Big Data and Cycling

  1. Romanillos Arroyo, Gustavo
  2. Zaltz Austwick, Martin
  3. Ettema, Dick
  4. De Kruijf, Joost
Revista:
Transport Reviews

ISSN: 0144-1647 1464-5327

Año de publicación: 2015

Volumen: 36

Número: 1

Páginas: 114-133

Tipo: Artículo

DOI: 10.1080/01441647.2015.1084067 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Transport Reviews

Referencias bibliográficas

  • Bargar A., (2014), The 3rd international workshop on urban computing
  • 10.1016/j.trc.2014.03.007
  • 10.1016/j.trc.2011.09.005
  • 10.1016/j.tra.2012.07.005
  • 10.3141/2387-13
  • Charlton B., (2010), CycleTracks: a bicycle route choice data collection application for GPS-enabled smartphones
  • Cintia, P., Pappalardo, L. & Pedreschi, D. (2013).‘Engine matters’: A first large scale data driven study on cyclists’ performance. 2013 IEEE 13th International Conference on Data Mining Workshops (pp. 147–153). IEEE. doi:10.1109/ICDMW.2013.41
  • 10.1109/ISSNIP.2011.6146582
  • van de Coevering P., (2014), Bike print. Policy renewal and innovation by means of tracking technology
  • Coleman E., (2013), Beyond transparency - Open data and the future of civic innovation, pp. 39
  • 10.1016/j.jtrangeo.2014.09.003
  • 10.5038/2375-0901.12.4.3
  • 10.1214/aoms/1177731829
  • 10.3141/2387-15
  • Doherty S. T., (2001), Moving beyond observed outcomes: integrating global positioning systems and interactive computer-based travel behavior surveys
  • Etienne C., (2012), Transportation Research-Part C, 5, pp. 1
  • 10.1016/j.jtrangeo.2014.01.013
  • 10.1080/01441647.2015.1033036
  • 10.1080/01441647.2013.775612
  • 10.1016/j.jtrangeo.2014.08.005
  • Froehlich J., (2009), Twenty-First international joint conference on artificial intelligence (IJCAI-09)
  • 10.1109/CIVTS.2014.7009473
  • 10.1016/j.jtrangeo.2014.04.004
  • Harvey F. J., (2007), Transportation Research Board
  • 10.3328/TL.2011.03.01.63-75
  • Hudson, J. G., Duthie, J. C., Rathod, Y. K., Larsen, K. A. & Meyer, J. L. (2012). Using smartphones to collect bicycle travel data in Texas (No. UTCM 11-35-69). Retrieved from http://utcm.tamu.edu/publications/final_reports/Hudson_11-35-69.pdf
  • Jónasson Á., (2013), Optimizing expenditure on cycling roads using cyclists’ GPS data
  • 10.1016/j.pmcj.2010.07.002
  • 10.1023/A:1013999415003
  • 10.1016/j.trc.2011.12.004
  • 10.1109/ICPPW.2010.70
  • Lindsey G., (2013), Feasibility of using GPS to track bicycle lane positioning
  • 10.1109/ESIAT.2009.298
  • 10.3141/1935-11
  • 10.1038/498255a
  • 10.1016/j.tra.2010.07.008
  • 10.3141/2190-02
  • 10.1016/j.jtrangeo.2013.06.007
  • Ohmori N., (2005), Eastern Asia Society for Transportation Studies, 5, pp. 1104
  • 10.1371/journal.pone.0037754
  • 10.1016/j.trc.2010.12.003
  • 10.1145/1753326.1753598
  • Rietveld P., (2000), Transportation Research Part D, 5, pp. 2
  • Rogers S., (2000), Bicycle counter
  • Romanillos G., (2014), Analysing and mapping the cyclable city. A GPS-based analysis of the real and potential bicycle use in Madrid
  • 10.1145/1810891.1810898
  • 10.3141/1939-10
  • Schuessler N., (2009), Map-matching of GPS traces on high-resolution navigation networks using the Multiple Hypothesis Technique (MHT)
  • 10.3141/2105-04
  • 10.1080/01441647.2014.903530
  • Smith W., (2014), Mobile interactive fitness technologies and the recreational experience of bicycling: A phenomenological exploration of the Strava community
  • The Nielsen Company, (2014), The digital consumer report 2014
  • 10.1016/j.sbspro.2011.08.058
  • Wamsley K., (2014), Optimal power-based cycling pacing strategies for Strava segments (Doctoral dissertation)
  • 10.3138/carto.46.4.239
  • 10.1371/journal.pone.0074685