Adaptive full-chain urban area network signal control optimization method

Abstract

The invention belongs to the technical field of ITS intelligent traffic systems, and particularly relates to an adaptive full-chain urban area network signal control optimization method. Traffic flow parameters are collected by utilizing a machine vision technology, traffic flow prediction is carried out based on a pre-trained traffic flow prediction algorithm by using obtained data, a microscopic traffic simulation model is constructed according to predicted traffic flow data, an original signal timing scheme and traffic network basic data, and a network-level signal optimization model is constructed. Active optimization is carried out on the network-level signal optimization model by adopting a Bayesian optimization algorithm so as to obtain an optimal signal timing scheme of the target area network. The method has good integration, an inner and outer circulation feedback closed loop is formed, an inner and outer circulation feedback mechanism can achieve interaction of a network signal optimization model and a microscopic traffic simulation model, it can be guaranteed that an optimization result scheme adapts to dynamic changes of the external environment, and then instantaneous dynamic optimization and long-term steady-state optimization are achieved.

Description

technical field [0001] The invention belongs to the technical field of the ITS intelligent transportation system, and relates to the fields of traffic big data, traffic flow prediction, Bayesian optimization algorithm, and computer vision and traffic network optimization technology, in particular to the technology of self-adaptive traffic control. An adaptive full-chain urban area network signal control optimization method. Background technique [0002] As an important part of urban road, urban road intersection is a key node of traffic management and control, and also a key breakthrough for urban traffic congestion control. Therefore, the research and governance of the intersection has been continued. The optimization of the signal timing of the network traffic intersection does not require large-scale reconstruction and expansion of the space resources of the intersection. It is an effective method with low cost and quick effect. Way. For example, Chen et al. built a lar...

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Problems solved by technology

[0003] The current research on signal control optimization has the following problems: first, it cannot meet the development needs of dynamic traffic at the same time; second, existing research methods cannot efficiently deal with large-scale network-level traffic signal control optimization problems; third, existing research Most of them focus on one or two points in traffic data collection, traffic flow prediction and network signal optimization. The integration is poor and it is impossible to form a closed feedback loop.

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