Abstract
One of the main tasks of Intelligent Transportation Systems is to predict state of the traffic from short to medium horizon. This prediction can be used to manage the traffic both to prevent the traffic congestions and to minimize their impact. This information is also useful for route planning. This prediction is not an easy task given that the traffic flow is very difficult to describe by numerical equations. Other possible approach to traffic state prediction is to use historical data about the traffic and relate them to the current state by application of some form of statistical approach. This task is, however, complicated by complex nature of the traffic data, which can, due to various reasons, be quite inaccurate. The paper is focused on finding the algorithms that can exploit valuable information contained in traffic data from Czech Republic highways to make a short term traffic speed predictions. Our proposed algorithm is based on hidden Markov models (HMM), which can naturally utilize data sources from Czech Republic highways.
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Acknowledgments
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project ‘IT4Innovations excellence in science—LQ1602’, and supported by ‘Transport Systems Development Centre’ co-financed by Technology Agency of the Czech Republic (reg. no. TE01020155) and co-financed by the internal grant agency of VŠB Technical University of Ostrava, Czech Republic, under the project no. SP2016/166 ‘PC Usage for Analysis of Uncertain Time Series II’.
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Rapant, L., Slaninová, K., Martinovič, J., Martinovič, T. (2016). Traffic Speed Prediction Using Hidden Markov Models for Czech Republic Highways. In: Jezic, G., Chen-Burger, YH., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technology and Applications. Smart Innovation, Systems and Technologies, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-319-39883-9_15
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DOI: https://doi.org/10.1007/978-3-319-39883-9_15
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