Using Google Maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion

A pilot study from Budapest

Keywords: smart city, urban transport, big data, Google Maps, travel costs, wasted time, urban traffic


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.


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How to Cite
BajiP. (2018). Using Google Maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion: A pilot study from Budapest. Hungarian Geographical Bulletin, 67(1), 61-74.