Traffic Control on Cruise Ships for Autonomous Vehicles Applying Artificial Neural Network

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles

Information ◽

10.3390/info12090346 ◽

2021 ◽

Vol 12 (9) ◽

pp. 346

Author(s):

Shuang Zhang ◽

Peng Jing ◽

Gang Xu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Structural Equation Model ◽

Autonomous Vehicles ◽

Structural Equation ◽

Autonomous Driving ◽

Equation Model ◽

Use Of Technology ◽

Artificial Neural ◽

Input Variables

The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which compensates for the linear problem of the structural equation model and ensures the accuracy of the input variables of the artificial neural network. In order to summarize the influencing factors of autonomous vehicles acceptance, the Unified Theory of Acceptance and Use of Technology model was expanded by adding two variables: risk expectation and consumer innovation. The results show that social influence is the strongest predictor of the acceptance of independent autonomous vehicles. The most significant factor of the cooperative vehicle-highway autonomous vehicles’ acceptance is effort expectation. Additionally, risks, performance, existing traffic conditions, and personal innovation awareness also significantly affect autonomous driving acceptance. The research results can provide a theoretical basis for technology developers and industry managers to develop autonomous driving technology and policymaking.

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Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.919-921.1063 ◽

2014 ◽

Vol 919-921 ◽

pp. 1063-1074

Author(s):

Yung Ching Lin ◽

Lee Kuo Lin ◽

Shao Hong Tsai

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Traffic Control ◽

Air Traffic Control ◽

Flight Time ◽

Air Traffic ◽

Artificial Neural

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

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Multi Layer Perception Type Artificial Neural Network based Traffic Control

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i5/87267 ◽

2016 ◽

Vol 9 (5) ◽

Cited By ~ 4

Author(s):

J. Mohamed Rizwan ◽

P. Navaneetha Krishnan ◽

R. Karthikeyan ◽

S. Rakesh Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Control ◽

Artificial Neural

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Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

Sustainability ◽

10.3390/su131910704 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10704

Author(s):

Isaac Oyeyemi Olayode ◽

Lagouge Kwanda Tartibu ◽

Modestus O. Okwu ◽

Alessandro Severino

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Control ◽

Ann Model ◽

The South ◽

Traffic Lights ◽

Testing Performance ◽

Road Intersections ◽

Artificial Neural

The accurate and effective prediction of the traffic flow of vehicles plays a significant role in the construction and planning of signalized road intersections. The application of artificially intelligent predictive models in the prediction of the performance of traffic flow has yielded positive results. However, much uncertainty still exists in the determination of which artificial intelligence methods effectively resolve traffic congestion issues, especially from the perspective of the traffic flow of vehicles at a four-way signalized road intersection. A hybrid algorithm, an artificial neural network trained by a particle swarm optimization model (ANN-PSO), and a heuristic Artificial Neural Network model (ANN) were compared in the prediction of the flow of traffic of vehicles using the South Africa transportation system as a case study. Two hundred and fifty-nine (259) traffic datasets were obtained from the South African road network using inductive loop detectors, video cameras, and GPS-controlled equipment. For the ANN and ANN-PSO training and testing, 219 traffic data were used for the training, and 40 were used for the testing of the ANN-PSO model, while training (160), testing (40), and validation (59) was used for the ANN. The ANN result presented a logistic sigmoid transfer function with a 13–6–1 model and a testing R2 of 0.99169 compared to the ANN-PSO result, which showed a testing performance of R2 0.99710. This result shows that the ANN-PSO model is more efficient and effective than the ANN model in the prediction of the traffic flow of vehicles at a four-way signalized road intersection. Furthermore, the ANN and ANN-PSO models are robust enough to predict traffic flow due to their better testing performance. The modelling approaches proposed in this study will assist transportation engineers and urban planners in designing a traffic control system for traffic lights at four-way signalized road intersections. Finally, the results of this research will assist transportation engineers and traffic controllers in providing traffic flow information and travel guidance for motorists and pedestrians in the optimization of their travel time decision-making.

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