Traffic signal lamp scheduling method and system based on iterative learning model predictive control

Abstract

The invention discloses a traffic signal lamp scheduling method and system based on iterative learning model predictive control, and relates to the field of intelligent traffic, and the method comprises the following steps: 1, building a traffic flow simulation model of a target region; 2, obtaining closed-loop data in a fixed time interval according to a control strategy; and 3, taking the closed-loop data as a terminal condition of a subsequent open-loop prediction optimization problem to carry out prediction control. The method comprises the following steps: firstly, starting from a traditional traffic signal lamp scheduling strategy with fixed time, generating data through iteration, then carrying out iteration, obtaining an optimal traffic flow mode by using previous closed-loop data, and further obtaining an optimal traffic flow mode based on some simulations; it is proved that the iterative learning model predictive control strategy using the historical iterative closed-loop data set as the terminal constraint of the current day predictive control optimization problem can reduce the queuing time of the vehicles on the road, so that the vehicles can pass faster and smoother in the traffic network.

Description

technical field [0001] The invention relates to the field of intelligent traffic, in particular to a traffic signal light scheduling method and system based on iterative learning model predictive control. Background technique [0002] The congestion of urban traffic network will bring serious pollution and economic cost, and traffic light control is one of the effective ways to alleviate traffic congestion. Traffic light scheduling is an important topic in traffic engineering. For the urban traffic network, road sections and intersections always encounter the problem of heavy traffic flow, which brings troubles to the current traffic control strategy. [0003] At present, most of the signalized intersections adopt the method of controlling the signal lights in a predetermined cycle or a fixed time interval. This method is based on the traffic demand observed in the past, and pre-sets the timing scheme for control. This scheme cannot respond in time to the random changes in...

AI Technical Summary

Problems solved by technology

This method is based on the traffic demand observed in the past, and pre-sets the timing scheme for control. This scheme cannot respond to random changes in traffic demand in a timely manner.

However, the existing learning model predictive control scheme in this field is to establish a "safety assessment data set" by collecting all previous iteration trajectories. Since this set is composed of points, the convexity of the terminal set cannot be guaranteed. However, the establishment of terminal cost and terminal constraint set often leads to mixed integer quadratic programming (MIQP) problem

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