Abstract
Road traffic snarl-up is a major issue in metropolitan area of both developing and developed countries. In order to diminish this imperative, traffic congestion states of road systems are assessed, so that congested path can be avoided and flipside path can be chosen while traveling from one place to another. Information’s are gathered by the GPS gadgets and offers new open doors for traffic and route prediction, particularly in urban city systems. The core purpose of this research work is to build up an Android application which gives a deliberate approach in providing the best route between a source and destination to the drivers so that driver will not be caught in the traffic. Android application uses Machine Learning algorithms. In this paper, Hidden Markov Model (HMM) is used for predicting traffic states which performs better and more robust than the other models. The best path from source to destination is predicted using Viterbi algorithm taking into the account of road traffic at the time and the driver will be directed to the best path. This application takes Json request as input to interface with the local server through Internet for predicting the traffic state and the best path. The output is returned back from the server as a Json response to the Android application.
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Suvitha, D., Vijayalakshmi, M., Mohideen Sameer, P.M. (2018). Traffic Prediction Using Viterbi Algorithm in Machine Learning Approach. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_25
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DOI: https://doi.org/10.1007/978-981-10-8657-1_25
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