Urban flows prediction from spatial-temporal data using machine learning: A survey
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；Institute of Artificial Intelligence, Southwest Jiaotong University（西南交通大学人工智能学院）；National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University（西南交通大学综合交通大数据应用技术国家工程实验室）
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.
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 …
Beihang University（北京航空航天大学）；Microsoft Research Asia（微软亚洲研究院）
Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-tempor…
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…
Central South University（中南大学）；Georgia Institute of Technology（佐治亚理工学院）；Peking University（北京大学）；Zhejiang University（浙江大学）
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolu…
Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data and geographical information for reconstructing the spatial and temporal patterns of urban traffic emissions. Vehicle emission factor models are employed to estimate emissions based on taxi trajectories. The estimated emissions are then mapped to spatial grids of urban areas to account for spatial hetero…
Fudan University（复旦大学）；Aalto University（阿尔托大学）；University of Chicago（芝加哥大学）
Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of urban traffic incidents. In this work, we aim to make use of the information of urban incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover urban traffic incidents with high impact on tr…
Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related information and fine-grained spatio-temporal logs has been recorded. Such helpful information can boost our understandings about the temporal evolution and spatial factors of urban crimes and can enhance accurate crime prediction. In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. In particular, we v…
With their continued increase in coverage and quality, data collected from personal air quality monitors has become an increasingly valuable tool to complement existing public health monitoring systems over urban areas. However, the potential of using such `citizen science data' for automatic early warning systems is hampered by the lack of models able to capture the high resolution, nonlinear spatio-temporal features stemming from local emission sources such as traffic, residential heating and commercial activities. In this work, we propose a machine learning approach to forecast high frequen…
We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment to minimize expected customer waiting times. Our scalable algorithm exploits the natural transportation network company topology by switching between two behaviors: distributed temporal-difference learning computed locally at each taxi and infrequent centralized Bellman updates computed at the dispatch center. We derive a regret bound and design the trigger condition between the two behaviors to e…
The spatio-temporal analysis of residential fires could allow decision makers to plan effective resource allocations in fire management according to fire clustering levels in space and time. In this study, we provide guidelines for the use of various methods in detecting the differences in clustering patterns of fire and non-fire (i.e., background residential) locations and how these patterns change over time. As a preliminary analysis step, various exploratory data analysis methods, such as, intensity plots (i.e., kernel density estimates) are used. Moreover, the use of Diggle's D-function (a…
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to offer generous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce financial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for cultural…
Land use land cover changes (LULCC) are generally modeled using multi-scale spatio-temporal variables. Recently, Markov Chain (MC) has been used to model LULCC. However, the model is derived from the proportion of LULCC observed over a given period and it does not account for temporal factors such as macro-economic, socio-economic, etc. In this paper, we present a richer model based on Hidden Markov Model (HMM), grounded in the common knowledge that economic, social and LULCC processes are tightly coupled. We propose a HMM where LULCC classes represent hidden states and temporal fac-tors repre…
The statistical quantification of temperature processes for the analysis of urban heat island (UHI) effects and local heat-waves is an increasingly important application domain in smart city dynamic modelling. This leads to the increased importance of real-time heatwave risk management on a fine-grained spatial resolution. This study attempts to analyze and develop new methods for modelling the spatio-temporal behavior of ground temperatures. The developed models consider higher-order stochastic spatial properties such as skewness and kurtosis, which are key components for understanding and de…
Real-time urban climate monitoring provides useful information that can be utilized to help monitor and adapt to extreme events, including urban heatwaves. Typical approaches to the monitoring of climate data include weather station monitoring and remote sensing. However, climate monitoring stations are very often distributed spatially in a sparse manner, and consequently, this has a significant impact on the ability to reveal exposure risks due to extreme climates at an intra-urban scale. Additionally, traditional remote sensing data sources are typically not received and analyzed in real-tim…
UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models
State University of New York at Binghamton（纽约州立大学布ingham顿分校）；San Diego State University（圣地亚哥州立大学）；University of Electronic Science and Technology of China（电子科技大学）；Worcester Polytechnic Institute（沃思堡理工学院）；Logistics and Supply Chain MultiTech R&D Centre（物流与供应链多技术研发中心）
Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）
Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles. Crucially, the efficiency of an MoD system highly depends on how well supply and demand distributions are aligned in spatio-temporal space (i.e., to satisfy user demand, cars have to be available in the correct place and at the desired time). To do so, we argue that predictive models should aim to explicitly disentangle between temporal} and spatial variability in the evolution of urban mobility demand. However, current appro…
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
The University of Hong Kong（香港大学）；The University of Queensland（昆士兰大学）；University of California, Los Angles（加州大学洛杉矶分校）；National University of Singapore（新加坡国立大学）
Toward an Integrated Cross-Urban Accident Prevention System: A Multi-Task Spatial-Temporal Learning Framework for Urban Safety Management
Discipline of Business Analytics, The University of Sydney Business School（悉尼大学商学院商业分析学科）；Newcastle Business School（新castle商学院）；Institute of Transport and Logistics Studies, The University of Sydney Business School（悉尼大学商学院运输与物流研究所）
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 rela…
Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First, different metro stations, e.g. transfer stations and non-transfer stations, have unique traffic patterns. Second, it is challenging to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to…
LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
National Innovative Institute of Defense Technology（国防科技创新研究院）；National University of Defense Technology（国防科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Electronic Science and Technology of China（电子科技大学）；Institute of Computing Technology, University of Chinese Academy of Sciences（中国科学院大学计算技术研究所）；College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智城）
In this paper, we have proposed STC-GEF, a novel Spatio-Temporal Cross-platform Graph Embedding Fusion approach for the urban traffic flow prediction. We have designed a spatial embedding module based on graph convolutional networks (GCN) to extract the complex spatial features within traffic flow data. Furthermore, to capture the temporal dependencies between the traffic flow data from various time intervals, we have designed a temporal embedding module based on recurrent neural networks. Based on the observations that different transportation platforms trip data (e.g., taxis, Uber, and Lyft)…
College of Engineering, Qatar University（卡塔尔大学工程学院）
Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To de…
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors, they cannot model time-evolving spatio-temporal correlation (STC) between sensors, and require pre-defined graphs, which are neither always available nor totally reliable, and target at only a specific type of sensor data at one time. Moreover, since the form of time-series f…
The objective of this study is to develop and test a novel structured deep-learning modeling framework for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold; first, the model i…
Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios
University of Gothenburg（哥德堡大学）；Chalmers University of Technology（查尔姆斯理工大学）
Hong Kong University of Science and Technology（香港科技大学）；University of Helsinki（赫尔辛基大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；JD Finance（京东金融）
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexp…
Data Science Lab, Hertie School, Berlin, Germany（海德堡学校数据科学实验室）；Centre for Sustainability, Hertie School, Berlin, Germany（海德堡学校可持续发展中心）；Department of Technology, Management and Economics, Intelligent Transportation Systems, Technical University of Denmark（技术、管理与经济系，智能交通系统，丹麦技术大学）
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）；University of Bologna（博洛尼亚大学）
Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and …
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）
Stanford University（斯坦福大学）；Tata Research Development and Design Centre（塔塔研究开发与设计中心）
Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine learning algorithms. In this exploratory work, we estimate the water consumption by the buildings in the region captured by satellite imagery. To this end, we break our analysis into three parts: i) Identification of building pixels, given a satellite image, followed by ii) identification of the building type (residential/non-residential) from the building pix…
University of Macau（澳门大学）；University of Central Florida（中佛罗里达大学）；Damo Academy, Alibaba Group（阿里巴巴集团达摩院）；Rutgers University（罗格斯大学）
Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolu…
In recent years, studying and predicting alternative mobility (e.g., sharing services) patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services. This need is aggravated during the current pandemic crisis, which pushes policymakers and private citizens to seek social-distancing compliant urban mobility services, such as electric bikes and scooter sharing offerings. However, predicting the number of incoming and outgoing vehicles for dif…
