Traffic Prediction for Intelligent Transportation System using Machine Learning

Traffic Prediction for Intelligent Transportation System using Machine Learning

Table of Contents


This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about all the above and many more daily life situations which can affect traffic then, a driver or rider can make an informed decision. Also, it helps in the future of autonomous vehicles. In the current decades, traffic data have been generating exponentially, and we have moved towards big data concepts for transportation. Available prediction methods for traffic flow use some traffic prediction models and are still unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow forecast problem build on the traffic data and models. It is cumbersome to forecast the traffic flow accurately because the data available for the transportation system is insanely huge. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyze the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles.

Author Keywords

  • Traffic Environment,
  • Deep Learning,
  • Machine Learning,
  • Genetic Algorithms,
  • Soft Computing,
  • Big Data,
  • Image Processing

IEEE Keywords

  • Machine learning algorithms,
  • Machine learning,
  • Prediction algorithms,
  • Roads,
  • Support vector machines


Various Business sectors and government agencies and individual travellers require precise and appropriately traffic flow information. It helps the riders and drivers to make better travel judgement to alleviate traffic congestion, improve traffic operation efficiency, and reduce carbon emissions. The development and deployment of Intelligent Transportation System (ITSs) provide better accuracy for Traffic flow prediction. It is deal with as a crucial element for the success of advanced traffic management systems, advanced public transportation systems, and traveller information systems. [1]. The dependency of traffic flow is dependent on real-time traffic and historical data collected from various sensor sources, including inductive loops, radars, cameras, mobile Global Positioning System, crowdsourcing, social media. Traffic data is exploding due to the vast use of traditional sensors and new technologies, and we have entered the era of a large volume of data transportation. Transportation control and management are now becoming more data-driven. [2], [3].However, there are already lots of traffic flow prediction systems and models; most of them use shallow traffic models and are still somewhat failing due to the enormous dataset dimension. Recently, deep learning concepts attract many persons involving academicians and industrialist due to their ability to deal with classification problems, understanding of natural language, dimensionality reduction, detection of objects, motion modelling. DL uses multi-layer concepts of neural networks to mining the inherent properties in data from the lowest level to the highest level [4]. They can identify massive volumes of structure in the data, which eventually helps us to visualize and make meaningful inferences from the data. Most of the ITS departments and researches in this area are also concerned about developing an autonomous vehicle, which can make transportation systems much economical and reduce the risk of lives. Also, saving time is the integrative benefit of this idea. In current decades the lots of attention have made towards the safe automatic driving. It is necessary that the information will be provided in time through driver assistance system (DAS), autonomous vehicles (AV)and Traffic Sign Recognition (TSR) [5].

predicting traffic flow information. But these algorithms are not accurate since Traffic Flow involves data having a vast dimension, so it is not very easy to predict accurate traffic flow information with less complexity. We intend to use Genetic, Deep Learning, Image Processing, Machine Learning and also Soft Computing algorithms for prediction of traffic flow since a lot of journals and research paper suggests that they work well when it comes to Big-Data.


deep learning and genetic algorithm is an important problem in data analysis, it has not been dealt with extensively by the ML community. The proposed algorithm gives higher accuracy than the existing algorithms also, It improves the complexity issues throughout the dataset. Also, we have planned to integrate the web server and the application. Also, the things algorithms will be further improved too much higher accuracy.

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The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

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FULL Paper PDF file:

Traffic Prediction for Intelligent Transportation System using Machine Learning



G. Meena, D. Sharma, and M. Mahrishi




Traffic Prediction for Intelligent Transportation System using Machine Learning

Publish in

2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India, 2020, pp. 145-148,



PDF reference and original file: Click here


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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.