Data-Driven Location Annotation for Fleet Mobility Modeling

Online Social Networks and Media, 2017

The knowledge of the urban mobility is a crucial aspect for city planners and administrators. The huge amount of geo-spatial data, generated by the combination of social media systems and the wide use of smart devices, is creating new challenges and opportunities to satisfy this thirst of knowledge. In this work, we explore how social media data can be used to infer knowledge about urban dynamics and mobility patterns in a urban area. Specifically, in order to highlight the main advantages, limitations, and open issues, we focus on mobility patterns by presenting a survey of the state of the art and a case-study based on the city of Barcelona.

The Vldb Journal

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people's travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the

Transportation Research Procedia

Retrieving exhaustive information about individual mobility patterns is an essential step in order to implement effective mobility solutions. Despite their popularity, digital travel surveys still require a significant amount of inputs from the respondent. Consequently, they require great efforts from both respondents and analysts, and are limited to a relatively short period of timebetween a few weeks and a year. Driven by these motivations, the approach proposed in this paper uses mobile phone location history to automatically detect activity location without any interaction with the respondent. The proposed methodology uses raw location data together with a special indexing technique to calculate the probability of performing a certain activity in a certain location. It uses a heuristic rule to improve this estimation by considering the value of information over time. Finally, GIS data about the number of facilities located in a certain area is downloaded in real-time to further improve the overall estimation. Results of this exploratory study support the idea that the proposed approach can reconstruct complex mobility patterns while minimizing the number of active inputs from the respondent.

Annals of the American Association of Geographers, 2021

People's daily activities in the urban environment are complex and vary by individuals. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known as mobility motifs, in people's daily lives. However, the limitation in using only a few inferred activity types hinders our ability to examine general patterns in detail. We proposed a mobility network approach with geographic context-aware Twitter data to investigate granular daily activity patterns in the urban environment. We first utilized publicly accessible geo-located tweets to track the movements of individuals in two major U.S. cities: Chicago and Greater Boston, where each recorded location is associated with its closest land use parcel to enrich its geographic context. A direct mobility network represents the daily location history of the selected active users, where the nodes are physical places with semantically labeled activity types, and the edges represent the transitions. Analyzing the isomorphic structure of the mobility networks uncovered 16 types of location-based motifs, which describe over 83% of the networks in both cities and are comparable to those from previous studies. With detailed and semantically labeled transitions between every two activities, we further dissected the general location-based motifs into activity-based motifs, where 16 common activity-based motifs describe more than 57% transitional behaviors in the daily activities in the two cities. The integration of geographic context from the synthesis of geo-located Twitter data with land use parcels enables us to reveal unique activity motifs that form the fundamental elements embedded in complex urban activities.

IEEE Communications Magazine

Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.

ArXiv, 2020

Several institutes produce large semantic data sets about daily activities and human mobility. The analysis and the understanding of these data are crucial for urban planning, socio-psychology and political sciences or epidemiology. Currently, none of usual data mining process is customized for a complete analysis of semantic mobility sequences from data to understandable behaviors. Based on an extended review of the literature, we define in this article a new methodological pipeline, SIMBA (Semantic Indicators for Mobility and Behavior Analysis), for mine and analyze semantic mobility sequences in order to discover coherent information and human behaviors. A framework for semantic sequence mobility analysis and clustering explicability integrating different complementary statistical indicators and visual tools is proposed. To validate this methodology, we use a large set of real daily mobility sequences obtained from one household-travel survey. Complementary knowledge are automati...

Journal on Data Semantics, 2018

Affordances from the urban space shape the way we interact with our environment, whether manifested as driving into the city centre for work or playing sports in designated arenas. Given today's abundance of crowd-generated digital traces on location-based social network (LBSN) platforms, an opportunity arises to grasp deeper semantic characterization of urban affordances beyond static representations found in traditional GIS systems. Complementing this perception of the city, travel surveys capture mobility dynamics of people with absolute trajectory recordings and explicit travel purposes. By marrying rich LBSN data with travel surveys, we ask if crowdsourced urban characteristics can be used to explain user behaviour when interacting with the city. Concretely, our objective is to model and infer the purpose of travel, or the activity at the destination of a trip, in daily life scenarios. To this end, we generate features to correspond to time, location, and demographics in or...

2011 IEEE Conference on Visual Analytics Science and Technology (VAST), 2011

We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two realworld problems requiring analysis at different spatial scales.

Knowledge and Information Systems, 2015

In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are able to infer major movement behavior in a city, which could bring some valuable knowledge for urban planning. Specifically, mobility evolution patterns consist of segments with the spatial region distribution and the corresponding time interval. To measure good segmentation from a set of check-in data, we formulate the problem of mining evolution patterns as a compression problem. In particular, we compute the representation length of the patterns based on the Minimum Description Length principle. Since the number of daily mobility evolution patterns is huge, we further cluster the daily mobility evolution patterns into groups and discover representative patterns. Note that we use the concept of locality-sensitive hashing to accelerate the cluster performance. To evaluate our proposed algorithms, we conducted experiments on the Gowalla and Brightkite datasets, and the experimental results show the effectiveness and efficiency of our proposed algorithms.