Human Mobility Analysis Research Articles

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective.We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood's position within the transportation network in future work.

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility.

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

Monitoring the COVID-19 epidemic with nationwide telecommunication data

In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 2020), consisting of ∼1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1%. The strongest reduction is linked to bans on gatherings of more than five people, which are estimated to have decreased mobility by 24.9%, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 1.11% reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.

Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

Public Curb Parking Demand Estimation With POI Distribution

With the increasing quantity of private cars, curb parking has evolved into an important approach to mitigate parking pressure in urban cities. While some efforts have been made for the demand analysis of point-of-interest (POI) and pattern analysis of human mobility, which may indirectly reflect the parking situation in urban area, there is a lack of comprehensive models for the parking demand, so as to make a prediction for the road sections without parking lots. In this paper, by focusing on curb parking and designing a systemic framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Curb Parking Demand Estimation</i> (CPDE), we model the public parking demand in urban area, w.r.t. parking durations and regional characteristics. Specifically, we use taxi destinations and the distribution of POIs to quantitatively analyze the regional characteristics, designing corresponding features, and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-means-based</i> Least Square (KLS) method to relate parking characteristics, namely, the temporal parking durations and the corresponding demands, with these features. In this way, we effectively avoid the geographical sparsity of road parking sections and can finely estimate parking durations and demands for newly developed districts without parking data. Moreover, we give a strategy, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parking Types Estimation</i> (PTE), which projects estimated parking durations and demands onto Gaussian Mixture Model (GMM) to accurately measure the distribution of demands over different parking durations for a road section. At last, we conduct experiments on a real-world curb parking dataset in Hefei, a provincial city in China. This dataset contains parking orders of 2016 over the urban area of Hefei. The experimental results validate the effectiveness of our methods, and show that our framework outperforms the state-of-the-art baseline schemes.

Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective

The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups, such as tourists, remain underexplored. In this study, we introduce an analytical framework to gain insights into tourist mobility patterns. By analyzing mobile phone trajectories of international travelers to three different cities in South Korea, we introduce nine mobility indicators to capture different facets of tourist travel behavior (e.g., duration of stay in a city, spatial extent of activities, location visited and trips conducted, and mobility diversity), and examine their statistical properties across cities. An eigendecomposition approach is then introduced to better understand the interdependency of these mobility indicators and inherent variations among individual travelers. Based on the eigendecomposition results, we further employ a dimension reduction technique to describe the key characteristics of each traveler. Since the mobile phone dataset captures the nationality of tourists, we use such information to quantify the behavioral heterogeneity of travelers across countries and regions. Finally, we select a few traveler groups with distinctive mobility patterns in each city and examine the spatial patterns of their activities. Substantial differences are observed among traveler groups in their spatial preferences. The implications for location recommendation and deployment of tourism services (e.g., transportation) are discussed. We hope the study brings a synergy between classic human mobility analysis and the emerging field of tourism big data. The framework can be applied or extended to compatible datasets to understand travel behavior of tourists, residents, and special population groups in cities.

Home location prediction with telecom data: Benchmarking heuristics with a predictive modelling approach.

Correctly identifying the home location is crucial for human mobility analysis with telecom data, more specifically call detail record (CDR) data. To that end, multiple heuristics have been developed in literature. Nevertheless, due to the lack of ground truth home location data, no study has thoroughly validated these widely used methods so far. We present a detailed performance analysis of existing home detection heuristics, using a unique dataset that enables this important validation on the lowest level, being the level of the cell tower. Our research indicates that simple heuristics surprisingly outperform their more complex counterparts. The benchmark study revealed that the best heuristic is able to identify the home location with an average error of approximately 4.5 km and selects the correct home tower in 60.69% of the cases. Based on the insights provided by our study, we propose a new heuristic that increases the accuracy to 61% and lowers the average distance error to 4.365 km. Secondly, if the home location is known for possibly only a fraction of the instances, we propose a labelled predictive modelling approach. Adding social network based variables in this predictive model further enhances the predictive performance. Our best model reduces the average distance error to 2.848 km and selects the correct home location in 72.08% of the cases. Furthermore, this result provides an indication of the upper bound for home detection with CDR data. Finally, models that only make use of social network based data are developed as well. Results show that even without using data of the focal individual, these models are able to select the correct home tower in 37.65% of the cases and achieve an average distance error of 8.1 km.

