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Location-based social network data offers the promise of collecting the datafrom a large base of users over a longer span of time at negligible cost. Whileseveral studies have applied social network data to activity and mobilityanalysis, a comparison with travel diaries and general statistics has beenlacking. In this paper, we analysed geo-referenced Twitter activities from alarge number of users in Singapore and neighbouring countries. By combiningthis data, population statistics and travel diaries and applying clusteringtechniques, we addressed detection of activity locations, as well as spatialseparation and transitions between these locations. Kernel density estimationperforms best to detect activity locations due to the scattered nature of thetwitter data; more activity locations are detected per user than reported inthe travel survey. The descriptive analysis shows that determining homelocations is more difficult than detecting work locations for most planningzones. Spatial separations between detected activity locations from Twitterdata - as reported in a travel survey and captured by public transport smartcard data - are mostly similarly distributed, but also show relevantdifferences for very short and very long distances. This also holds for thetransitions between zones. Whether the differences between Twitter data andother data sources stem from differences in the population sub-sample,clustering methodology, or whether social networks are being used significantlymore at specific locations must be determined by further research. Despitethese shortcomings, location-based social network data offers a promising datasource for insights into activity locations and mobility patterns, especiallyfor regions where travel survey data is not readily available.

Investigating the potential of social network data for transport demand models