Floating car dynamic real-time traffic information processing method based on GPS data

Abstract

The invention discloses a floating car state real-time traffic information processing method based on GPS data, which includes the following steps: 1. GPS point data pre-processing and map pre-processing measures; 2. conduct point matching and select alternative road section sets according to projection distance and azimuth angles; 3. determine correct matching road sections and find out travel routes according to an improved optical route selection method considering topological relation between front and rear points (including a special regional node processing method for urban complicate road network); 4. calculate the route average travel speed, and conduct statistics in a speed formula to generate a road network speed thematic map. The floating car state real-time traffic information processing method based on GPS data of the invention is well applicable the modern complicated urban road network full of overpasses and staggered main and side roads, which can not only meet the speed requirement for large-data-quantity GPS data real-time calculation, but also obtain quite high matching precision.

Description

technical field [0001] The invention relates to the application field of intelligent transportation, in particular to a method for real-time processing of dynamic information of a floating car based on GPS data. Background technique [0002] With the development of society, the urban traffic flow is constantly increasing, and traffic congestion occurs from time to time. For this reason, it is very important to strengthen traffic management, and the key is to monitor the traffic status in real time. [0003] As a new traffic flow information collection technology, the floating car system has developed rapidly in big cities. Floating vehicles usually refer to vehicles with positioning and wireless communication devices. The floating vehicle system generally consists of three parts: on-board equipment, wireless communication network and data processing center. The floating car uploads the collected position and time data to the data processing center, which stores and preproc...

AI Technical Summary

Problems solved by technology

[0009] 1. The data needs to be processed in real time, and the amount of data is large, so the calculation speed and performance of the system are required to be high;

[0010] 2. The data interval is generally large, resulting in poor correlation between the positioning point information;

[0011] 3. The road network in modern cities is dense and complex, so the matching fault tolerance rate of the system is high

[0012] These three difficulties make the above existing algorithms often unable to obtain ideal calculation results when applied to the real-time calculation system of floating vehicles in complex urban road networks

Point-to-point and point-to-line matching methods, the general algorithm is simple to meet the performance requirements of real-time computing speed, but it is often only suitable for large road networks with a simple structure such as the national expressway network, and will generate high traffic in urban areas with dense roads and complex structures. The matching error rate; and the line-to-line matching algorithm based on the trajectory curve, although the error tolerance rate is high and there are some mature algorithms that can be used for reference, but these algorithms need to obtain accurate trajectory curves as matching samples first, so they are only applicable to For high-density time-series data sets with a GPS collection interval of less than 5 seconds, for GPS track points with a collection interval of more than 1 minute, the gap between the two points before and after will reach 1-2 kilometers, and a very complicated urban road network may be passed in between. Such as overpass ramps, etc., the poor correlation between the front and rear points determines that the entire matching algorithm cannot use the line-to-line matching algorithm that requires strong correlation

In addition, considering that the entire process from data preprocessing to map matching to statistical calculation of road average travel speed to the generation of congestion thematic maps needs to be completed in real time, and the amount of GPS data collected by the floating car is large, and the comprehensive calculation efficiency is also a problem. It is not suitable for these line-to-line matching methods that can achieve high accuracy but are very complicated using neural networks

In addition, although the current relatively advanced point-to-line matching method that considers the topological relationship improves the matching accuracy of the general urban road network without greatly increasing the computational complexity, it still has to be used in areas such as main and auxiliary roads and complex interchange ramp entrances and exits. To achieve ideal matching fault-tolerant performance, modern cities are generally densely populated with complex interchange systems. All expressways, urban expressways and some main roads have main and auxiliary roads. Floating vehicles frequently get on and off the main and auxiliary roads and pass over the interchanges, almost seldom once. Complete travel can avoid these systems and only travel on simple flat urban road systems, so this part of the error cannot be ignored

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