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Moving Objects with Transportation Modes: A Survey

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Abstract

In this article, we survey the main achievements of moving objects with transportation modes that span the past decade. As an important kind of human behavior, transportation modes reflect characteristic movement features and enrich the mobility with informative knowledge. We make explicit comparisons with closely related work that investigates moving objects by incorporating into location-dependent semantics and descriptive attributes. An exhaustive survey is offered by considering the following aspects: 1) modeling and representing mobility data with motion modes; 2) answering spatio-temporal queries with transportation modes; 3) query optimization techniques; 4) predicting transportation modes from sensor data, e.g., GPS-enabled devices. Several new and emergent issues concerning transportation modes are proposed for future research.

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References

  1. Sistla P, Wolfson O, Chamberlain S, Dao S. Modeling and querying moving objects. In Proc. the 13th International Conference on Data Engineering, April 1997, pp.422-432.

  2. Güting R H, Schneider M. Moving Objects Databases. Morgan Kaufmann, 2005.

  3. Zheng Y, Chen Y, Li Q, Xie X, Ma W Y. Understanding transportation mode based on GPS data for web application. ACM Transaction on the Web, 2010, 4(1): Article No. 1.

  4. Yu M, Yu T, Wang S, Lin C, Chang E Y. Big data small footprint: The design of a low-power classifier for detecting transportation modes. Proceedings of the VLDB Endowment, 2014, 7(13): 1429-1440.

    Article  Google Scholar 

  5. Stenneth L, Wolfson O, Yu P, Xu B. Transportation mode detection using mobile phones and GIS information. In Proc. the 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, November 2011, pp.54-63.

  6. Tong Y, Zeng Y, Zhou Z, Chen L, Ye J, Xu K. A unified approach to route planning for shared mobility. Proceedings of the VLDB Endowment, 2018, 11(11): 1633-1646.

    Article  Google Scholar 

  7. Booth J, Sistla P, Wolfson O, Cruz I F. A data model for trip planning in multimodal transportation systems. In Proc. the 12th International Conference on Extending Database Technology, March 2009, pp.994-1005.

  8. Xu J, Güting R H, Zheng Y. The TM-RTree: An index on generic moving objects for range queries. GeoInformatica, 2015, 19(3): 487-524.

    Article  Google Scholar 

  9. Zografos K G, Androutsopoulos K N. Algorithms for itinerary planning in multimodal transportation networks. IEEE Trans. Intelligent Transportation Systems, 2008, 9(1): 175-184.

    Article  Google Scholar 

  10. Chaturvedi M, Srivastava S. Multi-modal design of an intelligent transportation system. IEEE Trans. Intelligent Transportation Systems, 2017, 18(8): 2017-2027.

    Article  Google Scholar 

  11. Li M, Dai J, Sahu S, Naphade M R. Trip analyzer through smartphone apps. In Proc. the 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, November 2011, pp.537-540.

  12. Froehlich J, Dillahunt T, Klasnja P V, Mankoff J, Consolvo S, Harrison B L, Landay J A. UbiGreen: Investigating a mobile tool for tracking and supporting green transportation habits. In Proc. the 27th International Conference on Human Factors in Computing Systems, April 2009, pp.1043-1052.

  13. Reddy S, Mun M, Burke J, Estrin D, Hansen M H, Srivastava M B. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks, 2010, 6(2): Article No. 13.

  14. Prentow T S, Blunck H, Kjærgaard M B, Stisen A. Towards indoor transportation mode detection using mobile sensing. In Proc. the 7th International Conference on Mobile Computing, Applications, and Services, November 2015, pp.259-279.

  15. Asghari M, Shahabi C. An on-line truthful and individually rational pricing mechanism for ride-sharing. In Proc. the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2017, Article No. 7.

  16. Cheng P, Xin H, Chen L. Utility-aware ridesharing on road networks. In Proc. the 2017 ACM International Conference on Management of Data, May 2017, pp.1197-1210.

  17. Wolfson O, Lin J. Fairness versus optimality in ridesharing. In Proc. the 18th IEEE International Conference on Mobile Data Management, May 2017, pp.118-123.

