2016
Journal article
Open Access
Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics
Andrienko N., Andrienko G., Rinzivillo S.
QA Visual Analytics Traffic modeling Information Systems Hardware and Architecture Software QA75 Mobility data mining
A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.
Source: Information systems (Oxf.) 57 (2016): 172–194. doi:10.1016/j.is.2015.08.007
Publisher: Pergamon,, Oxford , Regno Unito
1. Andrienko, G., Andrienko, N., Bak, P., Keim, D., and Wrobel, S. Visual Analytics of Movement. Springer, 2013.
2. Andrienko, N., Andrienko, G. Spatial Generalization and Aggregation of Massive Movement Data, IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.
3. Andrienko, N., Andrienko, G. A Visual Analytics Framework for Spatio-temporal Analysis and Modeling, Data Mining and Knowledge Discovery, 27(1): 55-83, 2013.
4. Andrienko, N., Andrienko, G., and Rinzivillo, S. Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics, Information Systems, 57(1): 172-194, 2016; Appendix: http://geoanalytics.net/and/is2015/
5. Gazis, D.C. Traffic Theory, Kliwer Academic, Boston, USA, 2002.
6. http://en.wikipedia.org/wiki/Fundamental_diagram_of_traffic_flow
7. Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D., and Giannotti, F. Understanding the Patterns of Car Travel. European Physics Journal Special Topics, 215, 61-73, 2013.
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2. Andrienko, N., Andrienko, G. Spatial Generalization and Aggregation of Massive Movement Data, IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.
3. Andrienko, N., Andrienko, G. A Visual Analytics Framework for Spatio-temporal Analysis and Modeling, Data Mining and Knowledge Discovery, 27(1): 55-83, 2013.
4. Andrienko, N., Andrienko, G., and Rinzivillo, S. Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics, Information Systems, 57(1): 172-194, 2016; Appendix: http://geoanalytics.net/and/is2015/
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6. http://en.wikipedia.org/wiki/Fundamental_diagram_of_traffic_flow
7. Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D., and Giannotti, F. Understanding the Patterns of Car Travel. European Physics Journal Special Topics, 215, 61-73, 2013.
[1] S. Afzal, R. Maciejewski, and D.S. Ebert. Visual analytics decision support environment for epidemic modeling and response evaluation. In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST'2011), pp. 191-200, 2011.
[2] G. Andrienko, N. Andrienko, P. Bak, D. Keim, and S. Wrobel. Visual Analytics of Movement. Springer, 2013.
[3] G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti, Interactive Visual Clustering of Large Collections of Trajectories, In Proc. IEEE Symp. Visual Analytics Science and Technology (VAST'09), pp. 3-10, 2009.
[4] N. Andrienko and G. Andrienko, Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach, Springer, Berlin, 2006.
[5] N. Andrienko and G. Andrienko, Spatial Generalization and Aggregation of Massive Movement Data, IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.
[6] N. Andrienko and G. Andrienko. Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization, 12(1): 3-24, 2013.
[7] N. Andrienko and G. Andrienko, A Visual Analytics Framework for Spatio-temporal Analysis and Modeling, Data Mining and Knowledge Discovery, 27(1): 55-83, 2013.
[8] N. Andrienko, G. Andrienko, and S. Rinzivillo. Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic. ISPRS International Journal of Geo-Information, 4(2): 591-606, 2015.
[9] P. Bak, M. Marder, S. Harary, A. Yaeli, and H.J. Ship, Scalable Detection of Spatiotemporal Encounters in Historical Movement Data, Computer Graphics Forum, 31(3-31): 915-924, June 2012.
[10]P. Bak, E. Packer, H. Ship, and D. Dotan, Algorithmic and Visual Analysis of Spatiotemporal Stops in Movement Data. In Proceedings of the 20th ACM SIGSPATIAL International Con-ference on Advances in Geographic Information Systems (ACM GIS 2012), November 6-9, 2012. Redondo Beach, CA, USA, 2012.
