Big Data for Social Transportation
Xinhu Zheng,Wei Chen,Pu Wang,Dayong Shen,Songhang Chen,Xiao Wang,Qingpeng Zhang,Liuqing Yang +7 more
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This paper overviews data sources, analytical approaches, and application systems for social transportation, and suggests a few future research directions for this new social transportation field.Abstract:
Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.read more
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Big Data Analytics in Intelligent Transportation Systems: A Survey
TL;DR: Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced.
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A hybrid deep learning based traffic flow prediction method and its understanding
TL;DR: A DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model.
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Big Data for Internet of Things: A Survey
TL;DR: This paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big data technologies across all the reviewed IoT domains.
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Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework.
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TL;DR: A novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP) is proposed, and experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting.
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