An urban big data-based air quality index prediction: A case study of routes planning for outdoor activities in Beijing

• Main Authors: ZOU, Zhiqiang, CAI, Tao, CAO, Kai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Urban big data; data mining; urban air quality; deep neural network; routes planning; Asian Studies; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/5423; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6426&context=sis_research
• Institution: Singapore Management University

Urban big data include various types of datasets, such as air quality data, meteorological data, and weather forecast data. Air quality index is broadly used in many countries as an indicator to measure the air pollution status. This indicator has a great impact on outdoor activities of urban residents, such as long-distance cycling, running, jogging, and walking. However, for routes planning for outdoor activities, there is still a lack of comprehensive consideration of air quality. In this paper, an air quality index prediction model (namely airQP-DNN) and its application are proposed to address the issue. This paper primarily consists of two components. The first component is to predict the future air quality index based on a deep neural network, using historical air quality datasets, current meteorological datasets, and weather forecasting datasets. The second component refers to a case study of outdoor activities routes planning in Beijing, which can help plan the routes for outdoor activities based on the airQP-DNN model, and allow users to enter the origin and destination of the route for the optimized path with the minimum accumulated air quality index. The air quality monitoring datasets of Beijing and surrounding cities from April 2014 to April 2015 (over 758,000 records) are used to verify the proposed airQP-DNN model. The experimental results explicitly demonstrate that our proposed model outperforms other commonly used methods in terms of prediction accuracy, including autoregressive integrated moving average model, gradient boosted decision tree, and long short-term memory. Based on the airQP-DNN model, the case study of outdoor activities routes planning is implemented. When the origin and destination are specified, the optimized paths with the minimum accumulated air quality index would be provided, instead of the standard static Dijkstra shortest path. In addition, a Web-GIS-based prototype has also been successfully developed to support the implementation of our proposed model in this research. The success of our study not only demonstrates the value of the proposed airQP-DNN model, but also shows the potential of our model in other possible extended applications.