scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

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

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

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.

scholarly journals Exploring the Roles of Local Mobility Patterns, Socioeconomic Conditions, and Lockdown Policies in Shaping the Patterns of COVID-19 Spread

2021 ◽  
Vol 13 (5) ◽  
pp. 112
Author(s):  
Mauricio Herrera ◽  
Alex Godoy-Faúndez
Keyword(s):  
Latin American ◽  
Low Income ◽  
Large Scale ◽  
Human Mobility ◽  
Epidemiological Data ◽  
High Income ◽  
Metropolitan Region ◽  
Mobility Patterns ◽  
Large Cities

The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. This pandemic has also highlighted the critical importance of generating rigorous evidence for decision-making, and actionable insights from data, considering further the intricate web of causes and drivers behind observed patterns of contagion diffusion. Using mobility, socioeconomic, and epidemiological data recorded throughout the pandemic development in the Santiago Metropolitan Region, we seek to understand the observed patterns of contagion. We characterize human mobility patterns during the pandemic through different mobility indices and correlate such patterns with the observed contagion diffusion, providing data-driven models for insights, analysis, and inferences. Through these models, we examine some effects of the late application of mobility restrictions in high-income urban regions that were affected by high contagion rates at the beginning of the pandemic. Using augmented synthesis control methods, we study the consequences of the early lifting of mobility restrictions in low-income sectors connected by public transport to high-risk and high-income communes. The Santiago Metropolitan Region is one of the largest Latin American metropolises with features that are common to large cities. Therefore, it can be used as a relevant case study to unravel complex patterns of the spread of COVID-19.

scholarly journals Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 235
Author(s):  
Caili Zhang ◽  
Yali Li ◽  
Longgang Xiang ◽  
Fengwei Jiao ◽  
Chenhao Wu ◽  
...  
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Low Frequency ◽  
Trajectory Data ◽  
Vehicle Trajectories ◽  
Intersection Detection ◽  
Density Peaks ◽  
Road Network Extraction ◽  
Representative Points

With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.

scholarly journals Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

2020 ◽  
Vol 9 (2) ◽  
pp. 125 ◽  
Author(s):  
Zeinab Ebrahimpour ◽  
Wanggen Wan ◽  
José Luis Velázquez García ◽  
Ofelia Cervantes ◽  
Li Hou
Keyword(s):  
Social Media ◽  
Large Scale ◽  
City Planning ◽  
Smart Cities ◽  
Human Mobility ◽  
Location Based Services ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Planning Decisions ◽  
Media Data

Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

A Family With Autosomal-Dominant Progressive Sensorineural Hearing Loss

1996 ◽  
Vol 5 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Chris Halpin ◽  
Barbara Herrmann ◽  
Margaret Whearty
Keyword(s):  
Speech Production ◽  
Hearing Aids ◽  
Role Models ◽  
Speech Intelligibility ◽  
Large Scale ◽  
Speech Language Pathology ◽  
The Family ◽  
Patient Will ◽  
Language Pathology

The family described in this article provides an unusual opportunity to relate findings from genetic, histological, electrophysiological, psychophysical, and rehabilitative investigation. Although the total number evaluated is large (49), the known, living affected population is smaller (14), and these are spread from age 20 to age 59. As a result, the findings described above are those of a large-scale case study. Clearly, more data will be available through longitudinal study of the individuals documented in the course of this investigation but, given the slow nature of the progression in this disease, such studies will be undertaken after an interval of several years. The general picture presented to the audiologist who must rehabilitate these cases is that of a progressive cochlear degeneration that affects only thresholds at first, and then rapidly diminishes speech intelligibility. The expected result is that, after normal language development, the patient may accept hearing aids well, encouraged by the support of the family. Performance and satisfaction with the hearing aids is good, until the onset of the speech intelligibility loss, at which time the patient will encounter serious difficulties and may reject hearing aids as unhelpful. As the histological and electrophysiological results indicate, however, the eighth nerve remains viable, especially in the younger affected members, and success with cochlear implantation may be expected. Audiologic counseling efforts are aided by the presence of role models and support from the other affected members of the family. Speech-language pathology services were not considered important by the members of this family since their speech production developed normally and has remained very good. Self-correction of speech was supported by hearing aids and cochlear implants (Case 5’s speech production was documented in Perkell, Lane, Svirsky, & Webster, 1992). These patients received genetic counseling and, due to the high penetrance of the disease, exhibited serious concerns regarding future generations and the hope of a cure.

The Boeing 787 Dreamliner--A case study in large-scale design integration

2008 ◽  
Author(s):  
D. L. McMullin ◽  
A. R. Jacobsen ◽  
D. C. Carvan ◽  
R. J. Gardner ◽  
J. A. Goegan ◽  
...  
Keyword(s):  
Large Scale ◽  
Design Integration ◽  
Scale Design
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