Evaluation of Spatio-Temporal Microsimulation Systems

The increasing expressiveness of spatio-temporal microsimulation systems makes them attractive for a wide range of real world applications. However, the broad field of applications puts new challenges to the quality of microsimulation systems. They are no longer expected to reflect a few selected mobility characteristics but to be a realistic representation of the real world. In consequence, the validation of spatio-temporal microsimulations has to be deepened and to be especially moved towards a holistic view on movement validation. One advantage hereby is the easier availability of mobility data sets at present, which enables the validation of many different aspects of movement behavior. However, these data sets bring their own challenges as the data may cover only a part of the observation space, differ in its temporal resolution, or not be representative in all aspects. In addition, the definition of appropriate similarity measures, which capture the various mobility characteristics, is challenging. The goal of this chapter is to pave the way for a novel, better, and more detailed evaluation standard for spatio-temporal microsimulation systems. The chapter collects and structure’s various aspects that have to be considered for the validation and comparison of movement data. In addition, it assembles the state-of-the-art of existing validation techniques. It concludes with examples of using big data sources for the extraction and validation of movement characteristics outlining the research challenges that have yet to be conquered. Evaluation of Spatio-Temporal Microsimulation Systems

[1]  Gennady Andrienko,et al.  A General Framework for Using Aggregation in Visual Exploration of Movement Data , 2010 .

[2]  Thomas Liebig,et al.  Visual Analytics for Understanding Spatial Situations from Episodic Movement Data , 2012, KI - Künstliche Intelligenz.

[3]  Simon Scheider,et al.  Pedestrian flow prediction in extensive road networks using biased observational data , 2008, GIS '08.

[4]  Dino Pedreschi,et al.  Understanding the patterns of car travel , 2013 .

[5]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[6]  J. Scheiner Social inequalities in travel behaviour: trip distances in the context of residential self-selection and lifestyles , 2010 .

[7]  Michael May,et al.  Sample Bias due to Missing Data in Mobility Surveys , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[8]  Thomas Liebig,et al.  Fast Visual Trajectory Analysis Using Spatial Bayesian Networks , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[9]  Clarke Wilson Analysis of Travel Behavior Using Sequence Alignment Methods , 1998 .

[10]  N. Andrienko,et al.  Basic Concepts of Movement Data , 2008, Mobility, Data Mining and Privacy.

[11]  Kay W. Axhausen,et al.  Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations , 2006 .

[12]  Christine Körner,et al.  Modeling Visit Potential of Geographic Locations Based on Mobility Data , 2012 .

[13]  Eric J. Miller,et al.  Comparison of MATSim and EMME/2 on Greater Toronto and Hamilton Area Network, Canada , 2010 .

[14]  Marek Junghans,et al.  Conceptual Approach for Determining Penetration Rates for Dynamic Indirect Traffic Detection Based on Bluetooth , 2012 .

[15]  Simon Scheider,et al.  A Vector-Geometry Based Spatial kNN-Algorithm for Traffic Frequency Predictions , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[16]  Andreas Horni,et al.  Location Choice Modeling for Shopping and Leisure Activi- ties with MATSim: Utility Function Extension and Validation Results , 2009 .

[17]  G. V. Kass An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[18]  Analyzing Temporal Usage Patterns of Street Segments Based on GPS Data - A Case Study in Switzerland , 2012 .

[19]  Daniel Schulz,et al.  Human Mobility from GSM Data - A Valid Alternative to GPS? , 2012 .

[20]  Stefan Wrobel,et al.  Spatiotemporal Modeling and Analysis—Introduction and Overview , 2012, KI - Künstliche Intelligenz.

[21]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[22]  Davy Janssens,et al.  Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform , 2010 .

[23]  Sung-Hyuk Cha Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[24]  Fritz Busch,et al.  Umfelddatenerfassung in Streckenbeeinflussungsanlagen, Testfeld "Eching Ost" des Bundes, Abschlussbericht 5. Testphase, Bundesministerium für Verkehr, Bau und Stadtentwicklung, 2010 , 2010 .

[25]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .