Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example

12/04/2019
by   Feilong Wang, et al.
0

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, the 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), and methods to extract trips from data generated via multiple positioning technologies (or, multi-sourced data) are absent. And yet, multi-sourced data are now 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.

READ FULL TEXT

Authors

page 5

page 8

page 10

page 17

page 23

page 24

page 25

08/24/2019

A statistical framework for measuring the temporal stability of human mobility patterns

Despite the growing popularity of human mobility studies that collect GP...
09/22/2020

Influences of Temporal Factors on GPS-based Human Mobility Lifestyle

Analysis of human mobility from GPS trajectories becomes crucial in many...
03/16/2020

TraLFM: Latent Factor Modeling of Traffic Trajectory Data

The widespread use of positioning devices (e.g., GPS) has given rise to ...
09/25/2019

Mining Human Mobility Data to Discover Locations and Habits

Many aspects of life are associated with places of human mobility patter...
02/08/2019

Exploring a New Model for Mobile Positioning Based on CDR Data of The Cellular Networks

The emerging technologies related to mobile data especially CDR data has...
06/19/2019

Estimating Commuting Patterns from High Resolution Phone GPS Data

The rise of location positioning technologies has generated enormous vol...
05/17/2019

Positioning aiding using LiDAR in GPS signal loss scenarios

In the presented scenario, an autonomous surface vehicle (ASV) equipped ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.