Different species traffic information real time integrating method

Abstract

This invention discloses one method for information process in traffic technique by use of abnormal traffic information for real time traffic status merging estimation, which comprises the following steps: sensor test information property extracting; b, reliability matrix W determining; c, reliability establishing; d, property merging module; e, final property deciding.

Description

technical field [0001] The invention relates to an information processing method in the technical field of transportation, in particular to a real-time fusion method of heterogeneous traffic information. Background technique [0002] With the development of intelligent transportation technology, the dynamic estimation of urban traffic flow has received more and more attention. The traffic state estimation is inseparable from accurate and reliable traffic information, so the accuracy of the detection information method determines the accuracy of the traffic state estimation. There are many ways to detect traffic flow information in urban road network, mainly magnetic frequency induction coil detection method, GPS detection car detection method, video detection method and so on. These methods have achieved success in some aspects and have practical value, but they have their own limitations in the estimation of urban road network traffic status. The induction loop detector c...

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Problems solved by technology

[0003] After searching the prior art literature, it was found that the paper "An arterial speed estimation model fusing data from stationary and mobile sensors" published by R.L.Cheu et al. , (A Main Road Speed ​​Estimation Model for Fusion of Static and Dynamic Sensor Data) In this paper, the neural network-based model is used to realize the fusion of the detection ring data and the GPS probe car data, and the simulation data is used to verify the model's performance through the simulation platform. Effect, however, the neural network needs a large amount of data truth value to train, which is difficult to achieve in practical applications; K.Choi et al published the paper "A data fusion" on Intelligent Transportation Systems (intelligent transportation system) in 2002 Algorithm for estimating link travel time", (data fusion algorithm for road section travel time estimation), among them, an algorithm based on fuzzy theory is proposed to fuse these two kinds of data, and a better effect is obtained, but the algorithm has high complexity, Moreover, poor portability is difficult to adapt to the real-time fusion environment of traffic information; E. Faouzi et al. published in their 2006 "Multisensor, Multisource Information Fusion: Architecture, Algorithm, Application, Processing of SPIE" (SPIE conference - multi-sensor, Multi-source Information Fusion: Architectures, Algorithms, and Applications) in the paper "Classifiers and distance-based evidential fusion for road travel timeestimation" (for estimating road segment travel time based on classifiers and distance-based evidence fusion) proposed a The classifier fusion method based on evidence theory provides a new idea for traffic state estimation. However, the above method is difficult to achieve real-time fusion estimation, requires a large amount of prior data, and has poor portability, so it greatly limits its engineering. application

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