Bus road condition processing system and method based on traffic multi-source data fusion

Abstract

The invention discloses a bus road condition processing system and method based on traffic multi-source data fusion. The bus road condition processing system comprises a preprocessing module, a source data processing module, a data source evaluation module and a data fusion module. The preprocessing module is used for processing traffic multi-source data; the source data processing module is used for processing data of a non-bus data source road condition calculation result; the data source evaluation module comprises an evaluation framework used for evaluating the quality of data sources, and the credibility of multi-source data to the bus road condition calculation result can be obtained through processing of the evaluation framework; the data fusion module is used for performing weighted calculation on the multi-source data based on the data source evaluation result, and finally the required bus road condition data are obtained; a display module is used for superposing a bus road condition map and a map file obtained through processing and displaying road condition information of the whole bus road network. By synthesizing the multiple traffic data source road condition calculation results, the advantages of all the sides can be made the best of, the fusion result information used for describing the bus road conditions can be obtained, and data support is provided for bus service.

Description

technical field [0001] The invention relates to a bus road condition processing technology of an intelligent transportation system, in particular to a bus road condition processing system and method based on the fusion of traffic multi-source data. Background technique [0002] Data fusion technology refers to the information processing technology that uses a computer to automatically analyze and synthesize a number of observation information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. [0003] Due to the different composition of traffic data sources at home and abroad, the proportion of different data sources is also different, and the research points and technologies in fusion are also significantly different. The current data fusion methods for the traffic field have the following limitations: (1) It is mostly the fusion of two types of data: most of the current mainstream fusion methods only involve two ty...

AI Technical Summary

Problems solved by technology

[0003] Due to the different composition of traffic data sources at home and abroad, the proportion of different data sources is also different, and the research points and technologies in fusion are also significantly different. The current data fusion methods for the traffic field have the following limitations: (1) It is mostly the fusion of two types of data: most of the current mainstream fusion methods only involve two types of data sources, such as coil / taxi, bus / taxi, and the model does not have good scalability for new types of data sources sex

(3) Use a certain type of data as the main data: because the main demand is for general travel conditions, these technical methods generally use taxi data as the main data source, while other types of data are only used as auxiliary data to carry out the main data. It must be adjusted, such road condition calculation result information cannot meet the special bus needs

(4) Sample training is required and the training cost is high: mainly for artificial neural network algorithms, as one of the currently widely used algorithms, its model training process requires a high sample size, and there are big problems in training efficiency, and for unstable data In the worst case, the training process needs to be repeated several times, and even the establishment of the neural network will fail when the data source increases

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