The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of ``phantom transitions'' arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.
On Predicting Sociodemographics from Mobility Signals
Department of Civil and Environmental Engineering, University of Washington（土木与环境工程系，华盛顿大学）；Department of Technology, Management and Economics, Technical University of Denmark（技术、管理与经济系，丹麦技术大学）
Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（清华大学电子工程系、北京信息科学与技术国家研究中心）
With the growth of using cell phones and the increase in diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis of CDR is one of the favorite subjects of study among the researchers. The user next location prediction is one of the main problems in the field of human mobility analysis. In this paper, we propose a data processing framework to predict user next location. We propose domain-spec…
University of New South Wales（新南威尔士大学）；University of Wollongong（伍伦贡大学）
In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For example, in the task of predicting Place-of-Interest (POI) customer flows, typically the number of visits is extracted from historical logs, and only the numerical data are used to predict visitor flows. In this research, we perform the forecasting task directly on the natural language input that includes all kinds of information such as numerical values and contextual semantic information. Specific prompts are introduced…
The University of Iowa（爱荷华大学）；Worcester Polytechnic Institute（伍斯特理工学院）
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead…
Universidade Federal de São Paulo (UNIFESP)（圣保罗联邦大学）
In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on th…
Southwestern University of Finance and Economics（西南财经大学）；University of Electronic Science and Technology（电子科技大学）；Iowa State University（爱荷华州立大学）
Deep neural networks have recently achieved considerable improvements in learning human behavioral patterns and individual preferences from massive spatial-temporal trajectories data. However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions. In addition, the inherent sparsity and under-explored heterogeneous collaborative items pertaining to human check-ins hinder the potential exploitation of human diverse periodic regul…
Machine Learning for Smart Mobility, Transport Division, DTU Management（丹麦技术大学管理学院交通系智慧出行机器学习组）
Predicting the supply and demand of transport systems is vital for efficient traffic management, control, optimization, and planning. For example, predicting where from/to and when people intend to travel by taxi can support fleet managers to distribute resources; better predicting traffic speeds/congestion allows for pro-active control measures or for users to better choose their paths. Making spatio-temporal predictions is known to be a hard task, but recently Graph Neural Networks (GNNs) have been widely applied on non-euclidean spatial data. However, most GNN models require a predefined gr…
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…
Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, an…
Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representin…
Massachusetts Institute of Technology（麻省理工学院）；The University of Hong Kong（香港大学）；Northeastern University（东北大学）
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. To address this issue, this study develops an activity-based modeling framework for individual mobility prediction. Specifically, an input-output hidden Markov model (IOHMM) framework is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual's next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration a…
A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs
Groupe de Recherche de Prospection et Valorisation des Données (Greprovad)（数据勘探与增值研究组（Greprovad））；GRIC；Université de Sherbrooke（谢布鲁克大学）；University of Kinshasa（金沙萨大学）；Montreal Behavioural Medicine Centre（蒙特利尔行为医学中心）；Centre Intégré Universitaire de Santé et Services Sociaux du Nord-de-l’Île-de-Montréal (CIUSSS-NIM)（蒙特利尔岛北部综合大学卫生与社会服务中心（CIUSSS-NIM））；LISV-UVSQ；Université Paris-Saclay（巴黎-萨克雷大学）；University of Klagenfurt（克拉根福大学）；Biomedical Research Unit, Hospital Monkole（蒙科尔医院生物医学研究单元）；University of Florida（佛罗里达大学）；School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气工程与计算机科学学院）；Centre National d’Intelligence Épidémiologique (CNIEP)（国家流行病学情报中心（CNIEP））
Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs),…
KTH, Royal Institute of Technology（KTH皇家理工学院）；Huawei（华为）
Predicting the future location of users in wireless net- works has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given use…
Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of the human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to…
BERT4Traj: Transformer Based Trajectory Reconstruction for Sparse Mobility Data
University of Georgia（佐治亚大学）；Department of Geography University of Georgia（佐治亚大学地理系）；College of Public Health University of Georgia（佐治亚大学公共卫生学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
RMIT University（皇家墨尔本理工大学）；The University of Melbourne（墨尔本大学）；Swinburne University of Technology（斯威本科技大学）
Traffic prediction is a flourishing research field due to its importance in human mobility in the urban space. Despite this, existing studies only focus on short-term prediction of up to few hours in advance, with most being up to one hour only. Long-term traffic prediction can enable more comprehensive, informed, and proactive measures against traffic congestion and is therefore an important task to explore. In this paper, we explore the task of long-term traffic prediction; where we predict traffic up to 24 hours in advance. We note the weaknesses of existing models--which are based on recur…
Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case scenarios such as predicting visit counts of individuals' for a given spatial area at a particular temporal resolution using raster/image format representation of the geographical region, since the movement patterns of an individual can be largely restricted and localized to a certain part of the raster. Additionally, current deep-learning approaches …
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
ETH Zurich（苏黎世联邦理工学院）；Swiss Data Science Center（瑞士数据科学中心）；Institute of Cartography and Geoinformation, ETH Zurich（苏黎世联邦理工学院制图与地理信息研究所）；Institute for Machine Learning, Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系机器学习研究所）；EPFL（洛桑联邦理工学院）
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）
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；University College London（伦敦大学学院）
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature an…
Ludwig Maximilian Universität（路德维希-马克西米利安大学）；Helmholtz Zentrum München – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. Several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches are often applying standard models of the respective research field. This habit, however, often comes along with certain restrictions. For instance, most statistical or epidemiological models cannot directly incor…
National University of Science and Technology（巴基斯坦国立科技大学）
Vehicle route prediction is one of the significant tasks in vehicles mobility. It is one of the means to reduce the accidents and increase comfort in human life. The task of route prediction becomes simpler with the development of certain machine learning and deep learning libraries. Meanwhile, the security and privacy issues are always lying in the vehicle communication as well as in route prediction. Therefore, we proposed a framework which will reduce these issues in vehicle communication and predict the route of vehicles in crossroads. Specifically, our proposed framework consists of two m…
Polytechnic University of Catalonia（加泰罗尼亚理工大学）；Universiteit Antwerpen - imec（安特卫普大学-imec）
Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will "track" the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as …
