Nuevas fuentes y retos para el estudio de la movilidad urbana

Referencias bibliográficas

• Baker, R., y Thomas, L. (2012). “Modelling real people and creating better place and movement plans: irrationality, Big Data and increasing access to choice”. LTT’s Modelling World 2012.
• Badger, E. (2012). “You already own the most important transportation planning tool”. http://www.citylab.com/tech/2012/02/you-already-own-next-most-important-transportation-planning-tool/1124/ [Consulta: 20 de octubre de 2016].
• Banister, D. (2008). “The sustainable mobility paradigm”. Transport Policy, 15(2), 73–80.
• Banister, D. (2011). “Cities, mobility and climate change”. Journal of Transport Geography, 19(6), 1538–1546.
• Batty, M. (2013). “Big data, smart cities and city planning”. Dialogues in Human Geography, 3(3), 274–279.
• Birkin, M., Harland, K., Malleson, N., Cross, P., y Clarke, M. (2014). “An Examination of Personal Mobility Patterns in Space and Time Using Twitter”. International Journal of Agricultural and Environmental Information Systems, 5(3), 55–72.
• Blanford, J. I., Huang, Z., Savelyev, A., y MacEachren, A. M. (2015). “Geo-located tweets. Enhancing mobility maps and capturing cross-border movement”. PLoS ONE, 10(6), 1–16.
• Bosque, J. (2015). “Neogeografía , Big Data y Tig : Problemas y nuevas posibilidades”. Polígonos, 27(2007), 165–173.
• Bregman, S. (2012). “TCRP Synthesis 99: Uses of Social Media in Public Transportation”. http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_syn_99.pdf [Consulta: 18 de septiembe de 2016]
• Caceres, N., Romero, L. M., Benitez, F. G., y del Castillo, J. M. (2012). “Traffic Flow Estimation Models Using Cellular Phone Data”. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1430–1441.
• Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., y Soltani, K. (2014). “A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data”. Computers, Environment and Urban Systems, 51, 70–82.
• Ciuccarelli, P., Lupi, G., y Simeone, L. (2014). “Visualizing the Data City Social Media as a Source of Knowledge for Urban Planning and Management”. https://doi.org/10.1007/978-3-319-02195-9
• Clarke, M. (2016). “Big Data in Transport”. Institution of Engineering and Technology Sectors Insights, 1–70.
• Dewulf, B., Neutens, T., Vanlommel, M., Logghe, S., De Maeyer, P., Witlox, F., … Van de Weghe, N. (2015). “Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times”. Journal of Transport Geography, 48, 41–51.
• Europe, E. (2015). “Big Data Europe for Smart , Green and Integrated Transport 1st Workshop Report”. 22th World Congress on ITS.
• Fang, Z., Shaw, S. L., Tu, W., Li, Q., y Li, Y. (2012). “Spatiotemporal analysis of critical transportation links based on time geographic concepts: A case study of critical bridges in Wuhan, China”. Journal of Transport Geography, 23, 44–59.
• Finnis K, K., y Walton, D. (2007). “Field observations of factors influencing walking speeds”. International Conference on Sustainability Engineering and Science, 2Nd, 2007, Auckland, New Zealand, 13P.
• Frias-martinez, V., Soto, V., Hohwald, H., y Frias-martinez, E. (2012). “Characterizing Urban Landscapes using Geolocated Tweets”. 2012 International Conference on Social Computing.
• Gao, S., Yang, J., Yan, B., Hu, Y., Janowicz, K., y McKenzie, G. (2014). “Detecting Origin-Destination Mobility Flows From Geotagged Tweets in Greater Los Angeles Area”. Eight International Conference on Geographic Information Science, 1–5.
• García-Palomares, J. C. (2010). “Urban sprawl and travel to work: the case of the metropolitan area of Madrid”. Journal of Transport Geography, 18(2), 197–213.
• García-Palomares, J. C., Gutiérrez, J., y Mínguez, C. (2015). “Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS”. Applied Geography, 63, 408–417.
• Gasparini, A., y Guidicini, P. (1990). Innovazione tecnologica e nuovo ordine urbano. Milán: Angeli.
