Project information

  • Category: Data Science
  • Data: NDW loop detector data and incident data
  • Project: Work of PhD student - Mahsa
  • Methods: Data fusion, Statistics, Visualisation
  • Technologies: Python, pandas, geopandas, networkx, kepler, folium, scipy, seaborn
  • Conference/Journal: IEEE ITSC Conference 2023
  • URL: Proceedings needs to be published

Project details

This study enhances road incident management by cross-validating and enriching incident data with traffic congestion information for the Dutch highway network. We align recorded incidents with corresponding traffic patterns, labeling them as 'congestion' or 'no-congestion' based on the observed traffic flow. For 'congestion' incidents, we further detail the congestion's duration, location, and Vehicle Loss Hours (VLH). Applied over five months of data from the Netherlands' major motorways, our approach improves the accuracy of incident detection algorithms and supports informed decision-making in traffic management and policy planning.