Using Google Maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion
A pilot study from Budapest
Abstract
In recent urban geography literature, smart cites became a fashionable subject. The smart city paradigm is strongly connected to researches based on big data. The main objective of this paper is to strengthen the idea of usefulness of big data in examining and developing the urban transportation system as part of smart cities. In this pilot research, Google Maps traffic estimation data were used to evaluate the special vehicular traffic flows in District 3, in Budapest. On 45 defined road sections, travel time estimation data were collected with the aim to calculate the extent of wasted time in traffic jams. According to our results this source of ‘big data’ is a feasible way of conducting ‘smart’ research on a city road system. The most relevant advantage of this database is that it is continually generated on a high spatial and temporal resolution. The conclusion of this pilot research is that spatial and temporal inequalities are evincible from this database, unrecognized processes can be easily analysed, which can help urban planners to rethink their strategies on urban transport system. The most important findings showed that, on workdays, there is a second wave of peak traffic on many roads, and, within our chosen district, there are congestion hot spot places. It is important to note that Google Maps data have limits, but by understanding them this method is a useful way for geographers to examine urban traffic congestion patterns with a high spatial resolution.
References
Abella-García, A., Ortiz-de-Urbina-Criado, M. and De-Pablos-Heredero, C. 2015. The ecosystem of services around smart cities: An exploratory analysis. Procedia Computer Science 64. 1075–1080. https://doi.org/10.1016/j.procs.2015.08.554
Al Nuaimi, E., Neyadi, H.A., Mohamed, N. and Al-Jaroodi, J. 2015. Applications of big data to smart cities. Journal of Internet Services and Applications 6. (1): 1–15. https://doi.org/10.1186/s13174-015-0041-5
Anttiroiko, A.V. 2013. U-cities reshaping our future: reflections on ubiquitous infrastructure as an enabler of smart urban development. AI & SOCIETY Journal of Knowledge, Culture and Communication 28. (4): 491–507.
Anttiroiko, A.V., Valkama, P. and Bailey, S.J. 2014. Smart cities in the new service economy: building platforms for smart services. AI & SOCIETY Journal of Knowledge, Culture and Communication 29. (3): 323–334.
Bálint, D. and Trócsányi, A. 2016. New ways of mobility: the birth of ridesharing. A case study from Hungary. Hungarian Geographical Bulletin 65. (4): 391–405. https://doi.org/10.15201/hungeobull.65.4.7
Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G. and Portugali, Y. 2012. Smart cities of the future. The European Physical Journal Special Topics 214. 481–518. https://doi.org/10.1140/epjst/e2012-01703-3
Camagni, R., Gibelli, M.C. and Rigamonti, P. 2002. Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion. Ecological Economics 40. (2): 199–216. https://doi.org/10.1016/S0921-8009(01)00254-3
Chin, A.T.H. 1996. Containing air pollution and traffic congestion: Transport policy and the environment in Singapore. Atmospheric Environment 30. (5): 787–801. https://doi.org/10.1016/1352-2310(95)00173-5
Chuan Tao, Y., Zhang, X., Hui, C., JingYuan, W., Daven, C. and Bertrand, D. 2015. A literature survey on smart cities. Science China Information Sciences 58. (10): 1–18. https://doi.org/10.1007/s11432-015-5397-4
Cohen, H. and Southworth, F. 1999. On the measurement and valuation of travel time variability due to incidents on freeways. Journal of Transportation and Statistics 2. (2): 123–131.
Downs, A. 1992. Stuck in Traffic. Washington, Brookings Institution.
Erhart, Sz. 2007. A budapesti közlekedési dugók okai és következményei (The causes and consequences of traffic jams in Budapest). Közgazdasági Szemle 54. (5): 435–458.
Fleischer, T. and Tir, M. 2016. The transport in our time-budget. Regional Statistics 6. (2): 54–94. https://doi.org/10.15196/RS06204
Giffinger, R., Fertner, C., Kramar, H., Meijers, E. and Pichler-Milanovic, N. 2007. Smart cities: Ranking of European medium-sized cities. Vienna, Vienna University of Technology.
Graham, S. 1997. Telecommunications and the future of cities: de-buking the myths. Cities 14. (1): 21–29. https://doi.org/10.1016/S0264-2751(37)00037-8
Greenshields, B.D. 1935. A Study of Traffic Capacity. Highway Research Board Proceedings 14. 448–477.
Keserű, I. 2013. Commuting patterns of secondary school students in the functional urban region of Budaepst. Hungarian Geographical Bulletin 62. (2): 197–219.
Kitchin, R. 2013. Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography 3. (3): 262–267. https://doi.org/10.1177/2043820613513388
Kitchin, R. 2014. The real-time city? Big data and smart urbanism. GeoJournal 79. (1): 1–14. https://doi.org/10.1007/s10708-013-9516-8
Leduc, G. 2008. Road Traffic Data: Collection Methods and Applications. Working Papers on Energy, Transport and Climate Change N. 1, European Commission – Joint Research Centre, Institute for Prospective Technological Studies, 53 p.
