Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 3862-3874.doi: 10.13229/j.cnki.jdxbgxb.20240359

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Adaptive control strategy for isolated intersection based on phase queue simulation

Dao-bin WANG(),Hui-hui ZHAO,Yuan-yuan XU,Zu-peng LIU   

  1. College of Automobile and Traffic Engineering，Wuhan University of Science and Technology，Wuhan 430065，China
  • Received:2024-04-07 Online:2025-12-01 Published:2026-02-03

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Abstract:

An adaptive control algorithm based on the in-phase queue dissipation was proposed to address the issues of low control efficiency and high model complexity in existing signal control algorithms. Firstly， a prediction model was established to optimize signal timing parameters， and calculate the vehicle's stopping delay based on traffic shockwave theory. Secondly， the acceleration and deceleration delays of vehicles were calculated based on the speed variation curve. Finally， state transition matrix for constructing dynamic optimization model was proposed to minimize delays， thereby realizing full-stage optimization of intersection signal control. Compared with fixed-time control and actuated control methods， the proposed method respectively reduced the average delay by 55% and 27.8% per vehicle in ultra-high-traffic demand scenario and reduced the average number of stops by 58.5% and 10.1%. In high-traffic demand scenario， the average delay decreased by 36.1% and 14.6% per vehicle， and the average number of stops decreased by 23.4% and 8.7%， respectively. In medium-traffic scenario， the average delay decreased by 22.8% and 10.5% per vehicle， and the average number of stops decreased by 3.3% and 2.6%. The results indicate that the algorithm proposed in this study can effectively improve the traffic operational efficiency at isolated intersections under different traffic demand conditions.

Key words: engineering of communication and transportation, adaptive signal control, phase queue simulation, flow prediction, dynamic optimization

CLC Number: 

  • U491

Fig.1

Vehicle delays"

Fig.2

Illustration of speed change state"

Fig.3

Cycle iteration logic"

Fig.4

Vehicle trajectories under different dissipation scenarios"

Fig.5

Acceleration change profile"

Fig.6

Follow the vehicle travel speed recovery curve"

Fig.7

Algorithmic process"

Fig.8

Intersection of east shanghai road and you-ai Street"

Table 1

Medium load experimental scenario traffic"

进口

直行/行人

/［（veh·h-1）/（p·h-1）］

左转/（veh·h-1）右转/（veh·h-1）合计/（veh·h-1）
东297/161147114558
西213/13778120411
南694/25111160865
北660/14010233795

Table 2

High load experimental scenario traffic"

进口

直行/行人

/［（veh·h-1）/（p·h-1）］

左转/（veh·h-1）右转/（veh·h-1）合计/（veh·h-1）
东396/214196152744
西284/182104160584
南925/334148801 153
北880/186136441 060

Table 3

Ultra-high load experimental scenario traffic"

进口

直行/行人

/［（veh·h-1）/（p·h-1）］

左转/（veh·h-1）右转/（veh·h-1）合计/（veh·h-1）
东495/268245190930
西355/228130200685
南1 156/4181851001 441
北1 100/233170551 325

Table 4

Timing plan"

进口直行时间/s左转时间/s

左转放行

顺序

固定/感应Gmin固定/感应Gmin
东45/3530/25lead
西30/2515/15lag
南60/3520/15lag
北60/3520/15lag

Fig.9

Comparison of delays from different thresholds"

Fig.10

Comparison of control benefits under different traffic loads"

Fig.11

Ultra-high demand vehicle queue length"

Fig.12

High demand vehicle queue length"

Fig.13

Medium demand vehicle queue length"

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