References
- Altomare, A., et al., 2016. Trajectory pattern mining for urban computing in the cloud. IEEE Transactions on Parallel and Distributed Systems, 1–1. doi:10.1109/TPDS.2016.2565480
- Barroudi, M.M., Harwood, A., and Karunasekera, S., 2014. Measuring similarity between mobility models and real world motion trajectories.
- Bonchi, F., Lakshmanan, L.V.S., and Wang, H., (Wendy), 2011. Trajectory anonymity in publishing personal mobility data. SIGKDD Explorations Newsletter, 13 (1), 30–42. doi:10.1145/2031331.2031336
- Bora, M., et al., 2014. Effect of different distance measures on the performance of K-means algorithm: an experimental study in matlab. arXiv Preprint arXiv:1405.7471.
- Brščić, D., et al., 2013. Person tracking in large public spaces using 3-D range sensors. IEEE Transactions on Human-Machine Systems, 43 (6), 522–534. doi:10.1109/THMS.2013.2283945
- Chen, B.Y., et al., 2013. Reliable space–time prisms under travel time uncertainty. Annals of the Association of American Geographers, 103 (6), 1502–1521. doi:10.1080/00045608.2013.834236
- Dias, D. and Costa, L.H.M.K., 2018. CRAWDAD dataset coppe-ufrj/RioBuses (v. 2018-03-19).
- Downs, J., et al., 2018. Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement. International Journal of Geographical Information Science, 32 (7), 1505–1522. doi:10.1080/13658816.2017.1421764
- Downs, J.A. and Horner, M.W., 2014. Adaptive-velocity time-geographic density estimation for mapping the potential and probable locations of mobile objects. Environment and Planning. B, Planning & Design, 41 (6), 1006–1021. doi:10.1068/b130065p
- Epple, B., 2006. Using a GPS-aided inertial system for coarse-pointing of free-space optical communication terminals. In: Free-Space Laser Communications VI. Presented at the Free-Space Laser Communications VI, International Society for Optics and Photonics, San Diego, California.
- Furtado, A.S., et al., 2018. Unveiling movement uncertainty for robust trajectory similarity analysis. International Journal of Geographical Information Science, 32 (1), 140–168. doi:10.1080/13658816.2017.1372763
- Golub, G.H. and Van Loan, C.F., 2012. Matrix computations. Baltimore, Maryland, USA: JHU press.
- He, W., Hwang, K., and Li, D., 2014. Intelligent carpool routing for urban ridesharing by mining GPS trajectories. IEEE Transactions on Intelligent Transportation Systems, 15 (5), 2286–2296. doi:10.1109/TITS.2014.2315521
- Hoteit, S., et al., 2014. Estimating human trajectories and hotspots through mobile phone data. Computer Networks, 64, 296–307. doi:10.1016/j.comnet.2014.02.011
- Huang, Q. and Wong, D.W.S., 2015. Modeling and visualizing regular human mobility patterns with uncertainty: an example using twitter data. Annals of the Association of American Geographers, 105 (6), 1179–1197. doi:10.1080/00045608.2015.1081120
- Jeung, H., et al., 2014. Managing evolving uncertainty in trajectory databases. IEEE Transactions on Knowledge and Data Engineering, 26 (7), 1692–1705. doi:10.1109/TKDE.2013.141
- Kobayashi, T., Miller, H.J., and Othman, W., 2011. Analytical methods for error propagation in planar space–time prisms. Journal of Geographical Systems, 13 (4), 327–354. doi:10.1007/s10109-010-0139-z
- Kong, X., et al., 2018. Big trajectory data: A survey of applications and services. IEEE Access, 6, 58295–58306. doi:10.1109/ACCESS.2018.2873779
- Kuijpers, B. and Othman, W., 2006. Trajectory databases: data models, uncertainty and complete query languages. Journal of Computer and System Sciences, 76 (7), 538–560.
- Kuijpers, B. and Othman, W., 2009. Modeling uncertainty of moving objects on road networks via space–time prisms. International Journal of Geographical Information Science, 23 (9), 1095–1117. doi:10.1080/13658810802097485
- Kwan, M.-P., 1998. Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. Geographical Analysis, 30 (3), 191–216. doi:10.1111/j.1538-4632.1998.tb00396.x
- Liao, F., Rasouli, S., and Timmermans, H., 2014. Incorporating activity-travel time uncertainty and stochastic space–time prisms in multistate supernetworks for activity-travel scheduling. International Journal of Geographical Information Science, 28 (5), 928–945. doi:10.1080/13658816.2014.887086
- Liu, X., et al., 2012. Mining large-scale, sparse GPS traces for map inference: comparison of approaches. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. Beijing, China: ACM, 669–677.
