Intellectual Algorithms for the Digital Platform of “Smart” Transport
In conformity with the concept of a “smart” city, resources of all municipal services are to be used in an optimum manner thereby ensuring maximum comfort for all city dwellers. It particularly concerns city passenger transport, the basic part of a “smart” city, for which development of an intellectual digital platform allowing operational management of transport processes as well as reaction to events in real time is essential at present.
Development of proposals and recommendations in improving and developing the existing intellectual digital solutions is essential for the municipality of Samara, as available software products use simplified optimization models and do not take into account the existing restrictions, therefore leading to results which do not meet the demands of city dwellers in the full extent.
This research is devoted to the development of the digital transport platform allowing city dwellers to obtain up-to-date information about city transport and about the possibility of optimizing their routes. In this research analysis of various models suitable for the solution of the mentioned aim is conducted, models suitable for the formulated tasks under the conditions of the most significant restrictions are chosen for the purpose of optimization of passenger flows and creating an intellectual system.
Passenger flow processes based on dynamic modeling which are analyzed in this research envisage using models based on the concept of metaheurustic, the latter being the particle swarm method, the ant colony algorithm, iteration technique, the combinatorial method, etc. An algorithm of using these methods with a variant of modified transport infrastructure worked out with due regard to the changing requirements to passenger traffic is also proposed. The intellectual system created on the basis of the chosen models and algorithms will allow obtaining the necessary predictive information for city dwellers using public transport.
KeywordsDigitization Dynamic programming Intellectual system Municipality Optimization of passenger flows Passenger flow processes Passenger traffic “Smart” city
To Kopeikin Sergei Vladimirovich – Doctor of Engineering Science, Professor of Samara State University of Transport for valuable advice in modeling.
- 1.Krushel EG et al (2015) Management of passenger transport in a small town. Models and algorithms. Volgograd State Technical University, VolgogradGoogle Scholar
- 2.Madar ON (2017) Simulation of demand for transport services in the suburban gravity zone of passenger flows for long-distance passenger trains. Scientific trends: issues of exact and technical Sciences. https://doi.org/10.18411/spc-12-10-2017-13
- 3.Parluk EG (2014) Imitation modeling of passenger flows in the system “Railway-city public transport”. Bulletin of Rostov State University of Transport. Rostov State Transport University (Rostov-on-Don). ISSN: 0201-727X №3(55):78–82Google Scholar
- 4.Turpisheva MS, Nurgaliev ER, Jahyaeva SB (2017) Research of the processes of passenger transportation by automobile transport. Bulletin of Astrakhan State Technical University. Astrakhan State Technical University (Astrakhan). ISSN: 1812–9498. №1(63):56–61Google Scholar
- 5.Tutigin RA, Popov VN (2016) Measurement of parameters of passenger flows for the purposes of modeling. Advanced topics of metrological assurance of scientific and practical activity. Northern (Arctic) Federal University named after M.V. Lomonosov (Arkhangelsk). ISBN: 978-5-261-01201-6. №2.:155–160Google Scholar
- 6.Fomicheva OE, Barzikov KV (2013) Agent and the model of its behavior in the multi-agent environment of modeling passenger flows. Mining information-analytical bulletin (scientific and technical journal). Ltd «Mountain book»(Moscow). ISSN: 0236–1493. №S5:193–195Google Scholar
- 7.Samara transport operator. http://tosamara.ru (date of reference: 03.05.2018)
- 8.Samarastat. http://samarastat.gks.ru (date of reference: 03.05.2018)
- 9.Anagnostopoulou E et al (2018) From mobility patterns to behavioural change: leveraging travel behaviour and personality profiles to nudge for sustainable transportation. J Intell Inform Syst. https://doi.org/10.1007/s10844-018-0528-1
- 10.Byun J (2016) Smart city implementation models based on IoT technology. Adv Sci Technol Lett. https://doi.org/10.14257/astl.2016.129.41
- 11.Huang L et al (2018) A multi-objective optimization model for determining the optimal standard feasible neighborhood of intelligent vehicles. Trends Artif Intell. https://www.springer.com/gp/book/9783319973036. August 28–31 2018