• GHD. (2014). “New traffic data sources – An overview”. New Data Sources for Transport Workshop, BITRE, 29.
• Graham, M., y Shelton, T. (2013). “Geography and the future of big data, big data and the future of geography”. Dialogues in Human Geography, 3(3), 255–261.
• Griffiths, R., y Richardson, A. J. (2000). “Travel Surveys”. Transportation in the New Millenium.
• Gutiérrez-Puebla, J., García-Palomares, J. C., y Salas-Olmedo, M. H. (2016). “Big (Geo) Data en Ciencias Sociales: Retos y Oportunidades”. Revista de Estudios Andaluces, 33(331), 1–23.
• Hanabusa, H. (2012). “Development of Nowcast Traffic Simulation System for Road Traffic in Urban Area”. 20th World Congress on ITS, 10, 3–10.
• Hasan, S., Zhan, X., y Ukkusuri, S. V. (2013). “Understanding Urban Human Activity and Mobility Patterns Using Large-scale Location-based Data from Online Social Media”. Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, 6:1--6:8.
• Kitchin, R. (2013). “Big data and human geography: Opportunities, challenges and risks”. Dialogues in Human Geography, 3(3), 262–267.
• Kwan, M., y Lee, J. (2011). “Visualisation of Socio Spatial Isolation Based on Human Activity Patterns and Social Networks in Space Time”. Tijdschrift Voor Economische En Sociale Geografie.
• Lansley, G., y Longley, P. A. (2016). “The geography of Twitter topics in London”. Computers, Environment and Urban Systems, 58, 85–96.
• Lathia, N., Smith, C., Froehlich, J., y Capra, L. (2013). “Individuals among commuters: Building personalised transport information services from fare collection systems”. Pervasive and Mobile Computing, 9(5), 643–664.
• Lee, J. H., y Lee, J. H. (2015). “Can Twitter data be used to validate travel demand models?”95th Annual Transportation Research Board Meeting, 1–27.
• Lenormand, M., Louail, T., Cantú-Ros, O. G., Picornell, M., Herranz, R., Arias, J. M., … Ramasco, J. J. (2015). “Influence of sociodemographics on human mobility”. Scientific Reports, 5(8557), 10075.
• Lenormand, M., Picornell, M., Cantú-Ros, O. G., Tugores, A., Louail, T., Herranz, R., … Ramasco, J. J. (2014). “Cross-checking different sources of mobility information”. PLoS ONE, 9(8), 30–38.
• Long, Y., y Shen, Z. (2015). “Profiling Underprivileged Residents with Mid-term Public Transit Smartcard Data of Beijing”. Geospatial Analysis to Support Urban Planning in Beijing (pp. 169–192). Springer International Publishing.
• Longley, P. A., y Adnan, M. (2016). “Geo-temporal Twitter demographics”. International Journal of Geographical Information Science, 30(2), 369–389.
• Luo, F., Cao, G., Mulligan, K., y Li, X. (2016). “Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago”. Applied Geography, 70, 11–25.
• Luong, T. T. B., y Houston, D. (2015). “Public opinions of light rail service in Los Angeles , an analysis using Twitter data”. iConference 2015 Proceedings, 2–5.
• Martin, D. J., Jordan, H., y Roderick, P. (2008). “Taking the bus: Incorporating public transport timetable data into health care accessibility modellin”g. Environment and Planning A, 40(10), 2510–2525.
• McKinsey & Company. (2011). “Big data: The next frontier for innovation, competition, and productivity”. McKinsey Global Institute, (June), 156.
• Miralles-Guasch, C. (2012). “Las encuestas de movilidad y los referentes ambientales de los transportes”. Eure, 38(115), 33–45.
• Miralles-Guasch, C., Delclòs, X., y Vich, G. (2015). “Nuevas fuentes de información para el análisis de la movilidad cotidiana: de las encuestas de movilidad a las aplicaciones para móviles”. XXIV Congreso de La Asociación de Geógrafos Españoles, 2055–2063.
• Miralles-Guasch, C., y Martínez, M. (2013). “Las fuentes de información sobre movilidad: la visión de los profesionales. Ejemplo de aplicación de metodología DELPHI”. Revista Transporte Y Territorio, (8), 99–116.
