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A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking.

Wenfeng Li1, Wenwen Zhang1, Cong Gao1

  • 1School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.

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|April 23, 2022
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Summary
This summary is machine-generated.

A new historical-trajectories-based map matching algorithm (HTMM) improves container positioning and tracking. HTMM enhances accuracy for low-sampling-rate GPS data, outperforming existing methods.

Keywords:
container positioning and trackinghistorical trajectoriesmap matchingpath reconstruction

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Area of Science:

  • Transportation Logistics
  • Geographic Information Systems
  • Data Science

Background:

  • Accurate container positioning and tracking are crucial for modern transportation logistics.
  • Existing map matching algorithms struggle with low sampling rates and missing GPS data common in container trajectories.
  • Shortest path-based algorithms are insufficient for the unique challenges of container tracking.

Purpose of the Study:

  • To develop an improved map matching algorithm for container positioning and tracking.
  • To address the limitations of current algorithms when dealing with sparse and incomplete trajectory data.
  • To enhance the efficiency and accuracy of container location determination.

Main Methods:

  • A novel historical-trajectories-based map matching algorithm (HTMM) was proposed.
  • HTMM utilizes travel time and frequency from historical trajectories to identify local paths.
  • Key components include a path reconstruction method for travel time calculation and a historical path index library for efficient data retrieval.

Main Results:

  • HTMM demonstrated significant performance improvements over ST-Matching and HMM-based algorithms.
  • Over 60-300s sampling intervals, HTMM showed >3% and >5% improvement, respectively.
  • Positioning error was halved at a 60s sampling interval, validating HTMM's effectiveness.

Conclusions:

  • The proposed HTMM algorithm effectively addresses the challenges of container positioning and tracking with low-sampling-rate GPS data.
  • HTMM offers a more accurate and reliable solution compared to existing map matching techniques.
  • The algorithm's ability to leverage historical trajectory data enhances location query and tracking capabilities.