Validating crowdsourced bicycling mobility data for supporting city planning

References

• ABS (Australian Bureau of Statistics). 2011. Journey to Work Survey. Australian Government: Canberra.
   Google Scholar
• Batty, M. 2013. “Big Data, Smart Cities and City Planning.” Dialogues in Human Geography 3 (3): 274–279.10.1177/2043820613513390
   PubMedGoogle Scholar
• Blanc, B., M. Figliozzi, and K. Clifton. 2016. “How Representative of Bicycling Populations are Smartphone Application Surveys of Travel Behavior?” Transportation Research Record: Journal of the Transportation Research Board 2587: 78–89.10.3141/2587-10
   Google Scholar
• Casello J. M., K. C. Rewa, and A. Nour. 2012. “An Analysis of Empirical Evidence of Cyclists’ Route Choice and their Implications for Planning.” Transportation Research Board 91st annual meeting, Washington, DC, January 22–26.
   Google Scholar
• Conrad, C., and K. G. Hilchey. 2011. “A Review of Citizen Science and Community-based Environmental Monitoring: Issues and Opportunities.” Environmental Monitoring and Assessment 176 (1–4): 273–291.10.1007/s10661-010-1582-5
   PubMed Web of Science ®Google Scholar
• Davern. 2008. “Representative Sample.” In Encyclopedia of Survey Research Methods, edited by P. J. Lavrakas. Newbury Park, CA: Sage. doi:10.4135/9781412963947.
   Google Scholar
• Dill, J. 2009. “Bicycling for Transportation and Health: The Role of Infrastructure.” Journal of Public Health Policy 30: S95–S110.10.1057/jphp.2008.56
   PubMed Web of Science ®Google Scholar
• Ellison, R. B., and S. Greaves. 2011. “Travel Time Competitiveness of Cycling in Sydney, Australia.” Transportation Research Record: Journal of the Transportation Research Board 2247: 99–108.10.3141/2247-12
   Web of Science ®Google Scholar
• Flint, E., and S. Cummins. 2016. “Active Commuting and Obesity in Mid-life: Cross-sectional, Observational Evidence from UK Biobank.” The Lancet Diabetes & Endocrinology 4 (5): 420–435.10.1016/S2213-8587(16)00053-X
   PubMed Web of Science ®Google Scholar
• Harris, R. 2016. Quantitative Geography: The Basics. Los Angeles, CA: Sage.
   Google Scholar
• de Hartog, J. J., H. Boogaard, H. Nijland, and G. Hoek. 2010. “Do the Health Benefits of Cycling Outweigh the Risks?” Environmental Health Perspectives 118: 1109–1116. doi:10.1289/ehp.0901747.
   PubMed Web of Science ®Google Scholar
• Hecht, B., and M. Stephens. 2014. “A Tale of Cities: Urban Biases in Volunteered Geographic Information.” ICWSM 14: 197–205.
   Google Scholar
• Heinen, E., K. Maat, and B. van Wee. 2011. “The Role of Attitudes Toward Characteristics of Bicycle Commuting on the Choice to Cycle to Work over Various Distances.” Transportation Research Part D: Transport and Environment 16: 102–109.10.1016/j.trd.2010.08.010
   Web of Science ®Google Scholar
• Hood, J., E. Sall, and B. Charlton. 2011. “A GPS-based Bicycle Route Choice Model for San Francisco, California.” Transportation Letters: The International Journal of Transportation Research 3: 63–75.10.3328/TL.2011.03.01.63-75
   Web of Science ®Google Scholar
• Hudson, J. G., J. C. Duthie, K. Rathod, K. A. Larsen, and L. Meyer Joel. 2012. Using Smartphones to Collect Bicycle Travel Data in Texas. College Station, TX: University Transportation Center for Mobility. Texas Transport Institute.
   Google Scholar
• Izadpanahi P., Leao S. Z., Lieske S. N., Pettit C., 2017. “Factors Motivating Bicycling in Sydney: Analysing Crowdsourced Data.” In Proceedings of 33rd PLEA, International Conference on Passive and Low Energy Architecture, Volume III, Transport, edited by L. Brotas, S. Roaf, F. Nicol, 1355–1362, Edinburgh, UK, July 3–5.
   Google Scholar
• Lane, N. D., E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. 2010. “A Survey of Mobile Phone Sensing.” IEEE Communications Magazine 48 (9): 140–150.10.1109/MCOM.2010.5560598
   Web of Science ®Google Scholar
• Leao, S. Z., S. N. Lieske, L. Conrow, J. Doig, V. Mann, and C. Pettit. 2017. “Building a National-Longitudinal Geospatial Bicycling Data Collection from Crowdsourcing.” Urban Science 1 (3): 23. doi:10.3390/urbansci1030023.
   Google Scholar
• Leao S. Z., and C. Pettit, 2017. “Mapping Bicycle Patterns with an Agent-based Model, Census and Crowdsourced Data.” In Agent-based Modelling of Urban Systems, Lecture Notes in Artificial Intelligence, edited by Padgham Namazi-Rad, Nagel Perez, and Bazzan, 112–128. Switzerland: Springer.
   Google Scholar
• Lindsay, G., A. Macmillan, and A. Woodward. 2011. “Moving Urban Trips from Cars to Bicycles: Impact on Health and Emissions.” Australian and New Zealand Journal of Public Health 35: 54–60.10.1111/j.1753-6405.2010.00621.x
   PubMed Web of Science ®Google Scholar
• NSW Government. 2013. Sydney’s Cycling Future: Cycling for Everyday Transport. NSW: Transport for NSW, December.
   Google Scholar
• Pettit, C., S. N. Lieske, and S. Z. Leao. 2016. “Big Bicycle Data Processing: From Personal Data to Urban Applications.” ISPRS XXIII Congress (2016), Prague, Czech Republic, July 12–19.
   Google Scholar
• Pucher, J., and R. Buehler. 2008. “Making Cycling Irresistible: Lessons from The Netherlands.” Denmark and Germany. Transport Reviews 28: 495–528.
   Web of Science ®Google Scholar
• Pucher, J., J. Garrard, and S. Greaves. 2011. “Cycling Down Under: A Comparative Analysis of Bicycling Trends and Policies in Sydney and Melbourne.” Journal of Transport Geography 19: 332–345.10.1016/j.jtrangeo.2010.02.007
   Web of Science ®Google Scholar
• Rose, A. N., and N. N. Nagle. 2017. “Validation of Spatiodemographic Estimates Produced through Data Fusion of Small Area Census Records and Household Microdata.” Computers, Environment and Urban Systems 63: 38–49.10.1016/j.compenvurbsys.2016.07.006
   Web of Science ®Google Scholar
• Segadilha, A. B. P., and S. da Penha Sanches. 2014. “Analysis of Bicycle Commuter Routes Using GPSs and GIS.” Procedia – Social and Behavioral Sciences 162: 198–207.10.1016/j.sbspro.2014.12.200
   Google Scholar