  18. Chiang M, Lim E, Lee W, Hoang T. Inferring trip occupancies in the rise of ride-hailing services. In Proc. the 27th ACM International Conference on Information and Knowledge Management, October 2018, pp.2097-2105.

  19. Xu Z, Li Z, Guan Q et al. Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 2018, pp.905-913.

  20. Agatz N A H, Bazzan A L C, Kutadinata R J, Mattfeld D C, Sester M, Winter S, Wolfson O. Autonomous car and ride sharing: Flexible road trains: (vision paper). In Proc. the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, October 2016, Article No. 10.

  21. Xu J, Güting R H. A generic data model for moving objects. GeoInformatica, 2013, 17(1): 125-172.

    Article  Google Scholar 

  22. Zheng Y, Li Q, Chen Y, Xie X, Ma W Y. Understanding mobility based on GPS data. In Proc. the 10th International Conference on Ubiquitous Computing, September 2008, pp.312-321.

  23. Zheng Y, Liu L, Wang L, Xie X. Learning transportation mode from raw GPS data for geographic applications on the web. In Proc. the 17th International Conference on World Wide Web, April 2008, pp.247-256.

  24. Vu T H, Dung L, Wang J. Transportation mode detection on mobile devices using recurrent nets. In Proc. the 2016 ACM Conference on Multimedia Conference, October 2016, pp.392-396.

  25. Biljecki F, Ledoux H, van Oosterom P. Transportation mode-based segmentation and classification of movement trajectories. International Journal of Geographical Information Science, 2013, 27(2): 385-407.

    Article  Google Scholar 

  26. Xiao Z, Wang Y, Fu K, Wu F. Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of Geo-Information, 2017, 6(2): Article No. 57.

  27. Ashqar H I, Almannaa M H, Elhenawy M, Rakha H A, House L. Smartphone transportation mode recognition using a hierarchical machine learning classifier and pooled features from time and frequency domains. IEEE Trans. Intelligent Transportation Systems, 2019, 20(1): 244-252.

    Article  Google Scholar 

  28. Hemminki S, Nurmi P, Tarkoma S. Accelerometer-based transportation mode detection on smartphones. In Proc. the 11th ACM Conference on Embedded Network Sensor Systems, November 2013, Article No. 13.

  29. Bloch A, Erdin R, Meyer S, Keller T, de Spindler A. Battery-efficient transportation mode detection on mobile devices. In Proc. the 16th IEEE International Conference on Mobile Data Management, June 2015, pp.185-190.

  30. Coffey C, Nair R, Pinelli F, Pozdnoukhov A, Calabrese F. Missed connections: Quantifying and optimizing multimodal interconnectivity in cities. In Proc. the 16th IEEE International Conference on Mobile Data Management, June 2012, pp.26-32.

  31. Xu J, Güting R H, Qin X. GMOBench: Benchmarking generic moving objects. GeoInformatica, 2015, 19(2): 227-276.

    Article  Google Scholar 

  32. Xu J, Güting R H. MWGen: A mini world generator. In Proc. the 13th IEEE International Conference on Mobile Data Management, July 2012, pp.258-267.

  33. Wang W, Xu J. A tool for 3D visualizing moving objects. In Proc. the 1st International Joint Conference on Web and Big Data, July 2017, pp.353-357.

  34. Sohn T, Varshavsky A, LaMarca A, Chen M Y, Choudhury T, Smith I E, Consolvo S, Hightower J, Griswold W G, de Lara E. Mobility detection using everyday GSM traces. In Proc. the 8th International Conference on Ubiquitous Computing, September 2006, pp.212-224.

  35. Patterson D J, Liao L, Fox D, Kautz H A. Inferring high level behavior from low-level sensors. In Proc. the 5th International Conference on Ubiquitous Computing, October 2003, pp.73-89.

  36. Wang B, Gao L, Juan Z. Travel mode detection using GPS data and socioeconomic attributes based on a random forest classifier. IEEE Trans. Intelligent Transportation Systems, 2018, 19(5): 1547-1558.

