Urban flow forecasting is a challenging task, given the inherent periodic characteristics of urban flow patterns. To capture the periodicity, existing urban flow prediction approaches are often designed with closeness, period, and trend components extracted from the urban flow sequence. However, these three components are often considered separately in the prediction model. These components have not been fully explored together and simultaneously incorporated in urban flow forecasting models. We introduce a novel urban flow forecasting architecture, TERMCast. A Transformer based long-term relation prediction module is explicitly designed to discover the periodicity and enable the three components to be jointly modeled This module predicts the periodic relation which is then used to yield the predicted urban flow tensor. To measure the consistency of the predicted periodic relation vector and the relation vector inferred from the predicted urban flow tensor, we propose a consistency module. A consistency loss is introduced in the training process to further improve the prediction performance. Through extensive experiments on three real-world datasets, we demonstrate that TERMCast outperforms multiple state-of-the-art methods. The effectiveness of each module in TERMCast has also been investigated.
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
Sichuan University（四川大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；McGill University（麦吉尔大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）
Universidad Nacional de Educación a Distancia — UNED（西班牙国立远程教育大学）；Inverence
Air quality has become one of the main issues in public health and urban planning management, due to the proven adverse effects of high pollutant concentrations. Considering the mitigation measures that cities all over the world are taking in order to face frequent low air quality episodes, the capability of foreseeing future pollutant concentrations is of great importance. Through this paper, we present SOCAIRE, an operational tool based on a Bayesian and spatiotemporal ensemble of neural and statistical nested models. SOCAIRE integrates endogenous and exogenous information in order to predic…
Artificial Intelligence Department Universidad Nacional de Educación a Distancia — UNED（西班牙马德里国立远程教育大学（UNED）人工智能系）
Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of …
Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data
Urban Transport Systems Laboratory (LUTS), École Polytechnique Fédérale de Lausanne (EPFL)（城市交通系统实验室（LUTS），瑞士联邦理工学院（EPFL））；Visual Intelligence for Transportation Laboratory (VITA), École Polytechnique Fédérale de Lausanne (EPFL)（交通视觉智能实验室（VITA），瑞士联邦理工学院（EPFL））
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity. Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment. In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints. The model conducts graph convolution operations on speed data with multi-weighted a…
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
Southeast University（东南大学）；Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies（江苏省现代城市交通技术协同创新中心）；City University of Hong Kong（香港城市大学）；School of Mathematics, Southeast University（东南大学数学学院）；Systems Research Institute of the Polish Academy of Sciences（波兰科学院系统研究所）；Luoyang Normal University（洛阳师范学院）；Purple Mountain Laboratories（紫金山实验室）；AGH University of Krakow（AGH科技大学）
The Pennsylvania State University（宾夕法尼亚州立大学）；University of Utah（犹他大学）
The world is increasingly urbanizing and the building industry accounts for more than 40% of energy consumption in the United States. To improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are limited in their abilities to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those models ei…
University of Patras（帕特雷大学）；Roma Tre University（罗马第三大学）；National Technical University of Athens（雅典国立技术大学）
Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one…
Urban Water Consumption Forecasting Using Deep Learning and Correlated District Metered Areas
KIOS Research and Innovation Center of Excellence（KIOS研究与创新中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Cyprus（塞浦路斯大学）
FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention（上海大气颗粒污染与防治重点实验室）；National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary（长江入海口湿地生态系统观测与研究站）；Department of Environmental Science and Engineering（环境科学与工程学院）；Shanghai Academy of AI for Science (SAIS)（上海人工智能科学研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Institute for Energy and Carbon Neutrality Strategy（上海能源与碳中和战略研究院）；IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health（国际减灾与发展研究院风险互联与治理国际联合实验室）；Shanghai Institute of Eco-Chongming (SIEC)（上海生态崇明研究院）；Shanghai Environmental Monitoring Center (SEMC)（上海市环境监测中心）
The Pennsylvania State University（宾夕法尼亚州立大学）；National Center for Atmospheric Research（美国国家大气研究中心）
The rising temperature is one of the key indicators of a warming climate, and it can cause extensive stress to biological systems as well as built structures. Due to the heat island effect, it is most severe in urban environments compared to other landscapes due to the decrease in vegetation associated with a dense human-built environment. It is essential to adequately monitor the local temperature dynamics to mitigate risks associated with increasing temperatures, which can include short term strategy to protect people and animals, to long term strategy to how to build a new structure and cop…
Nanchang University（南昌大学）；ETH Zurich（苏黎世联邦理工学院）；The Hong Kong Polytechnic University（香港理工大学）
Time series forecasting has widespread applications in urban life ranging from air quality monitoring to traffic analysis. However, accurate time series forecasting is challenging because real-world time series suffer from the distribution shift problem, where their statistical properties change over time. Despite extensive solutions to distribution shifts in domain adaptation or generalization, they fail to function effectively in unknown, constantly-changing distribution shifts, which are common in time series. In this paper, we propose Hyper Time- Series Forecasting (HTSF), a hypernetwork-b…
LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
Electrical and Electronic Engineering Department, California State University-Sacramento（电气与电子工程系，加州州立大学萨克拉门托分校）；Electrical and Electronic Engineering Department, University of California, Riverside（电气与电子工程系，加州大学河滨分校）；Computer Science Department, California State University, Northridge（计算机科学系，加州州立大学北岭分校）；Computer Engineering Department, California State University, Northridge（计算机工程系，加州州立大学北岭分校）
本文提出一种结合插值、归一化与特征提取的 LSTM 充电负荷预测框架，可在日、周、月尺度上准确预测多地点 EV 充电需求，并支持不同部署场景。
TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model
Purdue University（普渡大学）；George Washington University（乔治·华盛顿大学）；University of Pennsylvania（宾夕法尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；Koforidua Technical University（科福里杜亚技术大学）
Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）；Princeton University（普林斯顿大学）；The University of Arizona（亚利桑那大学）；University of South California（南加利福尼亚大学）；Duke Kunshan University（昆山杜克大学）
