GPS Logs Research Articles

Understanding Places Exploration and Visitation via Human Mobility Mining

Spatial-temporal data is widely available because of advancement in location acquisition technologies (GPS, GSM, Wifi, etc.) over past decades. This spatial-temporal data can easily be used to leverage user's trajectories, history, and habits to develop location-based services (LBS). But to leverage user's history based on location is bit challenging, in this paper we used the user's GPS log data to discover trajectories and analyze different life patterns over it. We proposed that a human spends 80% of his life on known places or safe places. We converted GPS patterns into visitation locations, converted them into daily and periodic trends and analyzed them over time to prove our assumption. We used the GPS data collected by Microsoft Research Asia, this data has 182 users with 73 users with their mode of movement information i.e., Taxi, Walk, Train etc.

Tracer Observation of Queuing Behavior During New Year’s Visits to Shrines/Temples Using GPS Log Data

Crowd behavior during customary New Year's visits to shrines/temples in Japan was investigated. During these mass gatherings, visitors form a queue along the approach to the Main Hall of the shrines/temples. The unidirectional flow of the queue as the visitors approach the Main Hall to perform rituals is managed by police. The author observed the queuing behavior of visitors by joining the procession and tracked his position over time using a GPS logger. The author also observed the surrounding geometrical features and the procedures for guiding the crowd. Control methods, such as cordons and preparation of extra queuing space, were examined based on the observational data and compared with those of building/fire evacuations. The “everyone is welcome” aspect of New Year's visits required not only cordons in front of bottlenecks such as gates, stairs, and praying areas, but also alteration of the queuing space to accommodate for expansion of the crowd size. In contrast, considering building/fire evacuations, occupancy can be estimated or controlled by ticket quantity or density. Therefore, the accumulation of evacuees at bottlenecks can be predicted and should be considered in the building design phase. Different events or facilities require distinct crowd management approaches.

Evaluating and Validating Stay Point Detection Algorithms with different GPS Log Intervals

This study explores the variability in stay point detection accuracy influenced by GPS log intervals and detection algorithms and confirms the impact of stay point detection on stay duration and trip frequency estimations. We compare three major detection algorithms across varied log intervals adjusted through down-sampling using five evaluation indices newly proposed in this study. Using GPS trajectory data with ground truth data collected in Hiroshima, Japan, we found that ST-DBSCAN, a time-distance density clustering method, offers the highest accuracy and maintains its performance up to a 5-minute interval. We also found that widely used conventional methods, including duration and distance-based methods and DBSCAN, would produce considerably biased results.

Leveraging electronic device outputs for service quality assessment of public transportation systems: An initial look at the Philippines’ service contracting program through data analytic lens

Electronic devices such as CCTVs and GPS trackers are becoming more prevalent in public utility vehicles. These electronic devices produce data that can be aggregated and processed to generate insights regarding the service quality provided by public transportation systems. We describe a methodology for processing CCTV-produced videos and GPS logs, and utilize it to perform a first order, limited assessment of the Philippines’ Service Contracting Program, which ran during the COVID-19 pandemic. Our analyses uncovered vast discrepancies between data-supported figures and manually-generated estimates provided by the transport operators, especially with regards to vehicle occupancy and total number of commuters served. Our study also uncovered imperfections in the data generated by the electronic devices, which place caveats or limitations on the insights generated by the data analyses. We provide and suggest ways on how the analyses can be modified to take into account such missing data, thus enabling us to come up with educated, reasonable insights despite incomplete input datasets.

Identifying the changes in people’s daily activities before and after the COVID-19 pandemic based on activity spaces measured using GPS log data

Identifying the changes in people’s daily activities before and after the COVID-19 pandemic based on activity spaces measured using GPS log data

Study on the Possibility of Replacing and Complementing Existing Traffic Surveys Using Commercial GPS Logs in Developing Countries

Study on the Possibility of Replacing and Complementing Existing Traffic Surveys Using Commercial GPS Logs in Developing Countries

EMD Based Semantic User Similarity using Past Travel Histories

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

Detection of Traffic Pattern Based on Fuzzy Clustering and Wavelet Analysis Model at Different Signaling Positioning Frequencies

