Adaptive traffic state estimation method

Abstract

An adaptive traffic state estimation method contains the following specific steps: (1) in the fusion parameter training, an elementary probability assignment table of each data source is calculated according to traffic information of each data source; (2) in traffic state estimation of multisource fusion, the trained probability assignment tables of the sources are fused into a decision table, and a traffic state estimation result is obtained from the decision table according to real-time traffic information of each source; and (3) in adaptive adjustment, adaptive adjustment is carried out periodically on the probability assignment tables of the sources, and the adaptively-adjusted probability assignment tables of the sources are fused into a new decision table for real-time traffic state estimation during the next period. The probability assignment table of each data source can be adaptively adjusted periodically. Thus, data size and workload of the fusion parameter training at the earlier stage are minimized, and the adaptively-adjusted probability assignment tables can adapt to real-time urban road traffic state changing patterns. Traffic state estimation accuracy can be raised.

Description

technical field [0001] The invention relates to an adaptive traffic state estimation method, which belongs to the field of intelligent traffic research. Background technique [0002] The rapid development of our country's society and economy has brought about the rapid growth of urban traffic flow, and the problem of traffic congestion is also becoming more and more serious. The use of intelligent transportation systems to alleviate traffic congestion and improve transportation efficiency has become a new trend and a turning point for comprehensively solving traffic problems. Among them, the realization of real-time estimation of traffic status through effective processing and analysis of traffic data has gained widespread attention. [0003] Due to the advantages of D-S evidence theory in the representation, measurement and combination of uncertainties, many intelligent transportation researchers have proposed the use of D-S evidence theory to integrate multi-source traffi...

AI Technical Summary

Problems solved by technology

However, in the traditional traffic state estimation method based on D-S evidence theory, the basic probability distribution table of each data source has been calculated during training and is fixed, so the decision table fused by it and used for traffic state estimation It is also fixed, resulting in the following defects: 1. In the early stage of fusion parameter training, a large amount of road traffic data is required and all road traffic states are manually marked by video, which is a huge workload; 2. In practical applications, cities The road traffic state change pattern is not static, and the fixed basic probability allocation table cannot adapt to the long-term (generally more than 3 months) road environment, which leads to the inability of the decision table used for traffic state estimation to adapt to the road environment, which reduces the traffic state estimation. accuracy rate

Although adaptive has been used in many fields, it has not been applied to traffic estimation

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