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Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

 
: Andrienko, G.; Andrienko, N.; Chen, W.; Maciejewski, R.; Zhao, Y.

:

IEEE transactions on intelligent transportation systems 18 (2017), Nr.8, S.2232-2249
ISSN: 1524-9050
European Commission EC
687591; datACRON
European Commission EC
688380; VaVeL
European Commission EC
699299; DART
European Commission EC
699303; INTUIT
European Commission EC
699260; BigData4ATM
National Science Foundation NSF
1350573, 1535031, 1637242
Englisch
Zeitschriftenaufsatz
Fraunhofer IAIS ()

Abstract
Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and the use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to the different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions, including social transportation, recommender systems, and policy implications.

: http://publica.fraunhofer.de/dokumente/N-480549.html