Tweets on the road

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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. © 2014 Lenormand et al.

Figures

  • Figure 1. Maps of geo-located tweets, roads and railways. (a) Geo-located tweets on a map. (b) Highway network. (c) Railway network. doi:10.1371/journal.pone.0105407.g001
  • Figure 2. Histograms of total lengths by country of highways and railways. (a) Highways total length by European countries. (b) Railways total length by European countries. (c) Ratio between the total length of highways and that of railways by several European countries. The red line marks the unit ratio. doi:10.1371/journal.pone.0105407.g002
  • Figure 3. Twitter penetration rate across Europe. (a) and (c) Geolocated Twitter penetration rate across Europe at country level (a) and NUTS 2 level (c). Twitter penetration rate is defined as the ratio between the number of users emitting geo-located tweets in our database and the population in 2012. (b) and (d) Penetration rate as a function of the Gross Domestic Product (GDP) per capita at country level (b) and NUTS 2 level (d). At country level, the figures for the GDP were obtained from the web of the International Monetary Fund [30], correspond to the year 2012 and are expressed in US dollars. At NUTS 2 level, the figures for the GDP were obtained from the web of Eurostat [32], the figures for GDP correspond to the year 2011 and are expressed in euros. Each point represents a country or a NUTS, the red points in (d) represent the NUTS 2 of Turkey. doi:10.1371/journal.pone.0105407.g003
  • Figure 4. Distribution of the social network’s degree. (a) Probability distribution of number of ties of an individual in the social network of 5 countries drawn at random among the 39 case studies. (b) Box plot of the 39 fitted exponent values. (c) Box plot of the R2 values. The box plot is composed of the minimum value, the lower hinge, the median, the upper hinge and the maximum value. doi:10.1371/journal.pone.0105407.g004
  • Figure 5. Highway and railway coverages. (a)–(b) Locations of the geo-located tweets on the road (a) and rail (b). (c)–(d) Segments of road (c) and rail (d) covered by the tweets. The red segments represent the segments covered by the tweets. doi:10.1371/journal.pone.0105407.g005
  • Figure 6. Top 20 of the countries ranked by highway coverage (a) and railway coverage (b).
  • Figure 7. Highway and railway coverages as a function of the penetration rate. (a) Highway coverage as a function of the penetration rate. (b) Railway coverage as a function of the penetration rate. (c) Railway coverage as a function of the highway coverage. Red lines are linear regression curves applied to the log-log plots. This mean that the slope corresponds to the exponents of power-law relationships. The slope in (a) is around 0.6, 0.8 in (b) and 1 (a linear relation) in (c). doi:10.1371/journal.pone.0105407.g007
  • Figure 8. Proportion of tweets in transports. (a) Proportion of tweets on the highway or railway networks by European countries. (b) Proportion of tweets according to the transport network by European countries. The blue color represents the tweets on the road. The red color represents the tweets on the rail. The proportions have been normalized in order to obtain a total proportion of tweets in transport equal to 1. doi:10.1371/journal.pone.0105407.g008

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APA

Lenormand, M., Tugores, A., Colet, P., & Ramasco, J. J. (2014). Tweets on the road. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0105407

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