Abstract
In recent years, artificial intelligence (AI) and its subarea of deep learning have drawn the attention of many researchers. At the same time, advances in technologies enable the generation or collection of large amounts of valuable data (e.g., sensor data) from various sources in different applications, such as those for the Internet of Things (IoT), which in turn aims towards the development of smart cities. With the availability of sensor data from various sources, sensor information fusion is in demand for effective integration of big data. In this article, we present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated and collected from various types of sensors, including remote sensed imagery for the geographic information system (GIS), accelerometers, as well as sensors for the global navigation satellite system (GNSS) and global positioning system (GPS). The discovered knowledge and information returned from our system provides analysts with a clearer understanding of trajectories or mobility of citizens, which in turn helps to develop better transportation models to achieve the ultimate goal of smarter cities. Evaluation results show the effectiveness and practicality of our AI-based sensor information fusion system for supporting deep supervised learning of big transportation data.
Highlights
Recent advances in technology have increased the popularity of the area of artificial intelligence (AI) [1,2], which aims to build “intelligent agents” with the ability to correctly interpret external data, learn from these data, and use the learned knowledge for cognitive tasks [3] like reasoning, planning, problem solving, decision making, motion and manipulation
We present an AI-based sensor-information fusion system, which integrates transportation data generated and collected from sensors for the geographic information system (GIS) and global navigation satellite/positioning system (GNSS/global positioning system (GPS))
We present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated, collected and integrated from various types of sensors
Summary
Recent advances in technology have increased the popularity of the area of artificial intelligence (AI) [1,2], which aims to build “intelligent agents” with the ability to correctly interpret external data, learn from these data, and use the learned knowledge for cognitive tasks [3] like reasoning, planning, problem solving, decision making, motion and manipulation. Subareas of AI include robotics, computer vision, natural language processing (NLP), and machine learning [4,5,6,7]. Within the latter, deep learning [8,9,10] has attracted the focus of many researchers. The development of AlphaGo (which uses deep reinforcement learning) for the board game of Go [11] has drawn the attention of researchers and the general public. Deep learning has been applied to the transportation domain [15,16], but for tasks like traffic flow forecasting, automatic vehicle detection, autonomous driving, and classification of speeding drivers. We present some background materials on works related to sensor information fusion and supervised learning of transportation data—traditional, sensor-based, and sensor fusion-based methods for urban data analytics and machine learning
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