On passenger flow data models for urban railways
Abstract
The article deals with issues related to the calculation (forecasting) of passenger traffic for urban railways. The article is a survey of research related to forecasting the number of passengers for new lines of urban railways. We consider the data sources for building the forecast, as well as models that can be used to obtain digital estimates. The sources of data discussed in this paper include migration data that can be collected using telecommunications operators, as well as information on the use of public transport, obtained from the validation of transport cards. Also, the paper considers the methods of forecasting passenger traffic, based on the analysis of network topology. The last section is devoted to the visualization of transport data.
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Namiot D. et al. On the assessment of socio-economic effects of the city railway //International Journal of Open Information Technologies. – 2018. – Т. 6. – №. 1. – С. 92-103.
Wang J. et al. Vulnerability analysis and passenger source prediction in urban rail transit networks //PloS one. – 2013. – Т. 8. – №. 11. – С. e80178.
US Census Bureau. Available: http://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.html. Retrieved: Jan, 2018
Bay Area Census http://www.bayareacensus.ca.gov/faq.htm. Retrieved: Jan, 2018
Steenbruggen J. et al. Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities //GeoJournal. – 2013. – Т. 78. – №. 2. – С. 223-243.
Ratti C. et al. Mobile landscapes: using location data from cell phones for urban analysis //Environment and Planning B: Planning and Design. – 2006. – Т. 33. – №. 5. – С. 727-748.
Calabrese F. et al. Real-time urban monitoring using cell phones: A case study in Rome //IEEE Transactions on Intelligent Transportation Systems. – 2011. – Т. 12. – №. 1. – С. 141-151.
Namiot D., Sneps-Sneppe M. On the analysis of statistics of mobile visitors //Automatic Control and Computer Sciences. – 2014. – Т. 48. – №. 3. – С. 150-158.
Ford, MIT use Bostonians’ cellphone location data for traffic planning https://www.computerworld.com/article/3112845/car-tech/ford-mit-use-bostonians-cellphone-location-data-for-city-traffic-planning.html Retrieved: Jan, 2018
Namiot D., Sneps-Sneppe M. Customized check-in procedures //Smart Spaces and Next Generation Wired/Wireless Networking. – 2011. – С. 160-164.
Zhou Y. et al. Time prediction model of subway transfer //SpringerPlus. – 2016. – Т. 5. – №. 1. – С. 44.
Ramli M. A. et al. A method to ascertain rapid transit systems’ throughput distribution using network analysis //Procedia Computer Science. – 2014. – Т. 29. – С. 1621-1630.
Freeman L. C. A set of measures of centrality based on betweenness //Sociometry. – 1977. – С. 35-41.
Altshuler Y. et al. Augmented betweenness centrality for mobility prediction in transportation networks //International Workshop on Finding Patterns of Human Behaviors in NEtworks and MObility Data, NEMO11. – 2011.
Chen W., Guo F., Wang F. Y. A survey of traffic data visualization //IEEE Transactions on Intelligent Transportation Systems. – 2015. – Т. 16. – №. 6. – С. 2970-2984
K. Kloeckl, X. Chen, C. Sommer, C. Ratti, and A. Biderman, “Trains of data.” Available: http://senseable.mit.edu/trainsofdata/
Mike Barry and Brian Card Visualizing MBTA Data. An interactive exploration of Boston's subway system http://mbtaviz.github.io/ Retrieved: Jan, 2018
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