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 unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as $k$-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses.
Extracting significant places or places of interest (POIs) using individuals' spatio-temporal data is of fundamental importance for human mobility analysis. Classical clustering methods have been used in prior work for detecting POIs, but without considering temporal constraints. Usually, the involved parameters for clustering are difficult to determine, e.g., the optimal cluster number in hierarchical clustering. Currently, researchers either choose heuristic values or use spatial distance-based optimization to determine an appropriate parameter set. We argue that existing research does not o…
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…
Singapore University of Technology and Design（新加坡科技设计大学）
Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely k-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through s…
Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patterns of dependency between such measures and their effects on disease incidence may vary dynamically and across different populations. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. We used a novel application of Optimal Transport …
Parkinson's disease (PD) is a common neurodegenerative disease with a high degree of heterogeneity in its clinical features, rate of progression, and change of variables over time. In this work, we present a novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of PD subtypes and 2) early prediction of disease progression in individual patients. Our subtype identification is based not only on PD variables, but also on their complex patterns of progression, providing a useful tool for the analysis of large heterogenous, longitudinal data. Specifica…
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-scal…
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；University of Southern California（南加州大学）
Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict visiting orders in trajectories and cannot take advantage of multiple types of mobility representations. This paper proposes a novel mobility clustering method for mobility behavior detection. First, the proposed method contains a permutation-equivalent operation to handle sub-traje…
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 …
Télécom ParisTech（巴黎电信学院）；Université Paris I Panthéon-Sorbonne（巴黎第一大学先贤祠-索邦大学）
Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present two approaches to clustering network-constrained trajectory data. The first approach discovers clusters of trajectories that traveled along the same parts of the road network. The second approach is segment-oriented and aims to group together road segm…
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 …
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.
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…
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）
In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to ide…
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）
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 …
Advancements in Intelligent Traffic Systems (ITS) have made huge amounts of traffic data available through automatic data collection. A big part of this data is stored as trajectories of moving vehicles and road users. Automatic analysis of this data with minimal human supervision would both lower the costs and eliminate subjectivity of the analysis. Trajectory clustering is an unsupervised task. In this paper, we perform a comprehensive comparison of similarity measures, clustering algorithms and evaluation measures using trajectory data from seven intersections. We also propose a method to a…
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）
Stable Trajectory Clustering: An Efficient Split and Merge Algorithm
Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS)（计算机科学与信息技术系，基础科学研究所（IASBS））；Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf（先进系统理解中心（CASUS），德累斯顿-罗斯托克研究中心）；Helmholtz Centre for Environmental Research – UFZ（环境研究赫尔姆霍兹中心 – UFZ）；University of Maryland（马里兰大学）
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…
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories. The modified technique, termed feature trajectory dynamic time warping (FTDTW), is applied as a similarity measure in the agglomerative hierarchical clustering of speech segments. Experiments using MFCC and PLP parametrisations extracted from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and statistically significant improvements in the q…
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…
Télécom ParisTech（巴黎高等电信学院）；Université Paris I Panthéon-Sorbonne（巴黎第一大学先贤祠-索邦大学）
Clustering trajectory data attracted considerable attention in the last few years. Most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of road segments that are often travelled by the same trajectories. To achieve this end, we model the interactions between segments w.r.t. their similar…
Télécom ParisTech（巴黎电信学院）；Orange Labs（Orange实验室）；Université Paris I Panthéon-Sorbonne（巴黎第一大学 先贤祠-索邦）
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful…
The development of autonomous vehicles requires having access to a large amount of data in the concerning driving scenarios. However, manual annotation of such driving scenarios is costly and subject to the errors in the rule-based trajectory labeling systems. To address this issue, we propose an effective non-parametric trajectory clustering framework consisting of five stages: (1) aligning trajectories and quantifying their pairwise temporal dissimilarities, (2) embedding the trajectory-based dissimilarities into a vector space, (3) extracting transitive relations, (4) embedding the transiti…
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 …
Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
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）
Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. There is a pressing need for scalable and efficient techniques for analyzing this data and discovering the underlying patterns. In this paper, we introduce a novel technique which we call vector-field $k$-means. The central idea of our approach is to use vector fields to induce a similarity notion between trajectories. Other clustering algorithms seek a representative trajectory that best describes each cluster, much like $k$-means identifies a representat…
Co-clustering is a specific type of clustering that addresses the problem of finding groups of objects without necessarily considering all attributes. This technique has shown to have more consistent results in high-dimensional sparse data than traditional clustering. In trajectory co-clustering, the methods found in the literature have two main limitations: first, the space and time dimensions have to be constrained by user-defined thresholds; second, elements (trajectory points) are clustered ignoring the trajectory sequence, assuming that the points are independent among them. To address th…
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）
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…
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Institute for Interdisciplinary Information Sciences（交叉信息科学研究所）；Tsinghua University（清华大学）；Paul G. Allen School of Computer Science & Engineering（保罗·G·艾伦计算机科学与工程学院）；University of Washington（华盛顿大学）
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）
MobilityGPT: Enhanced Human Mobility Modeling with a GPT model
Department of Electrical and Computer Engineering, University of California, Davis（电子与计算机工程系，加州大学戴维斯分校）；Apple Inc.（苹果公司）；Department of Civil and Environmental Engineering, University of California, Davis（土木与环境工程系，加州大学戴维斯分校）
