Volume 2, Issue 4, December 2017, Page: 96-101
Influence of the Seasonal Factor on the Long-Distance Passenger Correspondence
Dolia Kostiantyn, Department of GIS, Land and Real Estate Appraisal, O. M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine
Received: Oct. 9, 2017;       Accepted: Oct. 23, 2017;       Published: Dec. 12, 2017
DOI: 10.11648/j.ajdmkd.20170204.11      View  1131      Downloads  72
The process of transportation of passengers on inter-regional routes of general use is investigated in the article. It is established that long-distance passenger transport correspondences have fluctuations that are observed in time. Such variations can be attributed to changes in the volumes or directions of passenger transportation during the day, which are described by many researchers. In addition to the above, it is known that in the system of intercity passenger transport it is possible to observe the existence of processes for the formation of predicted changes when considering the period of transportation during the week. In this case, there is a corresponding change in the characteristics of volumes and directions of passenger correspondence on the days of the week. Similar fluctuations also occur in the consideration of the state of correspondence during the year. Intercity passenger transport systems have as their objective the functioning of a qualitative transport system and safe satisfaction of the needs for the movement of people. The presence of a stable route network scheme can be considered one of the requirements to the quality of passenger service. This leads to the need to take into account the influence of the environment of the functioning of the system in the organization of its functioning, subject to the restrictions.
Transport System, Gravity Model, Seasonal Fluctuations of Transport Correspondences, Long-Distance Transportation
To cite this article
Dolia Kostiantyn, Influence of the Seasonal Factor on the Long-Distance Passenger Correspondence, American Journal of Data Mining and Knowledge Discovery. Vol. 2, No. 4, 2017, pp. 96-101. doi: 10.11648/j.ajdmkd.20170204.11
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