Journal ArticleDOI
Crowdsourcing in ITS: The State of the Work and the Networking
TLDR
The ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and the problems and challenges that need further research are investigated.Abstract:
In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.read more
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief
Charles Herget,Paul Kostek,William T. Scherer,Emily Sopensky,Stefano Stramigioli,Fei-Yue Wang,Daniel J. Dailey,A Lberto Broggi,Mark Brackstone,Hsinchun Chen,Swaroop Darbha,Bart De Schutter,Hussein Dia,Tom Dingus,Alan L. Erera,Azim Eskandarian,Toshio Fukuda,Roy Goudy,Brian Johnson,Isam Kaysi,Ryuji Kohno,Masao Kuwahara,Der-Horng Lee,Yili Liu,Hani S. Mahmassani,Ichiro Masaki,Umit Ozguner,Markos Papageorgiou,Nikolaos Papanikolopoulos,Nadine Sarter,William T. Scherer,Dipti Srinivasan,Christoph Stiller,H. Takahashi,Shuming Tang,Philip Tarnoff,Sadayuki Tsugawa,Ljubo Vlacic,Chelsea C. White,Sherman Wong,Alex Zelinsky,Y. Zhao,Nan-Ning Zheng,W. C Leon,Joseph V. L Illie,Arthur W. W Inston,Moshe Kam +46 more
Journal ArticleDOI
Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media
TL;DR: This paper develops a systematic approach to examine social media activities and sense event occurrences and proposes a parametric and convex optimization-based approach, called optimization and prediction with hybrid loss function (OPL), to fuse the linear regression and the results of seasonal autoregressive integrated moving average (SARIMA) model jointly.
Journal ArticleDOI
Social media security and trustworthiness: Overview and new direction
TL;DR: A new direction on evaluating and measuring those fundamental and underlying platforms is highlighted, meanwhile proposing a hierarchical architecture for crowd evaluations based on signaling theory and crowd computing, which is essential for social media ecosystem.
Journal ArticleDOI
Cyber-Physical-Social Systems: The State of the Art and Perspectives
Jun Jason Zhang,Fei-Yue Wang,Xiao Wang,Gang Xiong,Fenghua Zhu,Yisheng Lv,Jiachen Hou,Shuangshuang Han,Yong Yuan,Qingchun Lu,Yishi Lee +10 more
TL;DR: The blockchainized IoM technology and the concepts of parallel society are described to contribute to the transition from the current social construct to a futuristic intelligent society in China.
Proceedings ArticleDOI
Short-term traffic flow prediction with LSTM recurrent neural network
TL;DR: The long short-term memory (LSTM) recurrent neural network is employed to analyze the effects of various input settings on the LSTM prediction performances to show that the inclusion of occupancy/speed information may help to enhance the performance of the model overall.
References
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Crowdsourcing systems on the World-Wide Web
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Journal ArticleDOI
Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications
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Proceedings ArticleDOI
UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits
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TL;DR: This paper explores the use of personal ambient displays on mobile phones to give users feedback about sensed and self-reported transportation behaviors, and presents a working system for semi-automatically tracking transit activity and a visual design capable of engaging users in the goal of increasing green transportation.