[11] M. Balmer, M. Rieser, K. Meister, D. Charypar, N. Lefebvre, K. Nagel, and K.W. Axhausen. MATSim-T: Architecture and Simulation Times. In A.L.C. Bazzan and F. Klügl (eds.) Multi-Agent Systems for Traffic and Transportation Engineering, 57-78, Information Science Reference, Hershey, 2009.
[12]M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, SUMO - Simulation of Urban MObility: An Overview, In Proc. 3rd Int. Conf. Advances in System Simulation (SIMUL 2011), pp. 63-68, 2011.
[13]M. Bögl, W. Aigner, P. Filzmoser, T. Lammarsch, S. Miksch, and A. Rind, Visual Analytics for Model Selection in Time Series Analysis, IEEE Trans. Visualization and Computer Graphics, 19(12): 2237-2246, 2013.
[14]E. Bourrel and J.-B. Lesort, Mixing Micro and Macro Representations of Traffic Flow: A Hybrid Model Based on the LWR Theory, 82th Ann. Meeting of the Transportation Research Board, Washington DC, 2003.
[15]W. Burghout, H.N. Koutsopoulos, and I. Andreasson, Hybrid Mesoscopic-Microscopic Traffic Simulation, Transportation Research Record, 1034: 218-225, 2005.
[16]W. Burghout, H.N. Koutsopoulos, and I. Andreasson, A Discrete-Event Mesoscopic Traffic Simulation Model for Hybrid Traffic Simulation, Proc. IEEE Intelligent Transportation Systems Conf. (ITSC'06), pp.- 1102 1107, 2006.
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[19]F.C. Daganzo, The Cell Transmission Model Part II: Network Traffic, Transportation Research Part B: Methodological, 29(2): 79-93, 1995.
[20]J.M. DelCastillo and F.G. Benitez, On the Functional Form of the Speed-Density Relationship I: General Theory, Transportation Research Part B: Methodological, 29(5): 373-389, 1995.
[21]U. Demšar, A.S. Fotheringham, and M. Charlton. Exrpilnog the spatio-temporal dynamics of geographical processes with Geographically Weighted Regression and Geovisual Analytics. Information Visualization, 7: 181-197, 2008.
[22]E.W. Dijkstra, A Note on Two Problems in Connexion with Graphs, Numerische Mathematik, 1: 269-271, 1959.
[23]J.A. Dykes and D.M. Mountain, Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications. Computational Statistics & Data Analysis; 43:581-603, 2003.
[24]S. van den Elzen and J.J. van Wijk, BaobabView: Interactive construction and analysis of decision trees, In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST'11), pp.-16501, 2011.
[25]S. Garg, J.E. Nam, I.V. Ramakrishnan, and K. Mueller, Model-driven Visual Analytics, In Proc. IEEE Symp. Visual Analytics Science and Technology (VAST'08), pp-.1296, 2008.
[26]S. Garg, S., I.V. Ramakrishnan, and K. Mueller, A Visual Analytics Approach to Model Learning, In Proc. IEEE Symp. Visual Analytics Science and Technology (VAST'10), pp-.7647, 2010.
[27]D.C. Gazis, Traffic Theory, Kliwer Academic, Boston, USA, 2002.
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[29]F. Giannotti and D. Pedreschi, editors. Mobility, Data Mining and Privacy - Geographic Knowledge Discovery. Springer, 2008.
[30]M. Gleicher. Explainers: Expert Explorations with Crafted Projections, IEEE Trans. Visualization and Computer Graphics, 19(12): 2042-2051, 2013.
[31]J. Gudmundsson, P. Laube, and T. Wolle. Computational Movement Analysis. In W. Kresse and D. M. Danko, editors, Springer Handbook of Geographic Information, pages 423-438. Springer Berlin Heidelberg, 2012.
[32]H. Guo, Z. Wang, B. Yu, H. Zhao, and X. Yuan, TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection. In: Proceedings of the Pacific Visualization Symposium PacificVis 2011, IEEE, pp. 163-170, 2011.
