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MNTG: An Extensible Web-Based Traffic Generator

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Advances in Spatial and Temporal Databases (SSTD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8098))

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Abstract

Road network traffic datasets have attracted significant attention in the past decade. For instance, in spatio-temporal databases area, researchers harness road network traffic data to evaluate and validate their research. Collecting real traffic datasets is tedious as it usually takes a significant amount of time and effort. Alternatively, many researchers opt to generate synthetic traffic data using existing traffic generation tools, e.g., Brinkhoff and BerlinMOD. Unfortunately, existing road network traffic generators require significant amount of time and effort to install, configure, and run. Moreover, it is not trivial to generate traffic data in arbitrary spatial regions using existing traffic generators. In this paper, we propose Minnesota Traffic Generator (MNTG); an extensible web-based road network traffic generator that overcomes the hurdles of using existing traffic generators. MNTG does not provide a new way to simulate traffic data. Instead, it serves as a wrapper over existing traffic generators, making them easy to use, configure, and run for any arbitrary spatial road region. To generate traffic data, MNTG users just need to use its user-friendly web interface to specify an arbitrary spatial range on the map, select a traffic generator method, and submit the traffic generation request to the server. MNTG dedicated server will receive and process the submitted traffic generation request, and notify the user via email when finished. MNTG users can then download their generated data and/or visualize it on MNTG map interface. MNTG is extensible in two frontiers: (1) It can be easily extended to support various traffic generators. It is already shipped with the two most common traffic generators, Brinkhoff and BerlinMOD, yet, it also has the interface that can be used to add new traffic generators. (2) It can be easily extended to support various road network sources. It is shipped with U.S. Tiger files and Open Street Map, yet, it also has the interface that can be used to add other sources. MNTG is launched as a web service for public use; a prototype can be accessed via http://mntg.cs.umn.edu .

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References

  1. Zheng, Y., Chen, Y., Xie, X., Ma, W.-Y.: GeoLife2.0: A Location-Based Social Networking Service. In: MDM, pp. 357–358 (2009)

    Google Scholar 

  2. Brinkhoff, T.: A Framework for Generating Network-based Moving Objects. GeoInformatica 6(2), 153–180 (2002)

    Article  MATH  Google Scholar 

  3. Düntgen, C., Behr, T., Güting, R.H.: BerlinMOD: a Benchmark for Moving Object Databases. VLDB Journal 18(6), 1335–1368 (2009)

    Article  Google Scholar 

  4. Güting, R.H., Behr, T., Düntgen, C.: Secondo: A platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Engineering Bulletin 33(2), 56–63 (2010)

    Google Scholar 

  5. OpenStreetMaps, http://www.openstreetmap.org/

  6. US TIGER LINES, http://www.census.gov/geo/maps-data/data/tiger-line.html

  7. Pfoser, D., Theodoridis, Y.: Generating Semantics-based Trajectories of Moving Objects. Computers, Environment and Urban Systems 27(3), 243–263 (2003)

    Article  Google Scholar 

  8. Saglio, J.-M., Moreira, J.: Oporto: A realistic scenario generator for moving objects. GeoInformatica 5(1), 71–93 (2001)

    Article  MATH  Google Scholar 

  9. Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the Generation of Spatiotemporal Datasets. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 147–164. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  10. Tzouramanis, T., Vassilakopoulos, M., Manolopoulos, Y.: On the Generation of Time-Evolving Regional Data. GeoInformatica 6(3), 207–231 (2002)

    Article  MATH  Google Scholar 

  11. Gidófalvi, G., Pedersen, T.B.: ST-ACTS: A Spatio-temporal Activity Simulator. In: GIS, pp. 155–162 (2006)

    Google Scholar 

  12. Hu, H., Lee, D.-L.: GAMMA: A Framework for Moving Object Simulation. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 37–54. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  13. Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility): An Open-Source Traffic Simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling, pp. 183–187 (2002)

    Google Scholar 

  14. SMARTEST: Simulation Modelling Applied to Road Transport European Scheme Tests, http://www.its.leeds.ac.uk/projects/smartest/

  15. Xu, J., Güting, R.H.: MWGen: A Mini World Generator. In: MDM, pp. 258–267 (2012)

    Google Scholar 

  16. Tsai, H.-P., Yang, D.-N., Chen, M.-S.: Mining Group Movement Patterns for Tracking Moving Objects Efficiently. IEEE TKDE 23(2), 266–281 (2011)

    Google Scholar 

  17. Jeung, H., Liu, Q., Shen, H.T., Zhou, X.: A Hybrid Prediction Model for Moving Objects. In: ICDE, pp. 70–79 (2008)

    Google Scholar 

  18. Mokbel, M.F., Xiong, X., Aref, W.G.: SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases. In: SIGMOD, pp. 623–634 (2004)

    Google Scholar 

  19. Wu, W., Guo, W., Tan, K.-L.: Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects. In: ICDE, pp. 1116–1125 (2007)

    Google Scholar 

  20. Mokbel, M.F., Aref, W.G.: SOLE: Scalable On-line Execution of Continuous Queries on Spatio-temporal Data Sreams. VLDB Journal 17(5), 971–995 (2008)

    Article  Google Scholar 

  21. Chung, B.S.E., Lee, W.-C., Chen, A.L.P.: Processing Probabilistic Spatio-temporal Range Queries Over Moving Objects with Uncertainty. In: EDBT, pp. 60–71 (2009)

    Google Scholar 

  22. Hu, H., Xu, J., Lee, D.L.: PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects. IEEE TKDE 22(3), 404–419 (2010)

    Google Scholar 

  23. Chen, S., Jensen, C.S., Lin, D.: A Benchmark for Evaluating Moving Object Indexes. VLDB Journal 1(2), 1574–1585 (2008)

    Google Scholar 

  24. Laender, A.H.F., Borges, K.A.V., Carvalho, J.C.P., Medeiros, C.B., da Silva, A.S., Davis, C.A.: Integrating Web Data and Geographic Knowledge into Spatial Databases. In: Spatial Databases, pp. 23–47 (2005)

    Google Scholar 

  25. Shen, C., Huang, Y., Powell, J.W.: The Design of a Benchmark for Geo-stream Management Systems. In: GIS, pp. 409–412 (2011)

    Google Scholar 

  26. Tzouramanis, T.: Benchmarking and Data Generation in Moving Objects Databases. In: Encyclopedia of Database Technologies and Applications, pp. 23–28 (2005)

    Google Scholar 

  27. Xu, J., Güting, R.H.: GMOBench: A Benchmark for Generic Moving Objects. In: GIS, pp. 410–413 (2012)

    Google Scholar 

  28. Güting, R.H., de Almeida, V.T., Ding, Z.: Modeling and Querying Moving Objects in Networks. VLDB Journal 15(2), 165–190 (2006)

    Article  Google Scholar 

  29. Vazirgiannis, M., Wolfson, O.: A Spatiotemporal Model and Language for Moving Objects on Road Networks. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 20–35. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  30. Eldawy, A., Mokbel, M.F.: A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data. In: VLDB (2013)

    Google Scholar 

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Mokbel, M.F. et al. (2013). MNTG: An Extensible Web-Based Traffic Generator. In: Nascimento, M.A., et al. Advances in Spatial and Temporal Databases. SSTD 2013. Lecture Notes in Computer Science, vol 8098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40235-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-40235-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40234-0

  • Online ISBN: 978-3-642-40235-7

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