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Traffic control and guidance system and method based on evolutionary multi-objective optimization and ant colony algorithm

A multi-objective optimization and ant colony algorithm technology, applied in the field of intelligent transportation, can solve problems such as poor real-time performance, poor intersection congestion prediction ability, complex coordination control, etc., and achieve the effect of improving traffic efficiency and reducing response time

Active Publication Date: 2015-07-08
NANJING UNIV OF POSTS & TELECOMM
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  • Summary
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing traffic control system is difficult to effectively take into account various indicators and efficiently adjust the timing of control signals according to real-time traffic conditions; in the regional multi-intersection coordination mode, the control coupling degree of each intersection is high, the coordination control is complicated, the real-time performance is poor, and the ability to predict intersection congestion is poor ; The guidance system takes less account of the dynamic cost on the road and the origin and destination of the traveler, and it is difficult to coordinate effectively with the control system

Method used

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  • Traffic control and guidance system and method based on evolutionary multi-objective optimization and ant colony algorithm
  • Traffic control and guidance system and method based on evolutionary multi-objective optimization and ant colony algorithm
  • Traffic control and guidance system and method based on evolutionary multi-objective optimization and ant colony algorithm

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Embodiment 1

[0055] like figure 1 As shown, the present invention provides a traffic control and guidance system based on evolutionary multi-objective optimization and ant colony algorithm. module, vehicle guidance module, guidance path output module, controlled traffic flow. Each module of the system is connected by wire or wirelessly.

[0056] The function of the traffic state perception module is to collect the traffic flow information on each lane at each intersection, the number of vehicles left in the last cycle, and the road weight matrix data of the road network through various sensing technologies.

[0057] The function of the single intersection optimization control module is: configured in the control nodes of each intersection, it can calculate the optimal timing scheme for each intersection according to the traffic state information collected by the traffic state perception module.

[0058] The function of the inter-intersection coordination control module is: configured in ...

Embodiment 2

[0067] The implementation process of intersection optimization control includes:

[0068] image 3 A single intersection model is given, which consists of four phases: go straight from east to west, turn left from east to west, go straight from north to south, and turn left from north to south. Each phase is timed as T 1 ,T 2 ,T 3 ,T 4 (unit: second), the traffic flow of the eight lanes is q 1 ,q 2 ,q 3 ,q 4 ,q 5 ,q 6 ,q 7 ,q 8 (unit: vehicle / second), the number of vehicles left in each lane in the last cycle is s i (i=1,2,3,4,5,6,7,8). Assuming that when the green light of each phase is on, according to experience, the first car needs 3t (t is the time it takes for the vehicle to pass the intersection without stopping) to pass the intersection, the second car needs 2t to pass the intersection, and the third car needs 2t to pass the intersection. And every car after that only needs t to pass the intersection, because the cars behind have already started.

[0069...

Embodiment 3

[0102] Such as Figure 6 As shown, the present invention also provides a method for realizing traffic control and guidance system based on evolutionary multi-objective optimization and ant colony algorithm, the method comprises the following steps:

[0103] Step 1: Collect the right-of-way matrix data of the road network, the traffic flow information on each lane at each intersection, and the number of vehicles left over in each lane at each intersection in the previous period.

[0104] Step 2: The single-intersection optimization control module optimizes the optimal timing scheme for each single-intersection according to the traffic information collected by the perception module.

[0105] Step 3: The inter-intersection coordination control module dynamically modifies the green time of the corresponding lane at each intersection according to the degree of traffic congestion between the intersections. The average time delay of each intersection is counted, and the average time...

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Abstract

The invention discloses a traffic control and guidance system and method based on evolutionary multi-objective optimization and an ant colony algorithm. According to the method, traffic control based on the evolutionary multi-objective optimization and a guidance technology based on the ant colony algorithm optimization are combined. A single crossing multi-objective optimization control model and an inter-crossing coordination mechanism are established by a control system. By adopting an improved evolutionary multi-objective optimization algorithm, traffic signal optimization is achieved. A method of equivalent path establishment is adopted on the guidance system, an optimal equivalent path is obtained by integrating with the improved ant colony algorithm, and traffic flow active guidance is achieved. The whole system comprises a traffic state sensing module, a single crossing optimization control module, an inter-crossing coordinating control module, a timing plan output module, a vehicle guidance module, a guidance path output module and controlled traffic flow.

Description

technical field [0001] The invention relates to a traffic control and guidance system based on evolutionary multi-objective optimization and ant colony algorithm and its realization method, belonging to the technical field of intelligent traffic. Background technique [0002] With the increase and complexity of traffic flow, the problem of urban road network congestion is becoming more and more serious, and the existing intelligent traffic control is difficult to improve the overall efficiency of the urban traffic system. An effective way to solve urban traffic problems is to organically combine the guidance system that actively guides traffic flow, balances traffic load, and the control system that passively guides traffic flow. How to construct such an intelligent transportation system optimization model and its optimization method has become a current research hotspot and key technology. The existing traffic control system is difficult to effectively take into account va...

Claims

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Application Information

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IPC IPC(8): G08G1/08G06N3/00G06Q10/04
CPCG06N3/00G06Q10/04G08G1/08
Inventor 罗杰周健
Owner NANJING UNIV OF POSTS & TELECOMM
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