Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel from the raw trajectory data? How can we infer such a mobility model from the single trajectory information? How can we further generalize the mobility inference to accommodate the real-world trajectory data that is sparsely sampled over time?
In this paper, based on formal and rigid definitions of the stay/travel mobility, we propose a single trajectory inference algorithm that utilizes a generic long-tailed sparsity pattern in the large-scale trajectory data. The algorithm guarantees a 100\% precision in the stay/travel inference with a provable lower-bound in the recall. Furthermore, we introduce an encoder-decoder learning architecture that admits multiple trajectories as inputs. The architecture is optimized for the mobility inference problem through customized embedding and learning mechanism. Evaluations with three trajectory data sets of 40 million urban users validate the performance guarantees of the proposed inference algorithm and demonstrate the superiority of our deep learning model, in comparison to well-known sequence learning methods. On extremely sparse trajectories, the deep learning model achieves a 2$\times$ overall accuracy improvement from the single trajectory inference algorithm, through proven scalability and generalizability to large-scale versatile training data.
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatio-temporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an uns…
The widespread use of positioning devices (e.g., GPS) has given rise to a vast body of human movement data, often in the form of trajectories. Understanding human mobility patterns could benefit many location-based applications. In this paper, we propose a novel generative model called TraLFM via latent factor modeling to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns …
Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Oxford Robotics Institute, Department of Engineering Science, University of Oxford（牛津大学机器人研究所、工程科学系、牛津大学）；Division of Robotics, Perception, and Learning (RPL), KTH Royal Institute of Technology（机器人、感知与学习 division，皇家理工学院）；Scania CV AB（Scania CV AB公司）；Bristol Robotics Laboratory (BRL), University of the West of England（布里斯托尔机器人实验室（BRL），西英格兰大学）
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 …
University of Pisa（比萨大学）；Institute of Information Sciences and Technologies (ISTI), CNR（意大利国家研究委员会信息科学与技术研究所）；University of Bristol（布里斯托大学）
The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of huma…
GTG: Generalizable Trajectory Generation Model for Urban Mobility
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）
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation
Zhejiang University（浙江大学）；Hangzhou City University（杭州城市大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学高等研究院）
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）
The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use …
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）
Beihang University（北京航空航天大学）；Pengcheng Laboratory（鹏城实验室）；Renmin University of China（中国人民大学）
Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect. Although many previous works have studied the problem of trajectory generation, the continuity of the generated trajectories has been neglected, which makes these methods useless for practical urban simulation scenarios. To solve this problem, we propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network, namely TS-TrajGen, which effic…
In this paper, we investigate the suitability of state-of-the-art representation learning methods to the analysis of behavioral similarity of moving individuals, based on CDR trajectories. The core of the contribution is a novel methodological framework, mob2vec, centered on the combined use of a recent symbolic trajectory segmentation method for the removal of noise, a novel trajectory generalization method incorporating behavioral information, and an unsupervised technique for the learning of vector representations from sequential data. Mob2vec is the result of an empirical study conducted o…
New York University（纽约大学）；Lockheed Martin Corporation（洛克希德·马丁公司）
Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermine its applicability. As mobility patterns are often repeated, we propose a method to use similar trajectory patterns from the local vicinity and probabilistically ensemble them to robustly reconstruct missing or unreliable observations. We evaluate the proposed approach in comparison with traditional functional trajectory interpolation using a case of sea vessel trajectory dat…
RMIT University（皇家墨尔本理工大学）；ELLIS Unit Alicante Foundation（ELLIS阿利坎特单元基金会）
Human mobility prediction is a core functionality in many location-based services and applications. However, due to the sparsity of mobility data, it is not an easy task to predict future POIs (place-of-interests) that are going to be visited. In this paper, we propose MobTCast, a Transformer-based context-aware network for mobility prediction. Specifically, we explore the influence of four types of context in the mobility prediction: temporal, semantic, social and geographical contexts. We first design a base mobility feature extractor using the Transformer architecture, which takes both the …
