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