Air pollution has long been a serious environmental health challenge, especially in metropolitan cities, where air pollutant concentrations are exacerbated by the street canyon effect and high building density. Whilst accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models fail to fully address the complex interaction between air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep learning framework that combines a convolutional neural network with a long short-term memory network, aims to address this gap to provide fine-grained city-wide air pollution estimation and station-wide forecast. Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect. Using Hong Kong and Beijing as case studies, Deep-AIR achieves a higher accuracy than our baseline models. Our model attains an accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr, and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%, 75.3%, and 63.5% for Beijing. Our saliency analysis has revealed that for Hong Kong, street canyon and road density are the best estimators for NO2, while meteorology is the best estimator for PM2.5.
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；The University of Melbourne（墨尔本大学）；Bielefeld University（比勒费尔德大学）
Pukyong National University（釜庆国立大学）；DigiQuay Company Ltd.（DigiQuay 有限公司）；National Institute of Meteorological Research, Korea Meteorological Administration（韩国气象厅国立气象研究所）
It is important to calculate and analyze temperature and humidity prediction accuracies among quantitative meteorological forecasting. This study manipulates the extant neural network methods to foster the predictive accuracy. To achieve such tasks, we analyze and explore the predictive accuracy and performance in the neural networks using two combined meteorological factors (temperature and humidity). Simulated studies are performed by applying the artificial neural network (ANN), deep neural network (DNN), extreme learning machine (ELM), long short-term memory (LSTM), and long short-term mem…
Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab's Stubble Burning on AQI Variability
University of Westminster（威斯敏斯特大学）；University of Bedfordshire（贝德福德大学）
Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments
School of Computer Science（计算机科学学院）；Engineering, Vellore Institute of Technology（工程学院，维洛雷理工学院）；Chennai Campus（钦奈校区）；Chennai, Tamil Nadu 600127, India（钦奈，泰米尔纳德邦600127，印度）
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GPS data for the cities of Bengaluru, India and New York, USA. Long Short-Term Memory (LSTM) networks are used for modeling and incorporating information from spatial neighbors into the model. We find that the LSTM model based on input features extracted from a variable-sized polyg…
Taibah University（塔伊巴大学）；University of Manouba（马努巴大学）；HITEC University Taxila（塔克西拉HITEC大学）；Edinburgh Napier University（爱丁堡龙比亚大学）
Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveilling crops, coastal changes, flood risk assessment, and urban sprawl. This paper addresses the challenge of using time-series satellite images to predict urban expansion. Building upon previous work, we propose a novel two-step approach based on semantic image segmentation in order to predict urban expansion. The first step aims to extract information about urban regions at different time scales and prepare them for use in the trai…
University of Waterloo（滑铁卢大学）；University of Tehran（德黑兰大学）
Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using Long-Short Term Memory (LSTM) network for automatic detection of fr…
Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the perceived riskiness of cycling. Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. This requires the analysis of the factors surrounding accidents and the outcome. However, meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available…
TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting
Department of Mathematical Sciences, Ball State University（数学科学系，巴尔的摩州立大学）；Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris（统计学学科，明尼苏达-莫里斯大学）
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
University of Electronic Science and Technology of China（电子科技大学）；Monash University（墨尔本大学）；Shenzhen Institute for Advanced Study（深圳先进研究所）；Monash Energy Institute（墨尔本能源研究所）
Utilizing a Novel Deep Learning Method for Scene Categorization in Remote Sensing Data
University of Baghdad（巴格达大学）；Aliraqia University（伊拉克大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Warith Al-Anbiyaa（瓦里特·安比亚大学）；Universiti Tenaga Nasional (UNITEN)（马来西亚国民能源大学）；Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU)（共生国际大学共生理工学院）