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to …
Exhaustive Circuit Mapping of a Single-Cell Foundation Model Reveals Massive Redundancy, Heavy-Tailed Hub Architecture, and Layer-Dependent Differentiation Control
Department of Computer Science, University of Tübingen（图宾根大学计算机科学系）
Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking
the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University（上海师范大学信息与机电工程学院）；Shanghai Urban-Rural Construction & Transportation Development Institute（上海城乡建设与交通发展研究院）；Zhongke Zidong Information Technology（中科紫东信息技术（北京）有限公司）
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
The University of Osaka（大阪大学）；The University of Tokyo（东京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Science Tokyo（东京科学大学）
Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for th…
Ocean University of China（中国海洋大学）；The University of Hong Kong（香港大学）
Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextual features in check-in data, resulting in poor performance for TUL task. In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL. Specifically, MainTUL is composed of a Recurrent Neural Network (RNN) trajectory encoder that models sequential patterns of input trajectory an…
Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is vulnerable to membership inference attacks. Current synthetic mobility dataset generators attempt to superficially match a priori modeled mobility characteristics which do not accurately reflect the real-world characteristics. Modeling human mobility to generate synthetic yet semantically and statistically realistic trajectories is therefore crucial for…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
A `trajectory' refers to a trace generated by a moving object in geographical spaces, usually represented by of a series of chronologically ordered points, where each point consists of a geo-spatial coordinate set and a timestamp. Rapid advancements in location sensing and wireless communication technology enabled us to collect and store a massive amount of trajectory data. As a result, many researchers use trajectory data to analyze mobility of various moving objects. In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic ne…
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
College of Computer Science, Zhejiang University（浙江大学计算机学院）；College of Software Technology, Zhejiang University（浙江大学软件技术学院）；State Key Laboratory of Blockchain and Security, Zhejiang University（浙江大学区块链与安全国家重点实验室）
Trajectory classification tasks became more complex as large volumes of mobility data are being generated every day and enriched with new sources of information, such as social networks and IoT sensors. Fast classification algorithms are essential for discovering knowledge in trajectory data for real applications. In this work we propose a method for fast discovery of subtrajectories with the reduction of the search space and the optimization of the MASTERMovelets method, which has proven to be effective for discovering interpretable patterns in classification problems.
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 …
TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Google Research, Mountain View, CA（谷歌研究，山景城，加利福尼亚州）；Google LLC, Mountain View, CA（谷歌公司，山景城，加利福尼亚州）；Dept. of Computer Science, University of Southern California, Los Angeles, CA（计算机科学系，南加州大学，洛杉矶，加利福尼亚州）；SEAI Lab, Dept. of Geography and the Environment, The University of Texas at Austin, Austin, TX（SEAI实验室，地理与环境系，德克萨斯大学奥斯汀分校，奥斯汀，德克萨斯州）
Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Texas A&M University（德克萨斯农工大学）；University of Louisville（路易斯维尔大学）；University of California Berkeley（加利福尼亚大学伯克利分校）；Novateur Research Solutions（诺瓦特研究解决方案公司）
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Department of Civil and Environmental Engineering, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系）；Novateur Research Solutions（Novateur研究解决方案）；Department of Civil and Environmental Engineering, University of California, Berkeley（加州大学伯克利分校土木与环境工程系）
CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China（香港理工大学建筑环境与能源工程系）；Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China（宁波东部先进研究所）；International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University, Hong Kong SAR, China（香港理工大学城市能源 nexus 中心）；Department of Computer and Systems Sciences, Stockholm University, Sweden（斯德哥尔摩大学计算机与系统科学系）；Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, China（浙江工业智能与数字孪生重点实验室）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究所）；LocationMind Inc., Tokyo 101-0042, Japan（LocationMind公司）
RMIT University（皇家墨尔本理工大学）；Cuebiq Inc.（Cuebiq公司）；University of New South Wales（新南威尔士大学）
Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory imputation and prediction. Nowadays, little existing work simultaneously deals with imputation and prediction on human trajectories. This work plans to explore whether the learning process of imputation and prediction could benefit from each other to achieve better outcomes. And the question will be answered by studying the coexistence patterns between missing points and observed ones in incomplete trajectories. More …
