Traffic flow modal fitting method based on quantum random walk

Abstract

The invention discloses a traffic flow modal fitting method based on quantum random walk. The method comprises the following steps: (1) constructing a modal generator based on quantum random walk; (2) multi-modal preparation; (3) modal screening; and (4) constructing a mapping mechanism between the mode and the traffic flow. According to the method, the high-speed traffic flow is considered to be a complex geographic space-time process formed by aliasing and coupling a plurality of traffic flow modes with different structures, and modeling simulation of the traffic flow is attempted to be realized from the perspective of multi-scale analysis; all possible traffic flow modes in high-speed traffic flow are generated by utilizing quantum random walk under a basic framework of an expressway network topological structure; under the constraint of an actual traffic flow time sequence, modal screening is achieved based on a specific screening rule, a conversion coupling mechanism between a traffic flow modal and traffic flow is explored, and modeling simulation of high-speed traffic flow is achieved.

Description

technical field [0001] The invention relates to the technical fields of urban planning and traffic geography, in particular to a traffic flow mode fitting method based on quantum random walk. Background technique [0002] Traffic flow is an important indicator for many traffic applications and is usually collected by sensors located at (highway) entrance / exit stations. Traffic flow provides key information in identifying the degree of road congestion, traffic diversion, and post-processing of traffic accidents, and plays an indispensable role in traffic management, flow forecasting, and regulation. However, the traffic flow system is an open system, and different drivers have different driving patterns. The heterogeneity of drivers increases the complexity of traffic flow, which brings great challenges to traffic flow modeling and simulation. [0003] The current traffic flow modeling models can be roughly classified into two categories: deterministic models and stochastic ...

AI Technical Summary

Problems solved by technology

However, the actual traffic flow cannot satisfy the independence and randomness assumptions of the classic random walk in most cases, which makes the classic random walk invalid in traffic flow simulation

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