Abstract
The pervasiveness of mobile devices, which is increasing daily, is generating a vast amount of geo-located data allowing us to gain further insights into human behaviors. In particular, this new technology enables users to communicate through mobile social media applications, such as Twitter, anytime and anywhere. Thus, geo-located tweets offer the possibility to carry out in-depth studies on human mobility. In this paper, we study the use of Twitter in transportation by identifying tweets posted from roads and rails in Europe between September 2012 and November 2013. We compute the percentage of highway and railway segments covered by tweets in 39 countries. The coverages are very different from country to country and their variability can be partially explained by differences in Twitter penetration rates. Still, some of these differences might be related to cultural factors regarding mobility habits and interacting socially online. Analyzing particular road sectors, our results show a positive correlation between the number of tweets on the road and the Average Annual Daily Traffic on highways in France and in the UK. Transport modality can be studied with these data as well, for which we discover very heterogeneous usage patterns across the continent.
Highlights
An increasing number of geo-located data are generated everyday through mobile devices
We present the results starting by general features about the Twitter database and comparing different European countries by their percentage of highway and railway covered by the tweets
The number of tweets on the road is compared with the Average Annual Daily Traffic (AADT) in France and in the United Kingdom to assess its capacity as a proxy to measure traffic loads
Summary
An increasing number of geo-located data are generated everyday through mobile devices. Some examples include cell phone records [3,4,5,6,7,8,9,10,11,12,13,14,15,16], credit card use information [17], GPS data from devices installed in cars [18,19], geolocated tweets [20,21,22,23,24,25] or Foursquare data [26] This information led to notable insights in human mobility at individual level [5,24], but it makes possible to introduce new methods to extract origin-destination tables at a more aggregated scale [7,13,25], to study the structure of cities [16] and even to determine land use patterns [11,12,15,25]. The number of tweets on the road is compared with the Average Annual Daily Traffic (AADT) in France and in the United Kingdom to assess its capacity as a proxy to measure traffic loads
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