[33]Z. Guo, M.O. Ward, and E.A. Rundensteiner, Model Space Visualization for Multivariate Linear Trend Discovery, Proc. IEEE Symp. Visual Analytics Science and Technology (VAST'09), p-p8.27,52009.
[34]M.C. Hao, H. Janetzko, S. Mittelstädt, W. Hill, U. Dayal, D.A. Keim, M. Marwah, and R.K. Sharma, A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series, Computer Graphics Forum, 30(3): 691-700, 2011.
[35]D. Helbing, Derivation of a Fundamental Diagram for Urban Traffic Flow, The European Physical Journal B, 70: 229-241, 2009.
[36]C.C. Holt. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1): 5-10, January-March 2004.
[37]C. Hurter, B. Tissoires, and S. Conversy, FromDaDy: Spreading aircraft trajectories across views to support iterative queries. IEEE Transactions on Visualization and Computer Graphics, 15(6): 1017-1024, 2009.
[38]S.L.J. Jones, A.J. Sullivan, N. Cheekoti, M.D. Anderson, D. Malave, Traffic Simulation Software Comparison Study, UTCA Report 02217, University Transportation Center of Alabama, University of Alabama, USA, 2004.
[39]T. Kapler and W. Wright, GeoTime information visualization. Information Visualization, 4(2): 136-146, 2005.
[40]D.A. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann. Mastering the information age-solving problems with visual analytics. Eurographics, 2010.
[41]A. Konev, J. Waser, B. Sadransky, D. Cornel, R.A.P. Perdig o, . Horv th, and ME.. Gröller. RunWatchers: Automatic Simulation-Based Decision Support in Flood Management. IEEE Trans. Visualization and Computer Graphics, 20(12): 1873-1882, 2014.
[42]G. Kotushevski and K.A. Hawick, A Review of Traffic Simulation Software, Computational Science Technical Note CSTN-095, Computer Science, Massey University, Auckland, New Zealand, 2009.
[43]M.-J. Kraak and F. Ormeling, Cartography: visualization of spatial data. Second edition, Pearson Education Ltd, Harlow, UK, 2003.
[44]M.H. Lighthill and G.B. Whitham, On Kinematic Waves II: A Theory of Traffic Flow on Long Crowded Roads, Proc. Royal Society of London A229, 1178 (May): 317-345, 1955.
[45]P. Lundblad, O. Eurenius, and T. Heldring, Interactive Visualization of Weather and Ship Data. In: Proceedings of the 13th International Conference on Information Visualization IV2009. IEEE Computer Society Press, pp. 379-386, 2009.
[46]R. Maciejewski, P. Livengood, S. Rudolph, T.F. Collins, D.S. Ebert, R.T. Brigantic, C.D. Corley, G.A. Muller, and S.W. Sanders, A Pandemic Influenza Modeling and Visualization Tool, Journal of Visual Languages and Computing, 22: 268-278, 2011.
[47]K. Matković, D. Gračanin, M. Jelović, A. Ammer, A. Lež, and H. Hauser, Interactive Visual Analysis of Multiple Simulation Runs Using the Simulation Model View: Understanding and Tuning of an Electronic Unit Injector, IEEE Trans. Visualization and Computer Graphics, 16(6): 1449-1457, 2010.
[48]K. Matković, D. Gračanin, M. Jelović, and Y. Cao, Adaptive Interactive M-Ruelstiolution Computational Steering for Complex Engineering Systems. In Proc. EuroVA 2011, Bergen, Norway, pp. 45-48, 2011.
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BibTeX entry
@article{oai:it.cnr:prodotti:345628,
title = {Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics},
author = {Andrienko N. and Andrienko G. and Rinzivillo S.},
publisher = {Pergamon,, Oxford , Regno Unito},
doi = {10.1016/j.is.2015.08.007},
journal = {Information systems (Oxf.)},
volume = {57},
pages = {172–194},
year = {2016}
}CNR authorsLaboratories
0000-0003-4404-4147CNR ExploRA
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
Information Systems
City Research Online
Fraunhofer-ePrints
www.sciencedirect.com
