Visual Analytics Indicators for Mobility and Transportation

Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.

[1]  Kawa Nazemi,et al.  Adaptive Semantics Visualization , 2016, Studies in Computational Intelligence.

[2]  Qi Han,et al.  Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data , 2019, Comput. Graph. Forum.

[3]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  Khanh Q. Bui,et al.  The Compliance Challenges in Emissions Control Regulations to Reduce Air Pollution from Shipping , 2019, OCEANS 2019 - Marseille.

[6]  Luis G. Willumsen,et al.  Transport demand model estimation from traffic counts , 1989 .

[7]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[8]  Antonin Danalet,et al.  Location choice with longitudinal WiFi data , 2016 .

[9]  Daniel A. Keim,et al.  Mastering the Information Age - Solving Problems with Visual Analytics , 2010 .

[10]  Feng Gao,et al.  Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks , 2016, Expert Syst. Appl..

[11]  Arjan Kuijper,et al.  Visual trend analysis with digital libraries , 2015, I-KNOW.

[12]  Darya Filippova,et al.  ICE--visual analytics for transportation incident datasets , 2009, 2009 IEEE International Conference on Information Reuse & Integration.

[13]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[14]  Jarke J. van Wijk,et al.  Challenges for Visual Analytics , 2017, VISIGRAPP.

[15]  Kawa Nazemi,et al.  Visual Analytics for Analyzing Technological Trends from Text , 2019, 2019 23rd International Conference Information Visualisation (IV).

[16]  James J. Thomas.  Visual analytics: A grand challenge in science - turning information overload into the opportunity of the decade , 2007, APVIS.

[17]  Hai Jin,et al.  Predicting Transportation Carbon Emission with Urban Big Data , 2017, IEEE Transactions on Sustainable Computing.

[18]  Lauren P. Alexander,et al.  Cell phone location data for travel behavior analysis , 2015 .

[19]  H. Ansoff,et al.  Managing Strategic Surprise by Response to Weak Signals , 1975 .

[20]  Ross Maciejewski,et al.  Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions , 2017, IEEE Transactions on Intelligent Transportation Systems.

[21]  Xin Li,et al.  Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology , 2019, Technological Forecasting and Social Change.

[22]  Kawa Nazemi,et al.  A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management , 2019, ISVC.

[23]  Daniel A. Keim,et al.  Visual Analytics of Movement , 2013, Springer Berlin Heidelberg.

[24]  Sun Drying.  Unit 1 , 2020, Chinese Primer.

[25]  Darya Filippova,et al.  Visual Analytics for Transportation Incident Data Sets , 2009 .