Big data is a keyword of current and future trends in transport planning. However, despite recent developments and diffusion of smart technology based e-ticketing (e.g. by access cards) and systems for tracking passenger movements by mobile devices, their use is still not common in European countries. Thus, currently both classical and novel surveying and data processing methods are used to estimate origin-destination (OD) matrices in interurban public transport in some countries like Hungary, where nationwide OD matrices are pillars of strategy-making and planning.
In 2016-2017, nearly ten years after the last update, new interurban public transport passenger OD matrices have been elaborated in Hungary, including all trips on non-local bus and train services on a weekday in autumn 2016. Although public passenger transport services are provided by state-owned companies (a national and a regional railway operator as well as seven regional bus companies) and a handful of small private operators, there is no standardized data collection or integrated ticketing.
Consequently survey methods of cross-sectional passenger counts and personal OD interviews have been combined with electronic ticket sales data to build a database. Trip chains and travel patterns of a sample of more than 100,000 passengers have been surveyed on buses, trains and major interchanges. Data has been processed to generate OD matrices of direct trips using the method of conversion and to integrate (bus↔bus and bus↔train) transfers using probability estimation techniques. Matrices have been estimated by transport mode, on both regional and national level, including a comprehensive national public transport OD matrix.
In this contribution, the novel combination of data collection techniques to realize a nationwide survey, data processing methodology as well as key findings (especially main traffic flows and travel patterns) are presented.

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https://zenodo.org/record/2590945 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
http://dx.doi.org/10.5281/zenodo.2590944 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

DOIS: 10.5281/zenodo.2590945 10.5281/zenodo.2590944

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.2590945
Licence: Other

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