A two-stage road traffic congestion prediction and resource dispatching toward a self-organizing traffic control system

Bouyahia, Zied; Haddad, Hedi; Jabeur, Nafaâ; Yasar, Ansar-Ul-Haque

doi:10.1007/s00779-019-01212-5

Cited by 17 publications

(12 citation statements)

References 43 publications

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“…Chen and Grant-Muller [89] investigated the potential of dynamic neural networks to forecast motorway traffic in normal and abnormal conditions, highlighting the importance of RANs. Bouyahia et al [90] used the Markov Random Field (MRF) to model and predict the spread of traffic congestion and the Markov Decision Process (MDP) to allocate traffic resources, showing good improvement in accuracy. To further improve the performance, Cui et al [91] used a RAN in road traffic prediction by employing a controller of the network slice that periodically collected the information to predict future road traffic and applied the Bayesian Attractor Model (BAM) to estimate the required resources.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

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

Reza

Oliveira

Machado

et al. 2021

Sensors

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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.

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

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

Reza

Oliveira

Machado

et al. 2021

Sensors

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“…Bouyahia et al [117] used more advanced techniques, which are the Markov random field and Markov decision process approaches. The authors created a simulation which aimed at optimising a traffic network created from 217 intersections.…”

Section: Literature Analysismentioning

confidence: 99%

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Rydzewski¹,

Czarnul²

2020

IET intell. transp. syst

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Over the past few decades, the increasing number of vehicles and imperfect road traffic management have been sources of congestion in cities and reasons for deteriorating health of its inhabitants. With the help of computer simulations, transport engineers optimise and improve the capacity of city streets. However, with an enormous number of possible simulation types, it is difficult to grasp valuable, innovative solutions which are of the greatest value to city citizens. In this work, the authors expose various problems within this area having reviewed and analysed over 130 papers selected out of 1200 works in the field of urban simulations. The study describes the selection process of important papers and highlights characteristics of microsimulations, macrosimulations, computation optimisations and other approaches found in the literature which are especially useful and should be further built on in the future. They present and compare results provided in reviewed works in terms of throughput improvement, queue, waiting and travel time reduction, vehicle speed increase, speed‐ups as well as assumed simulation parameters. Finally, they focus on research gaps, such as a small number of works considering crisis simulations, few real‐world scale simulations as well as on software architectural changes and low‐level optimizations.

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“…The penalty area of QL is to absorb a policy, which expresses an agent pardons action to take under what surroundings that does not even necessitate a model of the environment and it can grip difficulties with stochastic transitions and plunders, deprived from necessitating adaptations [20], [120]. [23] IoT representation annotation [24] Data-driven management [25] Data and Feedback validation [26] Visualization and understanding [27] Learning environment detection [28] Fraud detection [29] Prediction of the performance [50] Classification of capability [51] Tolerance related acquisition [52] IoT crime forensics [53] Fraud detection in IoT application [54] IoT decision process and making [55] LA Intrusion prediction [30] IoT representation annotation [31] Data-driven management [32] Data and Feedback validation [33] Visualization and understanding [34] Learning environment detection [35] Fraud detection [36] Predicting Software Defects on IoTs [56] Prediction of behavioral changes [57] Signature verification [58] Analysis and decisions [59] Auto-selection of IoT task [60] Traffic incident detection [61] Telecommunication [62] Internet networks [63] MDP Intrusion prediction [37] IoT representation annotation [38] Data-driven management [39] Data and Feedback validation [40] Visualization and understanding [41] Learning environment detection [42] Fraud detection [43] Re...…”

Section: Q-learningmentioning

confidence: 99%

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

Shafik

Matinkhah

Etemadinejad

et al. 2020

JOIV : Int. J. Inform. Visualization

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Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designing, a model implementation based on the bottlenecks currently assessed with an evaluation of the most fashionable hands-on utility of current methods for reinforcement learning.

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A two-stage road traffic congestion prediction and resource dispatching toward a self-organizing traffic control system

Cited by 17 publications

References 43 publications

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

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

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

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