Dataset Information

Framework for fusing traffic information from social and physical transportation data.

ABSTRACT: Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global positioning system (GPS) data. Temporal-spatial information of traffic anomalies caused by the traffic events are then retrieved from anomalous traffic states. The proposed framework successfully identified accidental traffic events with various scales and exhibited strong performance in event descriptions. Even though social media messages are generally posted after the occurrence of anomalous traffic states, resourceful event descriptions in the messages are helpful in explaining traffic anomalies and for deploying suitable countermeasures.

SUBMITTER: Zheng Z

PROVIDER: S-EPMC6072031 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Framework for fusing traffic information from social and physical transportation data.

Zheng Zhihao Z Wang Chengcheng C Wang Pu P Xiong Yusha Y Zhang Fan F Lv Yisheng Y

PloS one 20180802 8

Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global position ...[more]

PMID: 30071064

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Project description:One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two-grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first-grade land use data, but also containing the internal detail information of second-grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.

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Dataset Information

Framework for fusing traffic information from social and physical transportation data.

Publications

Framework for fusing traffic information from social and physical transportation data.

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