Where and when - about one approach to traffic analysis in the city

Dmitry Namiot, Andrey Akimov, Mariia Nekraplonna, Oleg Pokusaev

Abstract


This article deals with one model for analyzing urban mobility. Traditionally, the time domain is used in the analysis of movements. This is due to both traditional models of scheduling analysis and the classical approach to representing transport problems as traffic forecasting problems. At the same time, the development of telecommunication technologies and the penetration of smartphones have led to the fact that the movements of mobile devices can accurately measure traffic flows. Accordingly, the prediction of traffic flows is not the most urgent task - there is no need to predict what is being measured accurately. In modern conditions, data on flows are becoming a metric that reflects the processes (situations) in the city. For example, the data on movement between metro stations shows patterns of the use of the corresponding stations, which, in fact, describe the models of the functioning of the adjacent territories: is it a sleeping area where people leave in the morning to work and return in the evening, how are the working hours different on weekdays and weekends? days, etc. And any changes to such templates will signal a change in usage modes. Or, in other words, to be a reflection of some processes in the city. In this article, we are talking about one of the approaches to traffic analysis related to the search for movement patterns.


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References


Misharin, A., D. Namiot, and O. Pokusaev. "On Processing of Correspondence Matrices in Transport Systems." 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2019.

Namiot, Dmitry, et al. "OD-matrix and passenger flow analysis." International Journal of Open Information Technologies 8.4 (2020): 25-30.

Namiot, Dmitry, and Manfred Sneps-Sneppe. "A Survey of Smart Cards Data Mining." AIST (Supplement). 2017.

Bulygin, Mark, and Dmitry Namiot. "On the possibilities of using the data of cellular operators to solve the problems of digital urbanism." International Journal of Open Information Technologies 9.1: 48-57.

Nekraplonna, Mariia, and Dmitry Namiot. "Metro correspondence matrix analysis." International Journal of Open Information Technologies 7.7 (2019): 68-80.

Niu, Ben, et al. "A novel attack to spatial cloaking schemes in location-based services." Future Generation Computer Systems 49 (2015): 125-132.

Bulygin, Mark, and Dmitry Namiot. "Anomaly Detection Method For Aggregated Cellular Operator Data." 2021 28th Conference of Open Innovations Association (FRUCT). IEEE, 2021.

Yang, Chao, Fen Fan Yan, and Xiang Dong Xu. "Clustering Daily Metro Origin-Destination Matrix in Shenzhen China." Applied Mechanics and Materials. Vol. 743. Trans Tech Publications Ltd, 2015.

Duan, Zhengyu, et al. "Understanding multiple days’ metro travel demand at aggregate level." IET Intelligent Transport Systems 13.5 (2018): 756-763.

Yang, Chao, Fenfan Yan, and Satish V. Ukkusuri. "Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system." Transportmetrica A: Transport Science 14.7 (2018): 576-597.

Namiot, Dmitry, Oleg Pokusaev, and Vasily Kupriyanovsky. "On railway stations statistics in Smart Cities." International Journal of Open Information Technologies 7.4 (2019): 19-24.

Pokusaev, Oleg, Alexander Chekmarev, and Dmitry Namiot. "City railways–who are their passengers?." IOP Conference Series: Earth and Environmental Science. Vol. 534. No. 1. IOP Publishing, 2020.


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Abava  Absolutech Convergent 2020

ISSN: 2307-8162