Shortest path induction method based on real road network features and dynamic travel time

Abstract

The invention discloses a shortest path induction method based on real road network features and dynamic travel time. The method includes the steps that firstly, the real road network features are taken into account, and a road network abstract graph is obtained based on intersection turning restriction information and link traffic flow direction maldistribution; secondly, the dynamic travel time of a road section is predicted by regarding real-time travel time and historical travel time as factors, and road section traffic impedance is calculated and serves as road section weight of an abstract road network according to the predicted dynamic travel time; finally, the real-time shortest path is obtained through the shortest path search algorithm. The method overcomes the defects that an existing road network abstract model can not reflect the real road network features (the intersection turning restriction information and the link traffic flow direction maldistribution) and an existing shortest path induction method based on static traffic information can not respond to accidental, random and dynamic traffic events (such as traffic jams, temporary control and traffic accidents) in time and the other defects, and the induction path obtained through the method is better in real-time performance, more accurate and more effective.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic control and traffic guidance, and relates to a shortest path guidance method. Background technique [0002] With the rapid development of urbanization, the problem of traffic congestion is becoming more and more serious. Through the upgrading of road hardware such as urban road widening and road network reconstruction, the problem of traffic congestion has not been alleviated, but the service level of the entire transportation network has been reduced. The actual traffic efficiency has dropped significantly, seriously restricting the development of the city. As one of the effective ways to alleviate urban traffic congestion, Urban Intelligent Traffic Guidance (UITG) influences travelers before and during travel by guiding information, which can balance the traffic flow on the network macroscopically and make the network comprehensive The traffic capacity is maximized; the travel cost...

AI Technical Summary

Problems solved by technology

However, in the real world, because the traffic state on the road section changes in real time, the static traffic state does not conform to the real road network environment

Moreover, the traditional road network abstraction scheme based on undirected graphs cannot accurately represent the turning restriction information of intersections in the real road network (such as no left turn, no right turn, one-way lane), and cannot reflect the uneven distribution of road traffic directions. characteristics (peak out of the city and peak of returning to the city, etc.)

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