Crossroad-traffic-signal-lamp control algorithm based on neural network

A traffic signal light and neural network technology, applied in the field of traffic signal simulation control, can solve problems such as congestion, serious congestion, and failure to alleviate traffic conditions, and achieve the effects of solving congestion, good adjustment accuracy, and good practicability.

Inactive Publication Date: 2018-08-14
LIAONING UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

In this case, the traffic conditions at road intersections are ever-changing. When a certain direction is congested or multiple directions are congested, the road intersection is still controlled according to the predetermined method of traffic lights, which not only does not alleviate the traffic situation, but often l

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  • Crossroad-traffic-signal-lamp control algorithm based on neural network

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Embodiment Construction

[0035] The present invention will be described in further detail below, so that those skilled in the art can implement it with reference to the text of the description.

[0036] The neural network-based intersection traffic signal control algorithm provided by the present invention includes:

[0037] Detect the speed, acceleration, and number of vehicles passing through the intersection to estimate the delay time of a single vehicle;

[0038] The formula for calculating the delay time is: the formula for calculating the delay time is:

[0039]

[0040] Where T is the delay time, Where d i,j Represents the delay time of the i-th vehicle on the j-th road segment, l j Is the length on the j-th section, v i,j Is the actual speed of the vehicle, Indicates the expected speed of the vehicle in the free flow traffic state, a i,j Is the acceleration of the i-th vehicle passing through the intersection, and m is the number of vehicles passing through the intersection.

[0041] According to t...

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Abstract

The invention discloses a crossroad-traffic-signal-lamp control algorithm based on a neural network. The crossroad-traffic-signal-lamp control algorithm includes the steps that driving speeds and accelerated speeds when vehicles pass through crossings are detected, and single-vehicle delay time is estimated according to the quantity of traffic flow; according to the estimated single-vehicle delaytime, evaluation indexes of a whole road section are obtained; road evaluation indexes are defined according to urban road grades; a neural network model is established, and a signal lamp control strategy according to crossing traffic flow is given, wherein the delay time, the evaluation indexes of the whole road section, road evaluation indexes and walker passing time serve as an input layer of input variables; a first neural network is established, the vehicle delay time and the evaluation indexes are analyzed in the first neural network, and a vector group expressing the signal lamp controlstrategy is obtained; the vector group expressing the signal lamp strategy serves as control strategy to be output, and the congestion problem of the tide traffic flow phenomenon is effectively solved.

Description

Technical field [0001] The invention relates to the field of traffic signal simulation control, in particular to a signal light control algorithm for intersections based on neural networks. Background technique [0002] With the continuous advancement of urbanization, the number of motor vehicles has increased rapidly, and urban road congestion has become increasingly serious. In order to ensure the normal operation of vehicles between urban roads, traffic signal control at road intersections is particularly important. The traffic signal control of the intersection is mainly realized by the traffic signal controller. Therefore, the role of traffic signal controllers in daily life is becoming more and more important. [0003] In the prior art, the control of the traffic signal lights at the intersection is usually controlled according to a preset time, and the green light transit time in each direction of the intersection does not change once it is set during operation. In this c...

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Application Information

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IPC IPC(8): G08G1/08G05B13/04
CPCG05B13/042G08G1/08
Inventor 魏丹张忠洋唐阳山张兆飞
Owner LIAONING UNIVERSITY OF TECHNOLOGY
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