- Lou, Y., et al., 2009. Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. Seattle, Washington: ACM, 352–361.
- Lu, B., et al., 2016. The Minkowski approach for choosing the distance metric in geographically weighted regression. International Journal of Geographical Information Science, 30 (2), 351–368. doi:10.1080/13658816.2015.1087001
- Marshall, S., 2005. Streets & patterns. 1st. London; New York: Spon.
- Miller, H.J., 1991. Modelling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information System, 5 (3), 287–301. doi:10.1080/02693799108927856
- Miller, H.J., 2008. Time geography. In: S. Shekhar and H. Xiong, eds. Encyclopedia of GIS. Boston, MA: Springer US, 1151–1156.
- Miller, H.J., 2017. Time geography and space-time prism. In: D. Richardson, et al., eds.. International Encyclopedia of Geography. Oxford, UK: John Wiley & Sons, Ltd, 1–19.
- Niedermayer, J., et al., 2013. Similarity search on uncertain spatio-temporal data. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8199. Berlin, Heidelberg: LNCS, 43–49.
- Pfoser, D. and Jensen, C.S., 1999. Capturing the uncertainty of moving-object representations. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Heidelberg, 1651, 111–131.
- Powers, D.M., 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.
- Ranu, S., et al., 2015. Indexing and matching trajectories under inconsistent sampling rates. In: 2015 IEEE 31st International Conference on Data Engineering. Presented at the 2015 IEEE 31st International Conference on Data Engineering, Seoul, South Korea, 999–1010.
- Shahid, R., et al., 2009. Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Services Research, 9 (1), 200. doi:10.1186/1472-6963-9-200
- Song, Y., et al., 2016. Modeling visit probabilities within network-time prisms using markov techniques: visit probabilities within network-time prisms. Geographical Analysis, 48 (1), 18–42. doi:10.1111/gean.12076
- Song, Y. and Miller, H.J., 2014. Simulating visit probability distributions within planar space-time prisms. International Journal of Geographical Information Science, 28 (1), 104–125. doi:10.1080/13658816.2013.830308
- Trajcevski, G., et al., 2004. Managing uncertainty in moving objects databases. ACM Transactions on Database Systems (TODS), 29 (3), 463–507. doi:10.1145/1016028.1016030
- Trajcevski, G., 2011. Uncertainty in spatial trajectories. In: Y. Zheng and X. Zhou, eds.. Computing with spatial trajectories. New York: Springer New York, 63–107.
- Winter, S. and Yin, Z.-C., 2010. Directed movements in probabilistic time geography. International Journal of Geographical Information Science, 24 (9), 1349–1365.
- Winter, S. and Yin, Z.-C., 2011. The elements of probabilistic time geography, 18.
- Xia, F., et al., 2018. Exploring human mobility patterns in urban scenarios: A trajectory data perspective. IEEE Communications Magazine, 56 (3), 142–149. doi:10.1109/MCOM.2018.1700242
- Yuan, N.J., et al., 2014. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 27 (3), 712–725. doi:10.1109/TKDE.2014.2345405
- Zhang, G. and Hsu, L.-T., 2019. A new path planning algorithm using a GNSS localization error map for UAVS in an urban area. Journal of Intelligent & Robotic Systems, 94 (1), 219–235. doi:10.1007/s10846-018-0894-5
- Zheng, K., et al., 2012. Reducing uncertainty of low-sampling-rate trajectories. In: 2012 IEEE 28th international conference on data engineering, Arlington, Virginia, 1144–1155.
- Zheng, Y., et al., 2009. Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World wide web. ACM, Madrid, Spain, 791–800.
- Zheng, Y., 2015. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6 (3), 29.
- Zheng, Y. and Zhou, X., 2011. Computing with spatial trajectories. Berlin, Heidelberg: Springer Science & Business Media.
- Zheng, Z., Rasouli, S., and Timmermans, H., 2014. Evaluating the accuracy of GPS-based taxi trajectory records. Procedia Environmental Sciences, 22, 186–198. doi:10.1016/j.proenv.2014.11.019
- Zhou, X., et al., 2018. Detecting taxi speeding from sparse and low-sampled trajectory data. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data. Macau, China: Springer, 214–222.