The Problem of Data Mining in Modelling Traffic Flows in a Megapolis

The article discusses the problems of using data mining in a transport model as a digital platform for analysing data on traffic flows in a megapolis, and prerequisites for creation in future of single data banks and an integrated environment for interaction of models of different levels as clusters of the digital economy, which will consider all modes of transport to assess transport demand and develop projects for organizing traffic in a megapolis.

The objective of the work is to study the processes of obtaining quantitative characteristics of objects of transport modelling when creating a single electronic environment by calculating the derived parameters of the transport network of a megapolis. Quantitative spatial characteristics of an object are associated with calculating the distance from a city centre and a main street and are determined using geographic information systems entailing consequent problem of data unification and efficient data storage.

As part of achieving that objective, it is shown that it is necessary to create a preprocessing and validation procedure for all primary transport data, since data sources have different formats and spatial interpolation of tracking data. For this, it is recommended to use various methods of data analysis based on GIS technologies, digital terrain modelling, topology of the road network and other objects of the transport network of a megapolis. Besides, the use of intelligent data should be preceded by formatting and grouping the source data in real time. The most common errors arise at the stage of the iterative process for obtaining quantitative characteristics of objects of transport modelling and building the optimal route in terms of travel time along a certain transport network.

The existing trends of urban growth require global digitalization of all transport infrastructure objects, considering changes in the functions of the transport environment and in intensity of traffic flows. Theis entails further development and implementation of new information technologies for data processing using neural networks and other digital technologies.

1. GOST R [State Standard] 56670-2015 Intelligent transport systems.Subsystem for monitoring the parameters of traffic flows based on the analysis of telematic data of urban passenger transport [GOST R56670-2015 Intellektualnie transportnie sistemy. Podsistema monitoring parametrov transportnykh potokov na osnove analiza telematicheskikh dannykh gorodskogo passazhirskogo transporta]. [Electronic resource]: http://docs.cntd.ru/document/1200125977. Last accessed 20.07.2020.

2. Vaksman, S. A. Information technologies in management of urban public passenger transport (tasks, experience, problems) [Informatsionnie tekhnologii v upravlenii gorodskim obshchestvennym passazhirskim transportom (zadachi, opyt, problemy)]. Ed. by S. A. Vaksman. Yekaterinburg, Publishing house of AMB, 2012, 250 p. [Electronic resource]: http://www.waksman.ru/Russian/Criticism/Vaksman/Begin.pdf. Last accessed 20.07.2020.

3. Kuftinova, N. G. Modelling the dynamics of traffic flows using cluster analysis [Modelirovanie dinamiki avtotransportnykh potokov s pomoshchyu klasternogo analiza]. Collection of scientific works of IV international scientific and practical conference «Transport planning and modelling», St. Petersburg, 2019, pp. 106–108. [Electronic resource]: https://interactiveplus.ru/e-articles/545/Action545-470172.pdf. Last accessed 20.07.2020.

4. Kuftinova, N. G. Mathematical modelling of traffic flows on the basis of macro- and microapproaches of the urban transport system [Matematicheskoe modelirovanie transportnykh potokov na osnove makro- i mikro-podkhodov transportnoi sistemy]. Collection of scientific works of III international scientific and practical conference «Transport planning and modelling. The digital future of transport management». Ed. by D.Sc. (Eng), Professor S. V. Zhankaziev. Moscow, MADI publ., 2018, pp. 67–76. [Electronic resource]: https://www.elibrary.ru/item.asp?id=37057419. Last accessed 20.07.2020.

5. Kuftinova, N. G. Intelligent transport infrastructure of a megalopolis based on geoanalysis and geo-modelling of motor transport systems [Intellektualnaya transportnaya infrastruktura megapolisa na osnove geoanaliza i geomodelirovaniya avtotransportnykh sistem].Logistic audit of transport and supply chains: Materials of an international scientific and practical conference, Tyumen, TIU publ., 2018, pp. 76–82. [Electronic resource]: https://docplayer.ru/45169767-Udk-informacionno-logicheskaya-modeltransportnoy-seti-megapolisa-kuftinova-n-g.html. Last accessed 20.07.2020.

