Zunmin Liu, Xilong Zhang + Show 2 more

Open Access

https://doi.org/10.1177/1550147719852029

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Abstract

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.

Highlights

Mileage statistics is an important basis for vehicle maintenance, workload assessment, fuel consumption statistics, tire management, and fatigue driving warning in a fleet management
The ending point is the midpoint of PnÀ1, Pn, and it is tangent to PnÀ1, Pn. It can be seen from this feature that the trajectory generated by the Bspline curve does not pass through the positioning starting and ending points, and so the boundary processing of the original data points is required
It can be seen that the accuracy of the fitting method is 5.97% higher than the odometer method and 3.12% higher than method, which satisfies the requirement of accurate mileage statistics

Summary

Introduction

Mileage statistics is an important basis for vehicle maintenance, workload assessment, fuel consumption statistics, tire management, and fatigue driving warning in a fleet management. The original trajectory data need to be preprocessed, including static error point (zero point drift) processing, off-route singular point processing, and key missing data compensation during driving.[11,12]. According to the abovementioned method, the center of gravity coordinates of all the static drift discrete points in the new coordinate system can be calculated. The curvature elimination method is more accurate in the case of highways or suburban roads with little curvature, but in urban roads, there is a risk of eliminating normal data when there is a right-angle or greater than right-angle turning.[15] In this article, the track line contains urban sections; three-point velocity threshold method is adopted to process large error points based on continuous positioning points. (yA À yB)2 + (xA À xB)[2]

Calculate the projection coordinates of the offset point

Result analysis

Findings

Conclusion