An Empirical Study of Combining Participatory and Physical Sensing to Better Understand and Improve Urban Mobility Networks
暂无分享,去创建一个
[1] James B. D. Joshi,et al. Exploring trajectory-driven local geographic topics in foursquare , 2012, UbiComp.
[2] Chaoming Song,et al. Modelling the scaling properties of human mobility , 2010, 1010.0436.
[3] Bin Ran,et al. Dynamic Origin-Destination Travel Demand Estimation Using Location Based Social Networking Data , 2014 .
[4] Shou-De Lin,et al. Exploiting large-scale check-in data to recommend time-sensitive routes , 2012, UrbComp '12.
[5] Shan Jiang,et al. Discovering urban spatial-temporal structure from human activity patterns , 2012, UrbComp '12.
[6] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[7] Kyumin Lee,et al. You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.
[8] Marta C. González,et al. A universal model for mobility and migration patterns , 2011, Nature.
[9] Satish V. Ukkusuri,et al. Urban activity pattern classification using topic models from online geo-location data , 2014 .
[10] Stephen F. Smith,et al. Schedule-driven intersection control , 2012 .
[11] Cecilia Mascolo,et al. An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.
[12] Stephen F. Smith,et al. Schedule-Driven Coordination for Real-Time Traffic Network Control , 2012, ICAPS.
[13] Tao Zhou,et al. Origin of the scaling law in human mobility: hierarchy of traffic systems. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[14] Dino Pedreschi,et al. Human mobility, social ties, and link prediction , 2011, KDD.
[15] Hunter N. B. Moseley,et al. Limits of Predictability in Human Mobility , 2010 .
[16] Chetan Gupta,et al. Forecasting Spatiotemporal Impact of Traffic Incidents on Road Networks , 2013, 2013 IEEE 13th International Conference on Data Mining.
[17] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[18] T. Geisel,et al. The scaling laws of human travel , 2006, Nature.
[19] Kyumin Lee,et al. Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.
[20] Huan Liu,et al. Modeling temporal effects of human mobile behavior on location-based social networks , 2013, CIKM.
[21] Albert-László Barabási,et al. Limits of Predictability in Human Mobility , 2010, Science.
[22] Michael R. Lyu,et al. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks , 2012, AAAI.
[23] Zbigniew Smoreda,et al. Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.
[24] Wei Shen,et al. Improving Traffic Prediction with Tweet Semantics , 2013, IJCAI.
[25] Jussara M. Almeida,et al. Challenges and opportunities on the large scale study of city dynamics using participatory sensing , 2013, 2013 IEEE Symposium on Computers and Communications (ISCC).
[26] Hassan Hajji,et al. Statistical analysis of network traffic for adaptive faults detection , 2005, IEEE Transactions on Neural Networks.
[27] Franco Zambonelli,et al. Extracting urban patterns from location-based social networks , 2011, LBSN '11.
[28] Norman M. Sadeh,et al. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.
[29] Yu Zheng,et al. Constructing popular routes from uncertain trajectories , 2012, KDD.
[30] M. Hansen,et al. Participatory Sensing , 2019, Internet of Things.
[31] Wang,et al. Review of road traffic control strategies , 2003, Proceedings of the IEEE.
[32] Philip S. Yu,et al. Inferring distant-time location in low-sampling-rate trajectories , 2013, KDD.
[33] Swapna S. Gokhale,et al. Participatory Paradigms: Promises and Challenges for Urban Transportation , 2014 .
[34] Stephen F. Smith,et al. Unified Route Choice Framework and Empirical Study in Urban Traffic Control Environment , 2014 .
[35] Takahiro Hara,et al. Mining people's trips from large scale geo-tagged photos , 2010, ACM Multimedia.