    Article  Google Scholar 

  37. Li G, Chen C, Huang S, Chou A, Gou X, Peng W, Yi C. Public transportation mode detection from cellular data. In Proc. the 2017 ACM Conference on Information and Knowledge Management, November 2017, pp.2499-2502.

  38. Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K. SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proc. the 14th International Conference on Extending Database Technology, March 2011, pp.259-270.

  39. Zheng K, Shang S, Yuan N J, Yang Y. Towards efficient search for activity trajectories. In Proc. the 29th IEEE International Conference on Data Engineering, April 2013, pp.230-241.

  40. Zheng B, Yuan N J, Zheng K, Xie X, Sadiq S W, Zhou X. Approximate keyword search in semantic trajectory database. In Proc. the 31st IEEE International Conference on Data Engineering, April 2015, pp.975-986.

  41. Choi D W, Pei J, Heinis T. Efficient mining of regional movement patterns in semantic trajectories. Proceedings of the VLDB Endowment, 2017, 10(13): 2073-2084.

    Article  Google Scholar 

  42. Wang S, Bao Z, Culpepper J S, Sellis T, Sanderson M, Qin X. Answering top-k exemplar trajectory queries. In Proc. the 33rd IEEE International Conference on Data Engineering, April 2017, pp.597-608.

  43. Zhuang C, Yuan N J, Song R, Xie X, Ma Q. Understanding people lifestyles: Construction of urban movement knowledge graph from GPS trajectory. In Proc. the 26th International Joint Conference on Artificial Intelligence, August 2017, pp.3616-3623.

  44. Prentow T S, Thom A, Blunck H, Vahrenhold J. Making sense of trajectory data in indoor spaces. In Proc. the 16th IEEE International Conference on Mobile Data Management, June 2015, pp.116-121.

  45. Valdés F, Güting R H. Index-supported pattern matching on symbolic trajectories. In Proc. the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2014, pp.53-62.

  46. Güting R H, Valdés F, Damiani M L. Symbolic trajectories. ACM Transactions on Spatial Algorithms and Systems, 2015, 1(2): Article No. 7.

  47. Xu J, Lu H, Güting R H. Range queries on multi-attribute trajectories. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(6): 1206-1211.

    Article  Google Scholar 

  48. Xu J, Güting R H, Gao Y. Continuous k nearest neighbor queries over large multi-attribute trajectories: A systematic approach. GeoInformatica, 2018, 22(4): 723-766.

    Article  Google Scholar 

  49. Wolfson O, Xu B, Chamberlain S, Jiang L. Moving objects databases: Issues and solutions. In Proc. the 10th International Conference on Scientific and Statistical Database Management, July 1998, pp.111-122.

  50. Forlizzi L, Güting R H, Nardelli E, Schneider M. A data model and data structures for moving objects databases. In Proc. the 2000 ACM SIGMOD International Conference on Management of Data, May 2000, pp.319-330.

  51. Güting R H, Böhlen M H, Erwig M, Jensen C S, Lorentzos N A, Schneider M, Vazirgiannis M. A foundation for representing and querying moving objects. ACM Transactions on Database Systems, 2000, 25(1): 1-42.

    Article  Google Scholar 

  52. Vazirgiannis M, Wolfson O. A spatiotemporal model and language for moving objects on road networks. In Proc. the 7th International Symposium on Spatial and Temporal Databases, July 2001, pp.20-35.

  53. Speicys L, Jensen C S, Kligys A. Computational data modeling for network-constrained moving objects. In Proc. the 11th ACM International Symposium on Advances in Geographic Information Systems, November 2003, pp.118-125.

  54. Hage C, Jensen C S, Pedersen T B, Speicys L, Timko I. Integrated data management for mobile services in the real world. In Proc. the 29th International Conference on Very Large Data Bases, September 2003, pp.1019-1030.

  55. Güting R H, de Almeida V T, Ding Z M. Modeling and querying moving objects in networks. The VLDB Journal, 2006, 15(2): 165-190.