A regression framework for predicting user’s next location using Call Detail Records

With the growth of using cell phones and the increase in the 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 regression framework to predict next locations of users of cellular operators. We propose domain-specific data processing strategies and design a deep neural network model which is based on recurrent neurons and performs regression tasks. Using this framework on real-world data, we show that the error of the prediction decreases up to 74% in comparison to the traditional location prediction models. The results of this paper can be helpful in many applications from urban planning and digital marketing to predicting the spread of pandemics.

CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.

Trace2trace-A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories.

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

Coronavirus and Migration: Analysis of Human Mobility and the Spread of Covid-19

Reactions, measures as well as discourses dealing with the current pandemic vary significantly across the world. While some countries were completely locked down, as was the case in Italy, some had claimed to have very few or no cases, as was the case in Turkey and Indonesia by March 10th, 2020. Nevertheless, the spread of COVID-19 from China has been clearly linked to those travelling from Wuhan in Hubei province in Central China. Therefore, it is important to understand the travel density/volume of passengers carried as well as routes from Wuhan through connected main regional air travel hubs across China. In this study, we developed a model on migration and travel intensity that can explain outbreak and spread COVID-19 since it appeared at the end of 2019. We show that the presence of migrant stock populations of Chinese origin and the immigrant stock in China are useful indicators in the prediction of the spread of the outbreak worldwide in the event of interaction with several other macro factors. We argue that monitoring immigrant stock data and travel volume data based on human mobility corridors (i.e. origins and destinations), countries could have been better prepared and taken early measures to contain the spread of COVID-19.

Predictability and Prediction of Human Mobility Based on Application-Collected Location Data

In the modern information society, analysis of human mobility becomes increasingly essential in various areas such as city planning and resource management. With users' historical trajectories, the inherent patterns of their movements can be extracted and utilized to accurately predict the future movements. Plenty of previous work adopted traditional Markov model, which suffers when the trajectory becomes sparse or it shows distinct mobility patterns in different time of day. In this paper, based on an app-collected dataset of 100,000 individuals' actively uploaded location information, we comprehensively analyze the mobility and predictability of each user. To approach the theoretical predictability and overcome the shortcomings of traditional Markov model, we propose a time-variant Markov model based on Gibbs sampling for mobility prediction. Specifically, we model human mobility as several interconnected Markov chains, each chain corresponds to a movement pattern of a period of time. Then, we adopt Gibbs sampling method to simultaneously recover the missing part of trajectories and train the Markov chains, in order to solve the unevenly distribution and the high missing rate. Results show that our prediction algorithm can achieve 11.2 percent higher prediction accuracy than the benchmark method, especially on sparse trajectories. In addition, we discover a high correlation between prediction accuracy and predictability, with correlation coefficient reaching 0.81. Finally, we investigate various factors including spatial and temporal resolution, orders of Markov models, and radius of gyration, in order to further explore the predictability under different circumstances.

Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories

ABSTRACT In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world’s largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users’ route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health.

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

Customer mobility and congestion in supermarkets.