Signaling positioning technology provides a new opportunity to understand an individual’s travel characteristics. In recent studies, the travel parameters obtained are mainly macroscopic travel information. However, extracting detailed trip chain information, such as the trip mode and mode-switching time point, remains a challenge. Furthermore, because of the iterative development of wireless networks, existing communication operators usually store different frequencies and accuracy (2G/3G and 4G) of signaling data simultaneously, making the refined identification of travel information more difficult. Therefore, this paper proposes a new method. First, we use the shortest distance algorithm to match the signaling data with the road network. Second, a wavelet transform modulus maximum (WTMM) algorithm is proposed to divide multimodal travel trajectories into single-mode trip segments; thus, spatiotemporal information related to mode transfer can be obtained. Finally, an unsupervised fuzzy kernel c-means clustering (FKCM) algorithm is proposed to distinguish travel modes. As comparison data, smartphone GPS and travel log data are also collected to analyze the detection result and improve the method. The identification errors of mode-switching time points at different frequencies are all less than 360 s. The average correct rate of traffic mode identification for 2G is 65.1%, and the average correct rate of traffic mode identification for 3G is 78.2%. 4G intensive cellular positioning data has a significantly better recognition effect than low-frequency data; the average trip mode detection accuracy reaches 89.6%, and the mode-switching time point detection errors are within 300 s.

Contact duration: Intricacies of human mobility

Human mobility shapes our daily lives, our urban environment and even the trajectory of a global pandemic. While various aspects of human mobility and inter-personal contact duration have already been studied separately, little is known about how these two key aspects of our daily lives are fundamentally connected. Better understanding of such interconnected human behaviors is crucial for studying infectious diseases, as well as opportunistic content forwarding. To address these deficiencies, we conducted a study on a mobile social network of human mobility and contact duration, using data from 71 persons based on GPS and Bluetooth logs for 2 months in 2018. We augment these data with location APIs, enabling a finer granular characterization of the users’ mobility in addition to contact patterns. We model stops durations to reveal how time-unbounded-stops (e.g., bars or restaurants) follow a log-normal distribution while time-bounded-stops (e.g., offices, hotels) follow a power-law distribution. Furthermore, our analysis reveals contact duration adheres to a log-normal distribution, which we use to model the duration of contacts as a function of the duration of stays. We further extend our understanding of contact duration during trips by modeling these times as a Weibull distribution whose parameters are a function of trip length. These results could better inform models for information or epidemic spreading, helping guide the future design of network protocols as well as policy decisions.

Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual’s location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual’s PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

EMD-Based Semantic User Similarity Using Past Travel Histories

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

From Raw Pedestrian Trajectories to Semantic Graph Structured Model—Towards an end-to-end spatiotemporal analytics framework

Abstract The conditions and context in which pedestrians are exposed to the risk of road accidents, air pollution or epidemy are important for improving safety. Spatial information on pedestrian mobility was difficult and expensive to collect so far. This is changing with the popularization of several apps either for guidance or self-monitoring. Produced Space-Time Trajectories (STT) perfectly match the concept of temporal geography, as to assess pedestrian exposure to the above risks. In this article, we propose a modeling approach of pedestrian STT. The abstract model provides a high semantic expressiveness through spatiotemporal queries and a high-level graph representation of the data. We depict a whole framework for collecting and analyzing GPS logs generated by pedestrians’ devices, and discuss the system components. Among the targeted applications, a new generation of mobile applications can be developed that allow pedestrians to organize their daily activities and mobility, and minimize their exposure risks.

Bursty visitation of locations in human mobility

Understanding individuals’ travel patterns has great impact on practical issues such as traffic control and urban planning. Here, we analyze a 4G dataset of 1000 randomly selected individuals in Shijiazhuang city, China during half month which contains the position information of these cell phone users in each second and GPS logs of 182 volunteers in a period of over five years. We find that the dynamics of locations’ visitations is characterized by bursts, namely the distributions of the relative variation of visitation traffic per day, week display a long tail. On that basis we propose the Exploration and Rank-shift Return model combined the classic Exploration and Preferential Return model with a rank-shift mechanism where every location may move up to a higher ranking position with probability. The model qualitatively recovers the statistical properties observed in the empirical data and reproduces the existence of bursty visitation of locations.

Role of Urban Big Data in Travel Behavior Research

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

Local-entity resolution for building location-based social networks by using stay points

The quality of a location-based social network (LBSN) is mainly related to the granularity of information on the users' location. When LBSN is built using stay points, it presents much more information since GPS logs convey more users' mobility information. However, the main challenge in building LBSN using stay points is to define local-vertices. This problem is known as local-entity resolution. This local-vertices could represent venues with semantic information like parks, restaurants, among others. The most common way to resolve local-entity is by applying clustering algorithms to group nearby stay points into local-vertices. However, in this case, only geographic information is used, which makes it very difficult to separate geographically close venues into distinct local-vertices. This paper addresses this gap and presents a novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points. The experimental evaluation carried out indicates that our approach has advantages compared to usual clustering methods to represent real-world features.