Delft University of Technology（代尔夫特理工大学）；City University of Hong Kong（香港城市大学）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep learning approach, Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) is proposed to forecast passenger flow in urban rail transit systems to incorporate these complex factors. We propose to use multiple graphs to encode the spatial and other heterogen…
CoNOAir: A Neural Operator for Forecasting Carbon Monoxide Evolution in Cities
Indian Institute of Technology Delhi（印度德里理工学院）；Indian Institute of Science Bengaluru（班加罗尔印度科学学院）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度德里理工学院亚迪人工智能学院）
The release of toxic gases by industries, emissions from vehicles, and an increase in the concentration of harmful gases and particulate matter in the atmosphere are all contributing factors to the deterioration of the quality of the air. Factors such as industries, urbanization, population growth, and the increased use of vehicles contribute to the rapid increase in pollution levels, which can adversely impact human health. This paper presents a model for forecasting the air quality index in Nigeria using the Bi-directional LSTM model. The air pollution data was downloaded from an online data…
Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is challenging due to the fact that the road network contains complex and time-varying spatial-temporal dependencies. Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies. However, the existing methods often construct the graph only based on road network …
Department of Computer Science, University of Virginia（弗吉尼亚大学计算机科学系）
Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data is updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present TDEFSI (Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yiel…
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental science to evaluate the dangerous impact of particulate matters on public health. Besides that, deterministic models of air pollutant behavior are also generated; however, this is both complex and often inaccurate. On the contrary, deep recurrent neural network reveals potent potent…
Exploring the impact of weather on Metro demand forecasting using machine learning method
Qiushi Honors College, Tianjin University（天津大学求是学部）；School of Traffic and Transportation, Beijing Jiaotong University（北京交通大学交通运输学院）；Maynooth International Engineering College, Fuzhou University（福州大学梅努斯国际工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Forestry University（北京林业大学）
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic flow forecasting. For neural network MTL, a backpropagation (BP) network is constructed by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output …
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focused on modeling the spatial dependencies using the distance only. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and …
WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
NIT Meghalaya（梅加拉亚国家理工学院）；Sorbonne University（索邦大学）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；Universiti Kebangsaan（马来西亚国民大学）；University of Philippines（菲律宾大学）；Institute of Environmental Science and Meteorology（环境科学与气象研究所）
Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlation. In urban water distribution systems (WDS), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of monitored flow and pressure time series are of vital importance for operational decision making, alerts and anomaly detection. To address this issue, we proposed a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN). Our model consists of two stages: a spatial attention-based encoder and a tem…
Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting
School of Production Engineering and Management, Technical University of Crete（克里特技术大学生产工程与管理学院）；Quantum Innovation Pc（量子创新公司）；Alma-Sistemi Srl（阿尔玛系统有限公司）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；School of Electrical and Computer Engineering, Technical University of Crete（克里特技术大学电气与计算机工程学院）；QUBITECH Pc（QUBITECH公司）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；School of Civil Engineering, National Technical University of Athens（雅典国家技术大学土木工程学院）
Artificial Intelligence Department Universidad Nacional de Educación a Distancia — UNED（西班牙马德里开放大学（UNED）人工智能系）
High concentration episodes for NO$_2$ are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting is a family of techniques that allow for the prediction of the expected distribution function instead of a single value. In the case of NO$_2$, it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. We thoroughly compared 10 state o…
Chiang Mai University（清迈大学）；Data Science Research Center（数据科学研究中心）
Short-term precipitation forecasting is essential for planning of human activities in multiple scales, ranging from individuals' planning, urban management to flood prevention. Yet the short-term atmospheric dynamics are highly nonlinear that it cannot be easily captured with classical time series models. On the other hand, deep learning models are good at learning nonlinear interactions, but they are not designed to deal with the seasonality in time series. In this study, we aim to develop a forecasting model that can both handle the nonlinearities and detect the seasonality hidden within the…
Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models
School of Computing, National University of Computer & Emerging Sciences (FAST)（国家计算机与新兴科学大学（FAST）计算学院）；College of Computing and Informatics, Saudi Electronic University（沙特电子大学计算与信息学院）
Peking University（北京大学）；Beijing Institute of Big Data Research（北京大数据研究院）
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete con…
Frequency-Aware Attention-LSTM for PM$_{2.5}$ Time Series Forecasting
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；College of Computer Science and AI Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Information Technology Guangxi Police College（广西警察学院信息技术学院）
Norwegian University of Life Sciences（挪威生命科学大学）；Rosim AS（Rosim公司）
Combined Sewer Overflow (CSO) is a major problem to be addressed by many cities. Understanding the behavior of sewer system through proper urban hydrological models is an effective method of enhancing sewer system management. Conventional deterministic methods, which heavily rely on physical principles, is inappropriate for real-time purpose due to their expensive computation. On the other hand, data-driven methods have gained huge interests, but most studies only focus on modeling a single component of the sewer system and supply information at a very abstract level. In this paper, we propose…
Advances in traffic forecasting technology can greatly impact urban mobility. In the traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented detail, with traffic volume and speed information available at 5 minute intervals and high spatial resolution. To improve generalization to unknown cities, as required in the 2021 extended challenge, we propose to predict small quadratic city sections, rather than processing a full-city-raster at once. At test time, breaking down the test data into spatially-cropped overlapping snippets improves stability and robust…