    Article  Google Scholar 

  56. Speičys L, Jensen C S. Enabling location-based services — Multi-graph representation of transportation networks. GeoInformatica, 2008, 12(2): 219-253.

    Article  Google Scholar 

  57. Park S H, Lee J, Kim D. Spatial clustering based on moving distance in the presence of obstacles. In Proc. the 12th International Conference on Database Systems for Advanced Applications, April 2007, pp.1024-1027.

  58. Gao Y, Zheng B, Chen G, Chen C, Li Q. Continuous nearest-neighbor search in the presence of obstacles. ACM Transactions on Database Systems, 2011, 36(2): Article No. 9.

  59. Gao Y, Zheng B, Chen G, Li Q, Guo X. Continuous visible nearest neighbor query processing in spatial databases. The VLDB Journal, 2011, 20(3): 371-396.

    Article  Google Scholar 

  60. Gao Y, Liu Q, Miao X, Yang J. Reverse k-nearest neighbor search in the presence of obstacles. Information Science, 2016, 330: 274-292.

    Article  Google Scholar 

  61. Jensen C S, Lu H, Yang B. Graph model based indoor tracking. In Proc. the 10th International Conference on Mobile Data Management, May 2009, pp.122-131.

  62. Jensen C S, Lu H, Yang B. Indoor — A new data management frontier. IEEE Data Eng. Bull., 2010, 33(2): 12-17.

    Google Scholar 

  63. Xie X, Lu H, Pedersen T B. Efficient distance-aware query evaluation on indoor moving objects. In Proc. the 29th IEEE International Conference on Data Engineering, April 2013, pp.434-445.

  64. Xie X, Lu H, Pedersen T B. Distance-aware join for indoor moving objects. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(2): 428-442.

    Article  Google Scholar 

  65. Lu H, Cheema M A. Indoor data management. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.1414-1417.

  66. Jensen C S, Lu H, Yang B. Indexing the trajectories of moving objects in symbolic indoor space. In Proc. the 11th International Symposium on Spatial and Temporal Databases, July 2009, pp.208-227.

  67. Yang B, Lu H, Jensen C S. Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space. In Proc. the 13th International Conference on Extending Database Technology, March 2010, pp.335-346.

  68. Koncz N A, Adams T M. A data model for multidimensional transportation applications. International Journal of Geographical Information Science, 2002, 16(6): 551-569.

    Article  Google Scholar 

  69. Timko I, Pedersen T B. Capturing complex multidimensional data in location-based data warehouses. In Proc. the 12th ACM International Workshop on Geographic Information Systems, November 2004, pp.147-156.

  70. Jensen C S, Kligys A, Pedersen T B, Timko I. Multidimensional data modeling for location-based services. The VLDB Journal, 2004, 13: 1-21.

    Article  Google Scholar 

  71. Hussein S H, Lu H, Pedersen T B. Towards a unified model of outdoor and indoor spaces. In Proc. the 2012 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2012, pp.522-525.

  72. Lin J, Sasidharan S, Ma S, Wolfson O. A model of multimodal ridesharing and its analysis. In Proc. the 17th IEEE International Conference on Mobile Data Management, June 2016, pp.164-173.

  73. Yang Y, Gao H, Yu J X, Li J. Finding the cost-optimal path with time constraint over time-dependent graphs. Proceedings of the VLDB Endowment, 2014, 7(9): 673-684.

    Article  Google Scholar 

  74. Li L, Hua W, Du X, Zhou X. Minimal on-road time route scheduling on time-dependent graphs. Proceedings of the VLDB Endowment, 2017, 10(11): 1274-1285.

    Article  Google Scholar 

  75. Ding Z, Güting R H. Managing moving objects on dynamic transportation networks. In Proc. the 16th International Conference on Scientific and Statistical Database Management, June 2004, pp.287-296.

  76. Huang B, Wu Q, Zhan F B. A shortest path algorithm with novel heuristics for dynamic transportation networks. International Journal of Geographical Information Science, 2007, 21(6): 625-644.