Lengyel, B. and Jakobi, Á. 2016. Online social networks, location, and the dual effect of distance from the centre. Tijdschrift Voor Economische en Sociale Geografie 107. (3): 298–315. https://doi.org/10.1111/tesg.12150
Magyar Közút Nonprofit Zrt. 2017. Az országos közutak 2016. évre vonatkozó keresztmetszeti forgalma (Crosssection traffic of public roads in Hungary for 2016). Budapest, One Planet Mérnökiroda Kft.
Manyika, J., Chiu, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. 2011. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
Marvin, S.J. 1994. Green signals. The environmental role of telecommunications in cities. Cities 11. (5). 325–331. https://doi.org/10.1016/0264-2751(94)90085-X
Mattoni, B., Gugliermetti, F. and Bisegna, F. 2015. A multilevel method to assess and design the renovation and integration of Smart Cities. Sustainable Cities and Society 15. 105–119. https://doi.org/10.1016/j.scs.2014.12.002
Morgul, E., Yang, H., Kurkcu, A., Ozbay, K., Bartin, B., Kamga, C. and Salloum, R. 2014. Virtual sensors: Web-based real-time data collection methodology for transportation operation performance analysis. Transportation Research Record: Journal of the Transportation Research Board 2442. (12): 106–116. https://doi.org/10.3141/2442-12
Navarro, C., Roca-Riu, M., Furió, S. and Estrada, M. 2015. Designing new models for energy efficiency in urban freight transport for smart cities and its application to the Spanish case. Transportation Research Procedia 12. 314–324. https://doi.org/10.1016/j.trpro.2016.02.068
Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G. and Scorrano, F. 2014. Current trends in Smart City initiatives: Some stylised facts. Cities 38. 25–36. https://doi.org/10.1016/j.cities.2013.12.010
Niaros, V. 2016. Introduction a Taxonomy of the "Smart City": Towards a Commons-Oriented Approach? tripleC: Communication, Capitalism & Critique 14. (1): 51–61.
Pálóczi, G. 2016. Researching commuting to work using the methods of complex network analysis. Regional Statistics 6. (1): 3–22.
Qingyu, L., Zhicai, J., Baofeng, S. and Hongfei, J. 2007. Method Research on Measuring the External Costs of Urban Traffic Congestion. Journal of Transportation Systems Engineering and Information Technology 7. (5): 9–12. https://doi.org/10.1016/S1570-6672(07)60035-X
Rial, N. 2013. The power of big data in Europe. New Europe, May 24th. https://www.neweurope.eu/article/power-big-data-europe/ Downloaded: 28.02.2017.
Rouwendal, J. and Nijkamp, P. 2004. Living in two worlds: A review of home-to-work decisions. Growth and Change 35. (3): 287–303. https://doi.org/10.1111/j.1468-2257.2004.00250.x
Schrank, D., Lomax, T., Turner, S., Geng, L., Li, Y. and Koncz, N. 2010. TTI's 2010 Urban Mobility Report. Texas Transportation Institute, A&M University, College Station TX.
Smolan, R. and Erwitt, J. 2012. The Human Face of Big Data. New York, Sterling.
Steenbruggen, J., Tranos, E. and Nijkamp, P. 2015. Data from mobile phone operators: A tool for smarter cities? Telecommunications Policy 39. (3–4): 335–346. https://doi.org/10.1016/j.telpol.2014.04.001
Stopher, P.R. 2004. Reducing road congestion: A reality check. Transport Policy 11. (2): 117–131. https://doi.org/10.1016/j.tranpol.2003.09.002
Sweet, M. 2011. Does Traffic Congestion Slow the Economy? Journal of Planning Literature 26. (4): 391–404. https://doi.org/10.1177/0885412211409754
Sweet, M. 2013. Traffic Congestion's Economic Impacts: Evidence from US Metropolitan Regions. Urban Studies 51. (10): 2088–2110. https://doi.org/10.1177/0042098013505883
Townsend, A.M. 2013. Smart Cities. Big data, civic hackers, and the quest for a new utopia. New York, W.W. Norton & Company.
Varga, L., Tóth, G. and Néda, Z. 2016. An improved radiation model and its applicability for understanding commuting patterns in Hungary. Regional Statistics 6. (2): 27–38. https://doi.org/10.15196/RS06202
Wardrop, J.G. and Charlesworth, G. 1954. A method of estimating speed and flow of traffic from moving vehicle. Proceeding of the Institution of Civil Engineers 3. (1): 158–171. https://doi.org/10.1680/ipeds.1954.11628
Zikopoulos, P.C., Eaton, C., de Roos, D., Deutsch, T. and Lapis, G. 2012. Understanding big data. New York, McGraw Hill.
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