TY - GEN
T1 - Discovering and Characterizing Mobility Patterns in Urban Spaces
T2 - 25th International Conference on World Wide Web, WWW 2016
AU - Espín Noboa, Lisette
AU - Lemmerich, Florian
AU - Singer, Philipp
AU - Strohmaier, Markus
N1 - Publisher Copyright:
© 2016 International World Wide Web Conference Committee (IW3C2).
PY - 2016/4/11
Y1 - 2016/4/11
N2 - Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
AB - Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
KW - human keywords
KW - hyptrails
KW - tensor factorization
UR - http://www.scopus.com/inward/record.url?scp=85040223217&partnerID=8YFLogxK
U2 - 10.1145/2872518.2890468
DO - 10.1145/2872518.2890468
M3 - Conference contribution
AN - SCOPUS:85040223217
T3 - WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web
SP - 537
EP - 542
BT - WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
Y2 - 11 May 2016 through 15 May 2016
ER -