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Related Experiment Video

Updated: Sep 4, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Traffic violation analysis using time series, clustering and panel zero-truncated one-inflated mixed model.

Zahra Rezaei Ghahroodi1, Samaneh Eftekhari Mahabadi1, Sara Bourbour2

  • 1School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.

International Journal of Injury Control and Safety Promotion
|July 20, 2022
PubMed
Summary
This summary is machine-generated.

This study analyzed traffic violations in Tehran from 2016-2021 using a large dataset. Findings reveal patterns in traffic rule violations, crucial for urban traffic safety improvements.

Keywords:
Traffic violationk-means clusteringpanel count dataspatial analysistime series analysiszero-truncated one-inflated poisson mixed model

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Area of Science:

  • Urban planning
  • Transportation engineering
  • Data science

Background:

  • Traffic rule violations are a significant cause of crashes and safety issues in urban environments.
  • Understanding violation patterns is essential for effective traffic management.

Purpose of the Study:

  • To analyze the frequency and patterns of traffic violations in Tehran, Iran, over a five-year period (March 2016 - March 2021).
  • To identify spatial and temporal trends in traffic violations.
  • To explore factors influencing the number of driver violations.

Main Methods:

  • Utilized a comprehensive road traffic violation monitoring system database (approx. 97 million violations, 16 million drivers).
  • Applied three statistical approaches: multiplicative SARIMA and Bayesian Spatio-temporal models, K-means clustering, and a random-effect zero-truncated one-inflated Poisson model.

Main Results:

  • Identified specific temporal trends in monthly traffic violations using time-series and spatio-temporal models.
  • Clustered Tehran districts based on violation frequency and density, revealing spatial heterogeneity.
  • Determined key factors influencing the number of violations per driver over time.

Conclusions:

  • The study provides a data-driven analysis of traffic violations in a major urban center.
  • Results can inform targeted interventions to enhance traffic safety and reduce violations in Tehran.
  • The methodologies offer a framework for analyzing traffic violation data in other urban settings.