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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Selected Data About Geographic Locations01:25

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Field Application of Global Positioning System01:28

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Types of Global Positioning System Surveys01:30

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Errors in Global Positioning System01:26

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Distance Measurements by Taping01:18

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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Related Experiment Video

Updated: Oct 23, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

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A data-driven travel mode share estimation framework based on mobile device location data.

Mofeng Yang1, Yixuan Pan1, Aref Darzi1

  • 1Department of Civil and Environmental Engineering, Maryland Transportation Institute (MTI), University of Maryland, 1173 Glenn Martin Hall, College Park, MD 20742 USA.

Transportation
|August 17, 2021
PubMed
Summary
This summary is machine-generated.

Mobile device location data (MDLD) can estimate travel mode share. A new framework accurately imputes travel modes from MDLD, supporting transportation planning and mobility trend analysis.

Keywords:
Machine learningMobile device location dataTravel mode shareTravel surveys

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Area of Science:

  • Transportation Science
  • Data Science
  • Urban Planning

Background:

  • Mobile device location data (MDLD) offers extensive spatiotemporal coverage for travel behavior analysis, surpassing traditional surveys.
  • MDLD lacks inherent ground truth for trip origins, destinations, modes, and purposes, necessitating data imputation for comprehensive analysis.

Purpose of the Study:

  • To evaluate the capability of MDLD in estimating aggregated travel mode share.
  • To develop and validate a data-driven framework for extracting travel behavior information from MDLD.

Main Methods:

  • A modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm to identify trip ends.
  • Feature extraction from trips to impute travel modes using machine learning models, specifically a Random Forest classifier.
  • Validation using a labeled MDLD dataset with ground truth information.

Main Results:

  • Achieved a 95% recall rate in identifying trip ends.
  • Attained over 93% tenfold cross-validation accuracy in imputing five travel modes (drive, rail, bus, bike, walk).
  • Demonstrated successful application to large-scale MDLD datasets for the Baltimore-Washington metropolitan area and the United States.

Conclusions:

  • The proposed framework effectively extracts travel behavior from MDLD for mode share estimation.
  • The framework is adaptable to different regions and scales, offering a low-cost solution for multimodal travel demand analysis.
  • Findings support informed decision-making, mobility trend understanding, and transportation planning.