Trip volume seasonal variations at regional level – case study of Małopolska GSM OD matrices

• Autorzy: Kucharski R. , Szarata A. , Mielczarek J. , Drabicki A.
• Języki publikacji: EN

Abstrakty

EN

In this paper we analyze the big-data set of GSM origin-destination matrices collected between 360 districts (Powiat) in Poland to indirectly observe the trip volume fluctuations: weekly and seasonal. We utilized the dataset obtained from BTS position for all clients registered to a single provider. The data was collected over several days, which allows for a valuable temporal analysis of trip volumes. We analyze internal and external (inbound and outbound) trips in Małopolska region, intra-zonal trips (within district), inter-zonal (between districts of Małopolska). We discuss the general variability of observed trip volumes. We present the weekly fluctuations (working day, Friday, Saturday, Sunday) and the seasonal ones (winter, holiday, etc.). We verify the results obtained from GSM data with the traffic counts and their seasonal variations provided by national road administration (GDDKiA). Main contribution of the paper is presenting the observed fluctuations from the GSM data and comparing them with the classically collected data, as we demonstrate the results are comparable.

Słowa kluczowe

EN

demand model; floating car data; GSM; seasonal variations; GSM OD

PL

model popytu; dane z sondowania pojazdów; GSM; wahania sezonowe; GSM OD

• Wydawca: Polskie Stowarzyszenie Telematyki Transportu
• Czasopismo: Archives of Transport System Telematics
• Rocznik: 2018
• Tom: Vol. 11, iss. 1
• Strony: 40--45
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Kucharski R.

• CRACOW UNIVERSITY OF TECHNOLOGY, Warszawska 24 31-155, Kraków, Poland

autor

Szarata A.

• CRACOW UNIVERSITY OF TECHNOLOGY, Warszawska 24 31-155, Kraków, Poland

autor

Mielczarek J.

• CRACOW UNIVERSITY OF TECHNOLOGY, Warszawska 24 31-155, Kraków, Poland

autor

Drabicki A.

• CRACOW UNIVERSITY OF TECHNOLOGY, Warszawska 24 31-155, Kraków, Poland

Bibliografia

• [1] ANTONIOU C., BALAKRISHNA R., KOUTSOPOULOS H.N.: A synthesis of emerging data collection technologies and their impact on traffi c management applications. European Transport Research Review, 3(3), 2011, p.139-148
• [2] BECKER R.A., et al.: Route classifi cation using cellular handoff patterns. In: Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, Beijing, China, 2011
• [3] CALABRESE F., et al.: Real-time urban monitoring using cellphones: a case study in Rome. IEEE Transactions on Intelligent Transportation Systems 12 (1), 2011, p.141–151
• [4] DEMISSIE M.G., et al.: Intelligent road traffi c status detection system through cellular networks handover information: An exploratory study. Transportation research part C: emerging technologies, 32, 2013, p.76-88
• [5] HOU Z., LI X.: Repeatability and similarity of freeway traffic flow and long-term prediction under big data. IEEE Transactions on Intelligent Transportation Systems, 17(6), 2016, p.1786-1796
• [6] JÄRV O., et al.: Mobile phones in a traffic flow: a geographical perspective to evening rush hour traffi c analysis using call detail records. PloS one, 7(11), e49171, 2012.
• [7] LEDUC G., et al.: Road traffi c data: collection methods and applications. In: Working Papers on Energy, Transport and Climate Change N.1. EUROPEAN Comission-Joint Research Centre-Institute for Prospective Technologica, 2008
• [8] LV Y., et al.: Traffi c fl ow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 2015, p.865-873
• [9] SPŁAWIŃSKA M.: Charakterystyki zmienności natężeń ruchu i ich wpływ na eksploatację wybranych obiektów drogowych: praca doktorska, Politechnika Krakowka, Kraków, 2013
• [10] THIESSENHUSEN K.U., SCHÄFER R.P., LANG T.: Traffi c Data from Cellphon es: A Comparison with Loops and Probe Vehicle Data, 2003
• [11] TOOLE J.L., et al.: Th e path most traveled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies, 58, 2015, p.162-177
• [12] VACCARI A., et al.: A holistic framework for the study of urban traces and the profi ling of urban processes and dynamics. In: 12th International IEEE Conference on Intelligent Transportation Systems. IEEE, St. Louis, MO, 2009
• [13] VALERIO D.: Road Traffi c Information from Cellular Network Signaling. Forschungszentrum Telekommunikation Wien, Vienna, Austria, 2009
• [14] VALERIO D., et al.: Exploiting cellular networks for road traffic estimation: a survey and a research roadmap. In: IEEE 69th Vehicular Technology Conference, Barcelona, Spain, 2009a
• [15] VALERIO D., et al.: Road traffic estimation from cellular network monitoring: A hands-on investigation. In: Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on, Tokyo, 13–16 September 2009b, pp. 3035–3039
• [16] https://www.gddkia.gov.pl/userfiles/articles/s/stacje-ciaglychpomiarow-ruchu-d_26174/R02_05_2016.pdf [date of access: 31.01.2018]
• [17] https://www.gddkia.gov.pl/userfiles/articles/s/stacje-ciaglychpomiarow-ruchu-d_26174/R02_04_2016.pdf [date of access: 31.01.2018]
• [18] https://www.gddkia.gov.pl/userfiles/articles/g/godzinamiarodajna-i-wahania-ruc_24176/Metoda_szacowania_SDRR. pdf [date of access: 31.01.2018]