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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data.

Hongsheng Qi1, Meiqi Liu1, Dianhai Wang1

  • 1College of Civil Engineering and Architecture, Zhejiang University, 866, Yuhangtang Road, Hangzhou, 310058, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for analyzing traffic flow data, even with missing information. It identifies dynamic network bottlenecks by measuring traffic flow similarity and clustering congestion patterns.

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Area of Science:

  • Transportation Science
  • Network Analysis
  • Data Science

Background:

  • Traffic congestion is a dynamic phenomenon varying in space and time.
  • Existing methods for traffic flow analysis often struggle with missing data, leading to inaccuracies.
  • Understanding congestion dynamics is crucial for network performance and bottleneck identification.

Purpose of the Study:

  • To develop a new traffic flow operational index time series similarity measurement.
  • To establish a method for identifying dynamic network bottlenecks, even with missing data.
  • To improve the accuracy of traffic congestion analysis.

Main Methods:

  • A two-stage similarity measurement process (first and second order) is proposed to handle missing data.
  • Traffic flow operational similarity is calculated between neighboring road links.
  • Similarity results are used to cluster spatial-temporal congestion patterns.

Main Results:

  • The developed method effectively handles missing traffic flow data.
  • The approach successfully identifies dynamic network bottlenecks.
  • Results align with empirical observations and offer new insights into traffic flow.

Conclusions:

  • The proposed similarity measurement is a robust approach for analyzing traffic flow data with missing values.
  • This method provides a reliable basis for identifying dynamic network bottlenecks.
  • The findings contribute to a better understanding of traffic congestion dynamics.