Road traffic prediction and congestion control using Artificial Neural Networks

More, Rohan; Mugal, Abhishek; Rajgure, Sheetal; Adhao, Rahul; Pachghare, Vinod

doi:10.1109/cast.2016.7914939

Cited by 38 publications

(22 citation statements)

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“…The program can tell with almost 85 percent probability if the detected object is a car and 80 % accuracy of the detected object is a motorcycle. Based on these data, it provides a good reference to optimize the system in the future though is slightly lower than [3] [5] . Next the program is run during the night time to get the accuracy when the condition is dark and table 3 shows the accuracy of the data collected on the same location.…”

Section: Resultsmentioning

confidence: 99%

“…[5] developed a system which can theoretically predict the traffic conditions of the road and use congestion control to relief road congestion. The work focused on using Artificial Neural Network to map out number of vehicle on the road and use Jordan's Sequential Network to predict the new condition of the road in real time as well as prediction the future trends.…”

Section: Related Workmentioning

confidence: 99%

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Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Gani

Khalifa

Gunawan

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Gani

Khalifa

Gunawan

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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show abstract

“…Also, there are many AI methods used in transport such as ANNs. ANNs can be used for road planning [19], public transport [20,21] Traffic Incident Detection [22][23][24][25]; and predicting traffic conditions [26][27][28][29][30][31][32][33]. It is classified into supervised and unsupervised learning methods.…”

Section: Applications Of Ai In Transportmentioning

confidence: 99%

“…Another AI technique to predict the traffic flow is presented by [30]. The authors developed a simple recurrent neural network for short-term forecasting using Jordan's neural network.…”

Section: Predictive Modelsmentioning

confidence: 99%

“…was successful in extracting spatial features of traffic flow by using the convolutional neural network and temporal traffic flow features from Recurrent Neural Network. Another AI technique to predict the traffic flow is presented by [30]. The authors developed a simple recurrent neural network for short-term forecasting using Jordan's neural network.…”

Section: Predictive Modelsmentioning

confidence: 99%

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Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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AI‐Enabled Techniques for Intelligent Transportation System for Smarter Use of the Transport Network for Healthcare Services

Meenakshi,

Sharma

2023

Blockchain and Deep Learning for Smart Healthcare

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Road traffic prediction and congestion control using Artificial Neural Networks

Cited by 38 publications

References 1 publication

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Applications of Artificial Intelligence in Transport: An Overview

AI‐Enabled Techniques for Intelligent Transportation System for Smarter Use of the Transport Network for Healthcare Services

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