Shine on Transport Model Simulation Data: Web-based Visualization in R using Shiny

Microscopic transport demand models often use a large amount of data as input and provide detailed information for each individual trip as simulation output. Exploring this data can become very complex. Usually, several types of aggregation and disaggregation are performed on a spatial, temporal or demographic level. Consequently, often a combination of different tools is used for analyzing, communicating and validating the data. This paper introduces an interactive, user-friendly and scalable web application, which integrates different types of evaluations. Guidance and recommendations are given on how to implement such an application in R using Shiny.

[1]  Yihui Xie,et al.  A Wrapper of the JavaScript Library 'DataTables' , 2015 .

[2]  Antje von Schmidt,et al.  Applying Geovisualisation to Validate and Communicate Simulation Results of an Activity-based Travel Demand Model , 2015 .

[3]  Xiaoru Yuan,et al.  TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection , 2011, 2011 IEEE Pacific Visualization Symposium.

[4]  M. Bradley,et al.  The Sacramento activity-based travel demand model: estimation and validation results , 2006 .

[5]  Joseph Ying Jun Chow,et al.  Time-geographic relationships between vector fields of activity patterns and transport systems , 2015 .

[6]  Daniel Krajzewicz,et al.  Disaggregated Car Fleets in Microscopic Travel Demand Modelling , 2016, ANT/SEIT.

[7]  D. Heinrichs.  Autonomous Driving and Urban Land Use , 2016 .

[8]  Toshiyuki Yamamoto,et al.  Florida activity mobility simulator - Overview and preliminary validation results , 2005 .

[9]  Jj Allaire,et al.  Web Application Framework for R , 2016 .

[10]  Yihui Xie,et al.  Create Interactive Web Maps with the JavaScript 'Leaflet'Library , 2015 .

[11]  Daniel Krajzewicz,et al.  Introduction of car sharing into existing car fleets in microscopic travel demand modelling , 2017, Personal and Ubiquitous Computing.

[12]  Anita Graser,et al.  GIS and Transport Modeling - Strengthening the Spatial Perspective , 2016, ISPRS Int. J. Geo Inf..

[13]  Ian Muehlenhaus.  Visualize This: The FlowingData Guide to Design, Visualization, and Statistics , 2012 .

[14]  Olivier Klein,et al.  Visualizing Daily Mobility: Towards Other Modes of Representation , 2013 .

[15]  Daniel Krajzewicz,et al.  Generierung synthetischer Bevölkerungen für Verkehrsnachfragemodelle - Ein Methodenvergleich am Beispiel von Berlin , 2017 .