• Moro,E. (2014). “Tweeting and moving around. A day of trips in Spain”. https://vimeo.com/111579945 [Consulta: 23 de octubre de 2016].
• Moro, E. (2016). “Ciudades Movilidad y Social Media”. VII Congreso Estatal RITSI.
• Netto, V. M., Pinheiro, M., Meirelles, J. V., y Leite, H. (2015). “Digital footprints in the cityscape: Finding networks of segregation through Big Data”. International Conference on Location-Based Social Media Data, 1–15.
• OECD. (2015). “Big Data and Transport: Understanding and assessing options”. International Transport Forum.
• Ortúzar S., J. de D., y Willumsen, L. G. (2011). Modelling transport. Oxford: Wiley-Blackwell.
• Pazos, M. (2005). “El estudio de la movilidad diaria en España: limitaciones en las fuentes y alternativas propuestas”. Eria, 66, 85–92.
• Pereira, F. C., Rodrigues, F., y Ben-Akiva, M. (2015). “Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios”. Journal of Intelligent Transportation Systems, 19(3), 273–288.
• Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., y Srivastava, M. (2010). “Using mobile phones to determine transportation modes”. ACM Transactions on Sensor Networks, 6(2), 1–27.
• Romanillos, G., y Zaltz Austwick, M. (2015). “Madrid cycle track: visualizing the cyclable city”. Journal of Maps,12(5), 1–9.
• Sareh, H. A., Mohammad Ali, N., y Khosravi Farsani. (2012). “Evolution of the World Wide Web : From Web 1.0 to Web 4.0”. International Journal of Web & Semantic Technology, 3(1), 1–10.
• Schwanen, T. (2016). “Geographies of transport II: Reconciling the general and the particular”. Progress in Human Geography, 1-10.
• Serras, J., Bosredon, M., Herranz, R., y Batty, M. (2014). “Urban Planning and Big Data - Taking LUi Models to the Next Level?” http://www.nordregio.se/en/Metameny/Nordregio-News/2014/Planning-Tools-for-Urban-Sustainability/Reflection/ [Consulta: 11 de octubre de 2016].
• Shekhar, S., Evans, M. R., Gunturi, V., y Yang, K. (2012). “Spatial big-data challenges intersecting mobility and cloud computing”. MobiDE 2012 - Proceedings of the 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access - In Conjunction with ACM SIGMOD / PODS 2012, 1(c), 1–6.
• Shelton, T., Poorthuis, A., y Zook, M. (2015). “Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information”. Landscape and Urban Planning, 142, 198–211.
• Steiger, E., Westerholt, R., Resch, B., y Zipf, A. (2015). “Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data”. Computers, Environment and Urban Systems, 54, 255–265.
• Steur, R. (2014). “Twitter as a spatio-temporal information source for traffic incident management”. Geographical Information Management and Applications.
• Sui, D., y Goodchild, M. (2011). “The convergence of GIS and social media: challenges for GIScience”. International Journal of Geographical Information Science, 25(11), 1737–1748.
• Tao, S., Rohde, D., y Corcoran, J. (2014). “Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap”. Journal of Transport Geography, 41(December), 21–36.
• Tascón, M., y Coullaut, A. (2016). Big Data y el internet de las cosas : qué hay detrás y cómo nos va a cambiar. Madrid: Catarata.
• Versichele, M., Neutens, T., Delafontaine, M., y Van de Weghe, N. (2012). “The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities”. Applied Geography, 32(2), 208–220.
• Wu, L., Zhi, Y., Sui, Z., y Liu, Y. (2014). “Intra-urban human mobility and activity transition: Evidence from social media check-in data”. PLoS ONE, 9(5).
• Zhao, F., Ghorpade, A., Pereira, F. C., Zegras, C., y Ben-Akiva, M. (2015). “Quantifying Mobility: Pervasive Technologies for Transport Modeling”. Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 1039–1044.
• Zipf, A. (2015). “Enrichment of volunteered geographic information: some considerations”. RICH-VGI: Enrichment of Volunteered Geographic Information (VGI): Techniques, Practices and Current State of Knowledge, 1–15.