Abstract
Traffic flow management of smart city is one of the most current topics in traffic modeling. We have developed a method based on traffic routing and reordering that is capable of performing this task. One of the inputs of this method is time-dependant probabilistic speed profile, i.e., speed profiles that take into account both the time and uncertainty of traffic speed due to various traffic events and peaks. However, the exact calculation of these profiles for each road is very difficult due to the huge amounts of real-world data required. Therefore, we propose a methodology, which should, by utilizing various available metadata about traffic network and Markov chain model, be capable of producing these probabilistic speed profiles synthetically.
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Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPS II) project ‘IT4Innovations excellence in science—LQ1602’, by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project ‘IT4Innovations National Supercomputing Center LM2015070’, and partially by the SGC grant No. SP2019/108 ‘Extension of HPC platforms for executing scientific pipelines’, VŠB—Technical University of Ostrava, Czech Republic.
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Rapant, L., Szturcová, D., Golasowski, M., Vojtek, D. (2020). Methodology for Generating Synthetic Time-Dependant Probabilistic Speed Profiles. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 1136. Springer, Singapore. https://doi.org/10.1007/978-981-15-2930-6_8
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DOI: https://doi.org/10.1007/978-981-15-2930-6_8
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