6. Kuftinova, N. G. General characteristics of transport models for assessing the road network in urban areas [Obshchaya kharakteristika transportnykh modelei dlya otsenki dorozhnoi seti na gorodskikh territoriyakh]. Collection of scientific papers of III international scientific conference «Scientific Discoveries», Karlovy Vary–Moscow. Moscow, 2018, pp. 47–60. [Electronic resource]: https://www.elibrary.ru/item.asp?id=34934683. Last accessed 20.07.2020.

7. Marcus, G., Davis, E. Eight (No, Nine!) Problems with Big Data. [Electronic resource]: https://www.nytimes. com/2014/04/07/opinion/eight-no-nine-problems-withbig-data.html?_r=0. Last accessed 20.07.2020.

8. Chenyang Xu; Changqing Xu; Trieu-Kien Truong. Mining the spatio-temporal pattern using matrix factorisation: A case study of traffic flow. IET Intelligent Transport Systems, 2020, Vol. 14, Iss. 10, pp. 1328–1337. DOI: http://dx.doi.org/10.1049/iet-its.2019.0705. Last accessed 23.10.2020.

9. Alam, O., Kush, A., Emami, A., Pouladzadeh, P. Predicting irregularities in arrival times for transit buses with recurrent neural networks using GPS coordinates and weather data. Journal of Ambient Intelligence and Humanized Computing, 2020. DOI: https://doi.org/10.1007/s12652-020-02507-9. Last accessed 23.10.2020.

10. Guerreiro, G., Figueiras, P., Silva, R., Costa, R., Jardim-Goncalves, R. An architecture for big data processing on intelligent transportation systems. An application scenario on highway traffic flows. 2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016, pp. 65–72. DOI: 10.1109/IS.2016.7737393. Last accessed 20.07.2020.

11. Kohan, M., Ale, J. M. Discovering Traffic Congestion through Traffic Flow Patterns Generated by Moving Object Trajectories. Computers, Environment and Urban Systems, March 2020, Vol. 80, Article 101426. DOI: 10.1016/j.compenvurbsys.2019.101426. Last accessed 20.07.2020.

12. Kumar, B. A., Vanajakshi, L., Subramanian, S. C. A Hybrid Model Based Method for Bus Travel Time Estimation. Journal of Intelligent Transportation Systems, 2018, Vol. 22, Iss. 5, pp. 390–406. DOI: 10.1080/15472450. 2017.1378102. Last accessed 20.07.2020.

13. Taamneh, M., Alkheder, S., Taamneh, S. DataMining Techniques for Traffic Accident Modeling and Prediction in the United Arab Emirates. Journal of Transportation Safety & Security, 2017, Vol. 9, Iss. 2, pp. 146–166. DOI: https://doi.org/10.1080/19439962.20 16.1152338. Last accessed 20.07.2020.

14. Norris, D. RapidMiner – a potential game changer. November 15, 2013. [Electronic resource]: https://www.bloorresearch.com/2013/11/rapidminer-a-potentialgame-changer/. Last accessed 17.07.2020.

15. Yakubovich, A. N., Kuftinova, N. G., Rogova, O. B. Information technologies on vehicles: Study guide [Informatsionnie tekhnologii na avtotransporte: Ucheb. posobie]. Moscow, MADI, 2017, 252 p. [Electronic resource]: http://www.lib.madi.ru/fel/fel1/fel17E429.pdf. Last accessed 17.07.2020.

16. Mousa, S.R., Mousa, R.M., Ishak, S., Radwand, L. Modeling Speed-Density Relation for Highways in Developing Countries with No Lane Discipline: A Case Study in Egypt. 2017 ASCE India Conference, 2018, pp. 725–735. DOI: 10.1061/9780784482025.074. Last accessed 17.07.2020.

17. Kong, X., Das, S., Jha, K., Zhang, Y. Understanding Speeding Behavior From Naturalistic Driving Data: Applying Classification Based Association Rule Mining. Accident Analysis and Prevention, September 2020, Vol. 144. DOI: https://doi.org/10.1016/j.aap.2020.105620. Last accessed 23.10.2020.