Network Modeling Method of Comprehensive Passenger Terminal Group Based on Dynamic Graph Hybrid Automata

Abstract

The invention discloses a comprehensive passenger transport hub group network modeling method based on a dynamic graph hybrid automaton, which includes: dividing the hub group network; wherein, the city / region range is divided and numbered according to the administrative area, and the hub group of each city / region Classify the types, select the main hub and branch hub; describe the hub group with a graph, and establish the topology network of the hub group; the dynamic graph hybrid automata model of the single passenger transport hub; the parallelism of the dynamic graph hybrid automaton of the hub group network Combined model. The present invention establishes a network model of a comprehensive passenger transport hub group based on dynamic graphs and hybrid automata theory, and deduces a state space model of the hub group network by taking passenger flow as a state quantity in the hub; thereby providing a basis for estimating and predicting the passenger flow in the hub It provides an effective means to improve the integrated collaborative operation efficiency of the hub group and optimize the allocation of transport capacity resources, and provides effective support for ensuring the collection and distribution capacity of the hub.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a network modeling method of a comprehensive passenger transport hub group based on a dynamic graph hybrid automaton. Background technique [0002] As an important engine of regional development, efficient coordination between passenger transport hubs is an important guarantee for promoting the interconnection of urban agglomeration transport infrastructure and the integration of transport services. Due to the constraints of regional development, institutional mechanisms, functional positioning, operating models, and technical means, the passenger transport hubs of major urban agglomerations currently have weak functional complementarity, poor connection of modes, lack of coordinated operation, inefficient intermodal services, and insufficient information sharing. These problems have seriously affected the overall operational efficiency of the comprehensive tra...

AI Technical Summary

Problems solved by technology

Due to the constraints of regional development, institutional mechanisms, functional positioning, operating models, and technical means, the passenger transport hubs of major urban agglomerations currently have weak functional complementarity, poor connection of modes, lack of coordinated operation, inefficient intermodal services, and insufficient information sharing. and other issues have seriously affected the overall operational efficiency of the urban agglomeration comprehensive transport network

[0003] To provide support for improving the overall collection and evacuation capacity of urban agglomeration hubs, shorten transfer waiting time, and improve system operation efficiency; in order to realize high-efficiency integrated operation of hub agglomerations, collaborative safety assurance and comprehensive information services; for urban agglomeration passenger travel needs, Based on mobile Internet and wide-area multi-source big data, research on precise perception technology for passenger travel demand in urban agglomerations, feature extraction and characterization technology for interline travel chains, mining technology for large-scale spatio-temporal distribution of passenger flow, and intelligent identification technology for inter-hub travel collection and distribution points , hub scale and hub group layout collaborative optimization technology is very meaningful, but the research and development of these technologies are inseparable from the passenger hub group model as support and foundation

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