Comprehensive passenger transport hub group network modeling method based on dynamic graph hybrid automaton

Abstract

The invention discloses a comprehensive passenger transport hub group network modeling method based on a dynamic graph hybrid automaton. The method comprises the following steps: dividing a hub groupnetwork; dividing city / region ranges according to administrative regions, numbering the city / region ranges, classifying hub types of each city / region, and selecting a main hub and a branch hub; describing the hub group by using the graph, and establishing a topological network of the hub group, a dynamic graph hybrid automaton model of the single passenger transport hub and a parallel combinationmodel of a dynamic graph hybrid automation machine of the hub group network. Based on a dynamic graph and a hybrid automaton theory, a network model of a comprehensive passenger transport hub group isestablished, and a state space model of a hub group network is derived by taking passenger flow volume as a state quantity in a hub; therefore, an effective means is provided for estimating and predicting the passenger flow volume in the hub, a basis is provided for improving the integrated cooperative operation efficiency of the hub group and the optimal configuration of transport capacity resources, and an effective support is provided for guaranteeing the collecting, distributing and transporting capacity of the hub.

Description

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

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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