The analysis and characterization of human mobility using population-level mobility models is important for numerous applications, ranging from the estimation of commuter flows in cities to modeling trade flows between countries. However, almost all of these applications have focused on large spatial scales, which typically range between intracity scales and intercountry scales. In this paper, we investigate population-level human mobility models on a much smaller spatial scale by using them to estimate customer mobility flow between supermarket zones. We use anonymized, ordered customer-basket data to infer empirical mobility flow in supermarkets, and we apply variants of the gravity and intervening-opportunities models to fit this mobility flow and estimate the flow on unseen data. We find that a doubly-constrained gravity model and an extended radiation model (which is a type of intervening-opportunities model) can successfully estimate 65%-70% of the flow inside supermarkets. Using a gravity model as a case study, we then investigate how to reduce congestion in supermarkets using mobility models. We model each supermarket zone as a queue, and we use a gravity model to identify store layouts with low congestion, which we measure either by the maximum number of visits to a zone or by the total mean queue size. We then use a simulated-annealing algorithm to find store layouts with lower congestion than a supermarket's original layout. In these optimized store layouts, we find that popular zones are often in the perimeter of a store. Our research gives insight both into how customers move in supermarkets and into how retailers can arrange stores to reduce congestion. It also provides a case study of human mobility on small spatial scales.

The time varying network of urban space uses in Milan

In a metropolis, people movements design intricate patterns that change on very short temporal scales. Population mobility obviously is not random, but driven by the land uses of the city. Such an urban ecosystem can interestingly be explored by integrating the spatial analysis of land uses (through ecological indicators commonly used to characterize natural environments) with the temporal analysis of human mobility (reconstructed from anonymized mobile phone data). Considering the city of Milan (Italy) as a case study, here we aimed to identify the complex relations occurring between the land-use composition of its neighborhoods and the spatio-temporal patterns of occupation made by citizens. We generated two spatially explicit networks, one static and the other temporal, based on the analysis of land uses and mobile phone data, respectively. The comparison between the results of community detection performed on both networks revealed that neighborhoods that are similar in terms of land-use composition are not necessarily characterized by analogous temporal fluctuations of human activities. In particular, the historical concentric urban structure of Milan is still under play. Our big data driven approach to characterize urban diversity provides outcomes that could be important (i) to better understand how and when urban spaces are actually used, and (ii) to allow policy makers improving strategic development plans that account for the needs of metropolis-like permanently changing cities.

Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations

Understanding the relationship between urban locations is an essential task in urban planning and transportation management. Although prior works have focused on studying urban locations by aggregating location-based properties, our scheme preserves the mutual influence between urban locations and mobility behavior, and thereby enables situation-aware exploration of urban regions. By leveraging word embedding techniques, we encode urban locations with a vectorized representation while retaining situational awareness. Specifically, we design a spatial embedding algorithm that is precomputed by incorporating the interactions between urban locations and moving objects. To explore our proposed technique, we have designed and implemented a web-based visual exploration system that supports the comprehensive analysis of human mobility, location functionality, and traffic assessment by leveraging the proposed visual representation. The case studies demonstrate the effectiveness of our approach.

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are generated via a single positioning technology such as GPS or triangulation through cellular towers (thereby called single-sourced data). Methods to extract trips from data generated via multiple positioning technologies (called “multi-sourced data”) are absent. And yet, multi-sourced data are increasingly common. Generated using multiple technologies (e.g., GPS, cellular network- and WiFi-based), multi-sourced data contain high variances in their temporal and spatial properties. In this study, we propose a “Divide, Conquer and Integrate” (DCI) framework to extract trips from multi-sourced data. We evaluate the proposed framework by applying it to an app-based data, which is multi-sourced and has high variances in both location accuracy and observation interval (i.e. time interval between two consecutive observations). On a manually labeled sample of the app-based data, the framework outperforms the state-of-the-art SVM model that is designed for GPS data. The effectiveness of the framework is also illustrated by consistent mobility patterns obtained from the app-based data and an externally collected household travel survey data for the same region and the same period.

Spatiotemporal Data Clustering: A Survey of Methods

Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially interesting and useful information. Due to the complexity of ST data and the diversity of objectives, a number of ST analysis methods exist, including but not limited to clustering, prediction, and change detection. As one of the most important methods, clustering has been widely used in many applications. It is a process of grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. In this paper, some representative ST clustering methods are reviewed, most of which are extended from spatial clustering. These methods are broadly divided into hypothesis testing-based methods and partitional clustering methods that have been applied differently in previous research. Research trends and the challenges of ST clustering are also discussed.