Self-learning method and system for traffic signal control

A self-learning method and traffic signal technology, which is applied in the control of traffic signals, internal combustion piston engines, mechanical equipment, etc., can solve the problems of vehicle delays and difficult access, and achieve the effect of improving traffic capacity, high efficiency, and reducing risks

Active Publication Date: 2014-04-30
UNIV OF SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a self-learning method and system for traffic signal control, which effectively reduces the average vehicle delay...

Method used

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  • Self-learning method and system for traffic signal control
  • Self-learning method and system for traffic signal control
  • Self-learning method and system for traffic signal control

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Embodiment 1

[0030] figure 1 It is a flow chart of a self-learning method for traffic signal control provided by Embodiment 1 of the present invention. Such as figure 1 As shown, it mainly includes the following steps:

[0031] Step 11. Construct a virtual scene including the intersection and a virtual signal according to the shape, geometric size and road channelization of the real intersection, set up a virtual vehicle detector at the upstream entrance and exit of each road at the intersection, and set up a virtual vehicle detector on each road Spawns a specified number of different types of virtual vehicles.

[0032] The embodiment of the present invention is implemented based on a simulation platform, and in order to improve generality and accurate determination, the situations of different drivers and different vehicles are taken into consideration. The different types of virtual vehicles can include the following configurable parameters: driver age, personality (introversion, intr...

Embodiment 2

[0061] In order to facilitate understanding of the present invention, the traffic signal control self-learning method of the intersection is taken as an example to further introduce below, and it specifically includes the following steps:

[0062] 1. According to the shape, geometric size and road channelization of the controlled real "cross" intersection, use the scene construction function of the microscopic simulation software to generate the corresponding virtual intersection. Each generated road generates a corresponding upper speed limit according to the actual road traffic speed limit Virtual vehicle detectors are generated at each upstream entrance (generally 100 meters) and exit of the intersection to obtain the delay of vehicles at the intersection.

[0063] The intersection traffic signal control scheme involves the concepts of signal cycle and phase. The signal cycle refers to: the red, yellow, and green lights of the traffic signal are flashing in sequence during...

Embodiment 3

[0096] Image 6 A schematic diagram of a self-learning system for traffic signal control provided in Embodiment 3 of the present invention; Image 6 As shown, the system mainly includes:

[0097] The virtual scene construction module 61 is used for constructing the virtual scene comprising the crossing according to the shape, geometric dimensions and road channelization of the real crossing;

[0098] A virtual signal machine 62, set at a predetermined position in the virtual scene, for generating virtual signals;

[0099] The virtual vehicle detector 63 is arranged at the upstream entrance and exit of each road at the intersection, and is used to determine the phase queuing state;

[0100] A dispatching module 64, configured to generate a specified number of different types of virtual vehicles at each phase;

[0101] The main control module 65 is used to generate a traffic control scheme according to the self-study result, and perform time matching on the virtual signal mac...

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Abstract

The invention discloses a self-learning method and system for traffic signal control. The method includes the steps that a virtual scene containing an intersection and a virtual annunciator is built according to the shape and geometric dimensions of an actual intersection and road canalization, virtual vehicle detectors are arranged on upstream entrances and upstream exits of all roads of the intersection, and the assigned number of virtual vehicles of different types are generated on all the roads; queuing states of the roads controlled by all phases are obtained through the virtual vehicle detectors; according to a preset lookup table, optimum phase green light timing corresponding to the current queuing states of the roads controlled by all the phases is obtained; after the optimum phase green light timing of all the phases is sequentially executed through the virtual annunciator, average delay of the virtual vehicles at the intersection in the current period is obtained, and an evaluation value in the lookup table is updated to finish self-learning of traffic signal control this time. Through the method and system, the average delay of vehicles at the intersection in actual traffic is effectively reduced, the traffic capacity of the intersection is improved, and the problem that vehicle delay at the intersection is difficult to obtain is solved.

Description

technical field [0001] The invention relates to the technical fields of road traffic control and intelligent traffic, in particular to a self-learning method and system for traffic signal control. Background technique [0002] Intersection is the bottleneck of urban traffic, and its optimal control is the basis for smooth urban traffic. The existing intersection control models are mainly the Webster delay model of the United Kingdom and the HCM2000 delay model of the American Road Capacity Manual. The above two models are mainly composed of uniform arrival delay and random arrival delay items of traffic flow. The Webster delay model can calculate the optimal cycle time of the signal, but it is not suitable for dealing with high saturation intersection traffic flow control (when the saturation is higher than 0.9); the HCM2000 delay model can better estimate the intersection vehicles under different saturation delay scenarios, but does not provide an optimal cycle duration e...

Claims

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Application Information

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IPC IPC(8): G08G1/07
CPCY02T10/40
Inventor 陈锋
Owner UNIV OF SCI & TECH OF CHINA
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