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A traffic pattern detection algorithm based on multimodal sensing

التفاصيل البيبلوغرافية
العنوان: A traffic pattern detection algorithm based on multimodal sensing
المؤلفون: Yanjun Qin, Haiyong Luo, Fang Zhao, Zhongliang Zhao, Mengling Jiang
المصدر: International Journal of Distributed Sensor Networks, Vol 14 (2018)
بيانات النشر: Hindawi - SAGE Publishing, 2018.
سنة النشر: 2018
المجموعة: LCC:Electronic computers. Computer science
مصطلحات موضوعية: Electronic computers. Computer science, QA75.5-76.95
الوصف: Nowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode detection. Using the transportation mode detection, results can increase commuting efficiency and optimize metropolitan transportation planning. Although much work has been done on transportation mode detection problem, the accuracy is not sufficient. In this article, an accurate traffic pattern detection algorithm based on multimodal sensing is proposed. This algorithm first extracts various sensory features and semantic features from four types of sensor (i.e. accelerator, gyroscope, magnetometer, and barometer). These sensors are commonly embedded in commodity smartphones. All the extracted features are then fed into a convolutional neural network to infer traffic patterns. Extensive experimental results show that the proposed scheme can identify four transportation patterns with 94.18% accuracy.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1550-1477
15501477
Relation: https://doaj.org/toc/1550-1477
DOI: 10.1177/1550147718807832
URL الوصول: https://doaj.org/article/03cd8158f5f74cb0a36673e7baf8f185
رقم الأكسشن: edsdoj.03cd8158f5f74cb0a36673e7baf8f185
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:15501477
DOI:10.1177/1550147718807832