Table 1 Representative studies of travel behaviour using social media data. T is the time span covered by the dataset. S represents data source. On the Angle column, A indicates aggregate/lateral level and I indicates individual/longitudinal level. On the S column, F indicates Foursquare and T indicates Twitter
Study | Topic | Angle | User | Sample | Geo-scale | T | S |
|---|---|---|---|---|---|---|---|
[35] | Travel demand | A | – | 19,710 | City | 21 days | F |
[36] | Travel demand | A | 54,272 | 355,059 | City | 2 days | T |
[10] | Representativeness | A | 156,607 | 7,811,004 | Country | 8 months | T |
[26] | City influence | A | 571,893 | 21,017,892 | Cities | 1000 days | T |
[15] | Travel distance | A | 791,542 | 15,719,535 | Cities | 6 months | T |
[32] | User routines | I | 825 | 157,806 | City | 1 year | F |
[11] | Urban activity | I | 3256 | 504,000 | City | 1 month | T |
[37] | Activity space | I | 116 | 63,114 | County | 5 months | T |
[8] | Travel monitoring | I | 9738 | 6,000,000 | Districts | 1 year | T |
This study | Travel patterns | I + A | 2926 | 652,945 | Globe | 3.6 years | T |