    Article  Google Scholar 

  77. Bakalov P, Hoel E G, Heng W, Tsotras V J. Maintaining connectivity in dynamic multimodal network models. In Proc. the 24th International Conference on Data Engineering, April 2008, pp.1267-1276.

  78. Bakalov P, Hoel E G, Heng W. Time dependent transportation network models. In Proc. the 31st International Conference on Data Engineering, April 2015, pp.1364-1375.

  79. Wang S, Lin W, Yang Y, Xiao X, Zhou S. Efficient route planning on public transportation networks: A labelling approach. In Proc. the 2015 ACM SIGMOD International Conference on Management of Data, May 2015, pp.967-982.

  80. Bauer V, Gamper J, Loperfido R, Profanter S, Putzer S, Timko I. Computing isochrones in multi-modal, schedule-based transport networks. In Proc. the 16th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, November 2008, Article No. 78.

  81. Tang G, Keshav S, Golab L, Wu K. Bikeshare pool sizing for bike-and-ride multimodal transit. IEEE Trans. Intelligent Transportation Systems, 2018, 19(7): 2279-2289.

    Article  Google Scholar 

  82. Bastani F, Xie X, Huang Y, Powell J W. A greener transportation mode: Flexible routes discovery from GPS trajectory data. In Proc. the 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, November 2011, pp.405-408.

  83. Ma S, Zheng Y, Wolfson O. T-share: A large-scale dynamic taxi ridesharing service. In Proc. the 29th IEEE International Conference on Data Engineering, April 2013, pp.410-421.

  84. Chen L, Zhong Q, Xiao X, Gao Y, Jin P, Jensen C S. Price-and-time-aware dynamic ridesharing. In Proc. the 34th IEEE International Conference on Data Engineering, April 2018, pp.1061-1072.

  85. Xu J, Güting R H. Manage and query generic moving objects in SECONDO. Proceedings of the VLDB Endowment, 2012, 5(12): 2002-2005.

    Article  Google Scholar 

  86. Güting R H, Behr T, Düntgen C. SECONDO: A platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng. Bull., 2010, 33(2): 56-63.

    Google Scholar 

  87. Wei X, Xu J. MDBF: A tool for monitoring database files. In Proc. ER, October 2018, pp.54-58.

  88. Liao L, Patterson D J, Fox D, Kautz H A. Learning and inferring transportation routines. Artif. Intell., 2007, 171(5/6): 311-331.

    Article  MathSciNet  MATH  Google Scholar 

  89. Widhalm P, Nitsche P, Brändle N. Transport mode detection with realistic smartphone sensor data. In Proc. the 21st International Conference on Pattern Recognition, November 2012, pp.573-576.

  90. Gong H, Chen C, Bialostozky E, Lawson C T. A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 2012, 36(2): 131-139.

    Article  Google Scholar 

  91. Asmar D C, Zelek J S, Abdallah S M. SmartSLAM: Localization and mapping across multi-environments. In Proc. the 2004 IEEE International Conference on Systems, Man and Cybernetics, October 2004, pp.5240-5245.

  92. Du H, Henry P, Ren X, Cheng M, Goldman D B, Seitz S M, Fox D. Interactive 3D modeling of indoor environments with a consumer depth camera. In Proc. the 13th International Conference on Ubiquitous Computing, September 2011, pp.75-84.

  93. Jiang Y, Yun X, Pan X, Li K, Lv Q, Dick R P, Shang L, Hannigan M. Hallway based automatic indoor floorplan construction using room fingerprints. In Proc. the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September 2013, pp.315-324.

  94. Srivatsa M, Ganti R K, Wang J, Kolar V. Map matching: Facts and myths. In Proc. the 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2013, pp.474-477.

  95. Taguchi S, Koide S, Yoshimura T. Online map matching with route prediction. IEEE Trans. Intelligent Transportation Systems, 2019, 20(1): 338-347.

    Article  Google Scholar 

  96. AlDwyish A, Xie H, Tanin E, Karunasekera S, Ramamohanarao K. Using a traffic simulator for navigation service. In Proc. the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2017, Article No. 78.

