A spatial-temporal modeling method for holographic urban rail transit objects

Abstract

The present invention is a holographic urban rail transit object time-space modeling method, a road network model-based road traffic multi-source data fusion processing method, which is characterized in that it includes the following steps: reading the road navigation layer, node road object Carry out classification, and merge basic road sections according to the classification; associate statistical traffic detection equipment and bicycle traffic detection equipment with basic road sections and node road objects; merge similar collected data according to the same format and time granularity; The data quality of the traffic detection equipment is processed, and the data of the single-vehicle traffic detection equipment is distributed by OD; the traffic parameters of the road sections are obtained. The benefits of the present invention lie in the establishment of unified road object division, unified association of data collection objects, and data quality control and processing specifications. , to realize the fusion processing of multi-source data from different traffic information systems.

Description

technical field [0001] The present invention relates to a holographic urban rail transit object spatiotemporal modeling method, in particular to a modeling method involving object types, object spatiotemporal attributes, static and dynamic attributes of the entire life cycle of objects, and object semantic relationships in urban rail transit networks. model method. Background technique [0002] Urban rail transit has the characteristics of energy saving, land saving, large transportation volume, and safety. It has developed rapidly in large and medium-sized cities in my country in recent years. Many cities have also established rail transit network data centers to classify and summarize the data information originally scattered in various disciplines and business modules, centralize storage, and scientifically manage them, and carry out big data analysis and mining. [0003] These rail transit network data centers often have limitations such as non-standard data object stru...

AI Technical Summary

Problems solved by technology

[0003] These rail transit network data centers often have limitations such as non-standard data object structure, poor data interoperability, and diverse coding, which make data association analysis difficult and standards difficult to unify.

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