Will you go where you search? A deep learning framework for estimating user search-and-go behavior

Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such behavior can be of great importance for Internet companies to recommend point-of-interest (POI), advertisement, and route, as well as for governments and public service operators like metro companies to conduct traffic monitoring, crowd management, and transportation scheduling. Therefore, in this study, we first collect search log data and GPS log data with linked and consistent user ID from Yahoo! Japan portal application installed in millions of smart-phones and tablets. Then we propose a framework including a complete data-processing procedure and an end-to-end deep learning model to predict whether a user will check-in the searched place or not. Specifically, as users’ daily activities are considered to have high correlation with their travel, eating, and recreation decision in the future (i.e., go or not), Deep Spatial–Temporal Interaction Network (DeepSTIN) is elaborately designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data. Experimental results based on the standard metrics demonstrate that our proposed framework can achieve satisfactory performances on multiple real-world search scenarios.

Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and employs machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and power consumption. The performance evaluations yield that the proposed Mobi-IoST framework has approximately 93% accuracy and reduced the delay and power by approximately 23–26% and 37–41% respectively than the existing mobility-aware task delegation system.

Estimation of socioeconomic attributes from location information

Timely estimation of the distribution of socioeconomic attributes and their movement is crucial for academic as well as administrative and marketing purposes. In this study, assuming personal attributes affect human behavior and movement, we predict these attributes from location information. First, we predict the socioeconomic characteristics of individuals by supervised learning methods, i.e., logistic Lasso regression, Gaussian Naive Bayes, random forest, XGBoost, LightGBM, and support vector machine, using survey data we collected of personal attributes and frequency of visits to specific facilities, to test our conjecture. We find that gender, a crucial attribute, is as highly predictable from locations as from other sources such as social networking services, as done by existing studies. Second, we apply the model trained with the survey data to actual GPS log data to check the performance of our approach in a real-world setting. Though our approach does not perform as well as for the survey data, the results suggest that we can infer gender from a GPS log.

Residential neighborhood features associated with objectively measured walking near home: Revisiting walkability using the Automatic Context Measurement Tool (ACMT).

Many distinct characteristics of the social, natural, and built neighborhood environment have been included in walkability measures, and it is unclear which measures best describe the features of a place that support walking. We developed the Automatic Context Measurement Tool, which measures neighborhood environment characteristics from public data for any point location in the United States. We explored these characteristics in home neighborhood environments in relation to walking identified from integrated GPS, accelerometer, and travel log data from 681 residents of King Country, WA. Of 146 neighborhood characteristics, 92 (63%) were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery. The strongest built environment predictor of walking bout count was housing unit count. Models using data-driven and a priori defined walkability measures exhibited similar fit statistics. Walkability measures consisting of different neighborhood characteristic measurements may capture the same underlying variation in neighborhood conditions.

APT3: Automated Product Traceability Trees Generated from GPS Tracks

Abstract. With increasing concerns about food safety in many countries, product traceability has become an important risk-management tool. It enables the identification of possible sources of defective goods and facilitates the withdrawal and recall of affected products to protect consumers from foodborne diseases. However, it is troublesome for farmers to maintain the records required by high-precision product traceability during harvesting because traditional traceability systems usually involve human labor in paperwork or expenses for equipment purchase and installation; in either case, the resulting records are tedious. In this article, a fully automatic algorithm is proposed for efficiently generating product traceability trees to visualize and store the full transport record of wheat from fields to elevators. Extending previous work on harvesting activity recognition via GPS tracks, this algorithm powers our fully automatic prototype product traceability system, APT3, and demonstrates great potential for tracing products solely with the GPS logs of the vehicles involved in harvest and transport. From the output traceability trees, the product yielded at any point in the field can be tracked all the way to the elevator where it was sold or stored by starting from the corresponding leaf node in the traceability tree and walking to the root of the tree. Furthermore, each truckload of product unloaded at any destination elevator can be traced back to where the product was harvested by following the tree in the opposite direction. In this way, the traceability records can be clearly visualized for farmers and easily used by other algorithms. Keywords: GPS, Harvesting, Precision agriculture, Product traceability, Traceability trees.