  97. Keler A, Kaths J, Chucholowski F, Chucholowski M, Grigoropoulos G, Spangler M, Kaths H, Busch F. A bicycle simulator for experiencing microscopic traffic flow simulation in urban environments. In Proc. the 21st International Conference on Intelligent Transportation Systems, November 2018, pp.3020-3023.

  98. Liang Y, Gao S, Wu T, Wang S, Wu Y. Optimizing bus stop spacing using the simulated annealing algorithm with spatial interaction coverage model. In Proc. the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science, November 2018, pp.53-59.

  99. Xie H, Tanin E, Karunasekera S, Kulik L, Zhang R, Qi J, Ramamohanarao K. Studying transportation problems with the SMARTS simulator (demo paper). In Proc. the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2018, pp.580-583.

  100. Theodoridis Y, Silva J R O, Nascimento M A. On the generation of spatiotemporal datasets. In Proc. the 6th International Symposium on Spatial Databases, July 1999, pp.147-164.

  101. Pfoser D, Theodoridis Y. Generating semantics-based trajectories of moving objects. Computers, Environment and Urban Systems, 2003, 27(3): 243-263.

    Article  Google Scholar 

  102. Saltenis S, Jensen C S, Leutenegger S T, López M A. Indexing the positions of continuously moving objects. In Proc. the 2000 ACM SIGMOD International Conference on Management of Data, May 2000, pp.331-342.

  103. Düntgen C, Behr T, Güting R H. BerlinMOD: A benchmark for moving objects database. The VLDB Journal, 2009, 18(6): 1335-1368.

    Article  Google Scholar 

  104. Brinkhoff T. Generating network-based moving objects. In Proc. the 12th International Conference on Scientific and Statistical Database Management, July 2000, pp.253-255.

  105. Brinkhoff T. A framework for generating network-based moving objects. GeoInformatica, 2002, 6(2): 153-180.

    Article  MATH  Google Scholar 

  106. Krajzewicz D, Hertkorn G, Rössel C, Wagner P. Sumo (simulation of urban mobility): An open-source traffic simulation. In Proc. the 4th Middle East Symposium on Simulation and Modelling, September 2002, pp.183-187.

  107. Hu H, Lee D L. GAMMA: A framework for moving object simulation. In Proc. the 9th International Symposium on Spatial and Temporal Databases, August 2005, pp.37-54.

  108. Pelekis N, Ntrigkogias C, Tampakis P, Sideridis S, Theodoridis Y. Hermoupolis: A trajectory generator for simulating generalized mobility patterns. In Proc. the 2013 European Conference on Machine Learning and Knowledge Discovery in Databases, September 2013, pp.659-662.

  109. Mokbel M F, Alarabi L, Bao J, Eldawy A, Magdy A, Sarwat M, Waytas E, Yackel S. A demonstration of MNTG — A web-based road network traffic generator. In Proc. the 30th IEEE International Conference on Data Engineering, March 2014, pp.1246-1249.

  110. Huang C, Jin P, Wang H, Wang N, Wan S, Yue L. IndoorSTG: A flexible tool to generate trajectory data for indoor moving objects. In Proc. the 14th International Conference on Mobile Data Management, June 2013, pp.341-343.

  111. Li H, Lu H, Chen X, Chen G, Chen K, Shou L. Vita: A versatile toolkit for generating indoor mobility data for real-world buildings. Proceedings of the VLDB Endowment, 2016, 9(13): 1453-1456.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jian-Qiu Xu

  2. Faculty of Mathematics and Computer Science, University of Hagen, 58097, Hagen, Germany

    Ralf Hartmut Güting

  3. JD Intelligent City Research, Beijing, 100176, China

    Yu Zheng

  4. Department of Computer Science, University of Illinois at Chicago, Chicago, 60607, U.S.A.

    Ouri Wolfson

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Xu, JQ., Güting, R.H., Zheng, Y. et al. Moving Objects with Transportation Modes: A Survey. J. Comput. Sci. Technol. 34, 709–726 (2019). https://doi.org/10.1007/s11390-019-1938-4

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