Analysis and Control of Intelligent Traffic Signal System Based on Adaptive Fuzzy Neural Network

Dong, Changqing; Yang, Kaixin; Guo, Jinwei; Chen, Xiaowei; Dong, Haibo; Bai, Yunlong

doi:10.1109/ictis.2019.8883791

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimizing cost. We use the same initial parameter values υ 0 = [10,20,30,50,10,10,8,8,5,5] over different traffic conditions (indicated by different Poisson rates) to test how the controller performs. The optimal average waiting time is recorded after 20 times of parameter (υ) updates.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For example, [7] developed a two-layer type-2 fuzzy controller which can not only improve the traffic situation of each intersection but also consider downstream intersections and enlarge the so-called "green wave" band. In [8] an adaptive fuzzy neural network algorithm is used to learn from the historical data and make online adjustments, while [9] used a Genetic Algorithm to adapt the traffic signal time. Moreover, [10] developed a form of decentralized multi-agent RL algorithm that can be applied to large-scale TLC problems showing the capability of achieving lower and more sustainable intersection delays, by distributing the traffic more homogeneously among intersections.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows

Chen¹,

Cassandras²

2023

Preprint

View full text Add to dashboard Cite

We study the Traffic Light Control (TLC) problem for a single intersection, considering both straight driving vehicle flows and corresponding crossing pedestrian flows with the goal of achieving a fair jointly optimal sharing policy in terms of average waiting times. Using a stochastic hybrid system model, we design a quasi-dynamic policy controlling the traffic light cycles with several threshold parameters applied to the light cycles and the partially observed contents of vehicle and pedestrian queues. Infinitesimal Perturbation Analysis (IPA) is then used to derive a data-driven gradient estimator of a cost metric with respect to the policy parameters and to iteratively adjust these parameters through an online gradientbased algorithm in order to improve overall performance on this intersection and adapt the policy to changing traffic conditions. The controller is applied to a simulated intersection in the town of Veberöd, Sweden, to illustrate the performance of this approach using real traffic data from this intersection.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows

Chen¹,

Cassandras²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“… The proposed TSCS can be enhanced to classify disturbances using data mining and clustering techniques. [ 180 ] Verifying the adaptive traffic signal control system is valid and correct. Adaptive Fuzzy Neural Network (AFNN) algorithm.…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

“…Metaheuristic algorithms [ 176 ] optimised the upstream flow, and VISSIM tested the performance. To regulate a traffic light system [ 180 ], employed an adaptive fuzzy neural network and tested its performance in a microsimulation scenario [ 154 ]. used adaptive GA to coordinate traffic signal control; the performance was evaluated through the VISSIM COM interface [ 181 ].…”

Section: Vissim Application Assessment and Evaluationmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Mir and Hassan [149] proposed a neuro-fuzzy-based approach where a Fuzzy Logic System (FLS) was used for model training and an NN was used for the calculation of the green light time, proving the potentiality of an efficient traffic signal control. Dong et al [150] combined an NN and FLS to derive an Adaptive Fuzzy Neural Network (AFNN) algorithm that reduced the delay time by 8.45% with a 24.04% increase in average fuel economy.…”

Section: Neural Network Controllermentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

View full text Add to dashboard Cite

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

show abstract

Analysis and Control of Intelligent Traffic Signal System Based on Adaptive Fuzzy Neural Network

Cited by 6 publications

References 7 publications

Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows

Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Contact Info

Product

Resources

About