A methodology with a distributed algorithm for large-scale trajectory distribution prediction

References

• Calabrese, F., et al. 2011. Real-time urban monitoring using cell phones: A case study in Rome. Intelligent Transportation Systems, IEEE Transactions On, 12 (1), 141–151. doi:10.1109/TITS.2010.2074196.
   Web of Science ®Google Scholar
• Chen, P.-T., Chen, F., and Qian, Z., 2014. Road traffic congestion monitoring in social media with hinge-loss Markov random fields. ed. 2014 IEEE International Conference on Data Mining, 2014, Shenzhen, 80–89.
   Google Scholar
• Chen, Z., Shen, H.T., and Zhou, X., 2011. Discovering popular routes from trajectories. ed. 2011 IEEE 27th International Conference on Data Engineering, 2011, 900–911.
   Google Scholar
• Clark, S., 2003. Traffic prediction using multivariate nonparametric regression. Journal of Transportation Engineering, 129 (2), 161–168. doi:10.1061/(ASCE)0733-947X(2003)129:2(161).
   Web of Science ®Google Scholar
• Cranshaw, J., et al., The livehoods project: utilizing social media to understand the dynamics of a city. ed. International AAAI Conference on Weblogs and Social Media, 2012, Dublin, Ireland, 58.
   Google Scholar
• Davis, G.A. and Nihan, N.L., 1991. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117, 178–188. doi:10.1061/(ASCE)0733-947X(1991)117:2(178)
   Google Scholar
• Deri, J.A., Franchetti, F., and Moura, J.M., 2016. Big data computation of taxi movement in New York City. ed. Proceedings of the 1st IEEE Big Data Conference Workshop on Big Spatial Data, 2016, Washington, DC.
   Google Scholar
• Deri, J.A. and Moura, J.M., 2015. Taxi data in New York city: a network perspective. ed. Signals, Systems and Computers, 2015 49th Asilomar Conference on, 2015, Pacific Grove, 1829–1833.
   Google Scholar
• Franses, P.H., 2016. A note on the mean absolute scaled error. International Journal of Forecasting, 32 (1), 20–22. doi:10.1016/j.ijforecast.2015.03.008.
   Web of Science ®Google Scholar
• Guo, Q. and Karimi, H.A., 2017. A novel methodology for prediction of spatial-temporal activities using latent features. Computers, Environment and Urban Systems, 62, 74–85. doi:10.1016/j.compenvurbsys.2016.10.006
   Web of Science ®Google Scholar
• Hai, N.T., Nguyen, -H.-H., and Thai-Nghe, N., 2016. A mobility prediction model for location-based social networks. ed. Asian Conference on Intelligent Information and Database Systems, 2016, Da Nang, Vietnam, 106–115.
   Google Scholar
• Han, B., Liu, L., and Omiecinski, E., 2015. Road-network aware trajectory clustering: integrating locality, flow, and density. IEEE Transactions on Mobile Computing, 14 (2), 416–429. doi:10.1109/TMC.2013.119.
   Web of Science ®Google Scholar
• Jeung, H., et al. 2010. Path prediction and predictive range querying in road network databases. The VLDB Journal, 19 (4), 585–602. doi:10.1007/s00778-010-0181-y.
   Web of Science ®Google Scholar
• Lima, A., et al. 2016. Understanding individual routing behaviour. Journal of the Royal Society Interface, 13 (116), 20160021. doi:10.1098/rsif.2016.0021.
   PubMed Web of Science ®Google Scholar
• Liu, X. and Karimi, H.A., 2006. Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30 (6), 741–756. doi:10.1016/j.compenvurbsys.2006.02.007.
   Web of Science ®Google Scholar
• Kriegel, H.-P., et al. 2008. Statistical density prediction in traffic networks. Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 2008, Atlanta, USA, 692 - 703.
   Google Scholar
• Newson, P. and Krumm, J., Hidden Markov map matching through noise and sparseness. ed. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, 2009, Seattle, 336–343. doi:10.1177/1753193408101464.
   Google Scholar
• Noulas, A., et al., 2011. Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. The Social Mobile Web, Barcelona, Spain, 11.
   Google Scholar
• NYCOPENDATA, 2016. NYC open data [online]. Available from: https://opendata.cityofnewyork.us/ [Accessed 01 Jan 2016].
   Google Scholar
• OSM4ROUTING, 2017. OSM4Routing [online]. Available from: https://github.com/Tristramg/osm4routing.
   Google Scholar
• Pan, B., et al., 2013. Crowd sensing of traffic anomalies based on human mobility and social media. ed. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2013, Orlando, 344–353.
   Google Scholar
• Ren, Y., et al. 2014. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. arXiv preprint arXiv:1410.4849. doi:10.1038/ncomms6347.
   Google Scholar
• Shekhar, S. and Williams, B., 2008. Adaptive seasonal time series models for forecasting short-term traffic flow. Transportation Research Record: Journal of the Transportation Research Board, 116–125 (2024).
   Google Scholar
• Simini, F., et al. 2012. A universal model for mobility and migration patterns. Nature, 484 (7392), 96–100. doi:10.1038/nature10856.
   PubMed Web of Science ®Google Scholar
• Toole, J.L., et al., 2015. The path most traveled: travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies, 58, 162–177. doi:10.1016/j.trc.2015.04.022.
   Web of Science ®Google Scholar
• Wang, S., et al., 2015. Citywide traffic congestion estimation with social media. ed. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015, Seattle, 34.
   Google Scholar
• Wei, L.-Y., Zheng, Y., and Peng, W.-C., 2012. Constructing popular routes from uncertain trajectories. ed. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, Beijing, China, 195–203.
   Google Scholar
• Williams, B.M. and Hoel, L.A., 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. Journal of Transportation Engineering, 129 (6), 664–672. doi:10.1061/(ASCE)0733-947X(2003)129:6(664).
   Web of Science ®Google Scholar
• Wilson, A.G., 1967. A statistical theory of spatial distribution models. Transportation Research, 1 (3), 253–269. doi:10.1016/0041-1647(67)90035-4.
   Google Scholar
• Yang, C. and Gidófalvi, G., 2016. Interactive visual exploration of most likely movements. ed. the 19th AGILE International Conference on Geographic Information Science, Helsinki, Finland, 2016.
   Google Scholar
• Yen, J.Y., 1970. An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Quarterly of applied mathematics. American Mathematical Society, 526–530.
   Google Scholar
• Yu, X., et al., 2010. Cooperative sensing and compression in vehicular sensor networks for urban monitoring. ed. Communications (ICC), 2010 IEEE International Conference on, 2010, Cape Town, 1–5.
   Google Scholar
• Zhang, K., Lin, Y.-R., and Pelechrinis, K., Eigen Transitions with hypothesis testing: the anatomy of urban mobility .ed Icwsm, 2016, Cologne, Germany, 486–495.
   Google Scholar
• Zhang, W., et al. 2011. Mobile sensing in metropolitan area: case study in Beijing. In ed., Mobile sensing challenges opportunities and future directions, Ubicomp2011 workshop, 2011,  Beijing, China, 637-638.
   Google Scholar
• Zhu, X. and Guo, D., 2014. Mapping large spatial flow data with hierarchical clustering. Transactions in GIS, 18 (3), 421–435. doi:10.1111/tgis.2014.18.issue-3.
   Web of Science ®Google Scholar