Adaptive traffic signal control based on bio-neural network

Castro, Guilherme B.; Hirakawa, André Riyuiti; Martini, José Sidnei Colombo

doi:10.1016/j.procs.2017.05.394

Cited by 24 publications

(14 citation statements)

References 10 publications

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“…Dahal et al [75] proposed an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach to assist the human operator of a road traffic control center to manage the current traffic state. Castro et al [76] proposed an adaptive biologically inspired neural network that received the system state and was able to change the behavior of the control scheme and the order of semaphore phases instead of prefixed cycle-based phases. e analyses conducted showed that the model was robust to different initial conditions and had fast adaptation among system equilibrium states.…”

Section: Using Multi-source Traffic Data To Study the Evolution Regulmentioning

confidence: 99%

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Zhou

et al. 2020

Journal of Advanced Transportation

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To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

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Section: Using Multi-source Traffic Data To Study the Evolution Regulmentioning

confidence: 99%

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Zhou

et al. 2020

Journal of Advanced Transportation

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“…where , ( ) is the time the traffic flow takes to join the queue from the entrance of link ( , ) which can be calculated according to formula (11). V ℎ represents the average vehicle length.…”

Section: Model-based Signal Controlmentioning

confidence: 99%

“…Samra et al [10] proposed a dynamic programming method to obtain the optimal green time splits. Castro et al [11] proposed an adaptive biologically-inspired neural network method to control urban traffic. However, they function ineffectively under oversaturated condition [12].…”

Section: Introductionmentioning

confidence: 99%

Integration of Model‐Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

Chen

2019

Mathematical Problems in Engineering

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An improved model-based predictive control approach integrating model-based signal control and queue spillover control is proposed in this paper, which includes three modules: model-based signal control, queue spillover identification, and spillover control to deal with the problem of traffic congestion for urban oversaturated signalized intersection. The main steps are as follows. First of all, according to the real-time traffic flow data, the green time splits for all intersections will be solved online by the modelbased signal control controller whose optimization model is based on model-predictive control (MPC) strategy. Second, the queue spillover identification module will be used to detect the potential queue spillover. If potential queue spillover is detected, the spillover control module will be activated to prevent vehicles from the upstream link of the link with possible spillover from entering the downstream link to avoid traffic congestion. The experiment is performed on a simulated road network. The results verify that the proposed scheme can significantly decrease the delay which reflects the overall performance of the studied intersection.

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“…As a result, it was concluded that average vehicle 2 Advances in Civil Engineering delays and stop ratios can be reduced significantly. Castro et al [26] developed an adaptive (biological neural networkbased) system which can also change the phase control scheme as well as traffic signal timings. e proposed system was evaluated on a three-leg signalized intersection.…”

Section: Introductionmentioning

confidence: 99%

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

Çakıcı

Murat

2019

Advances in Civil Engineering

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Sustainable management of traffic flows at signalized intersections is an important issue in terms of traffic engineering. The minimization of lost time, emission, fuel consumption, etc., can be achieved by optimization-based intersection management. In this study, a new traffic signal control model is developed for the management of three-leg signalized intersections. In the proposed model, signal timing and signal phasing are optimized simultaneously using Differential Evolution (DE) algorithm which is one of the population-based metaheuristic algorithms. The effectiveness of the model is tested on sample traffic scenarios with VISSIM simulation software considering average vehicle delay performance criteria. Results show that the proposed approach may reduce the average vehicle delay between the rates of 28%–42% and 3%–38% comparing to the optimum fixed-time signal control and vehicle-actuated signal control for tested scenarios, respectively.

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Adaptive traffic signal control based on bio-neural network

Cited by 24 publications

References 10 publications

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Integration of Model‐Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

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