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Big data for road and traffic network analysis

Primary supervisor

Jackie Rong


The road network is a crucial aspect of modern transportation, but traffic can significantly impact the driving experience. Accurate estimation of traffic conditions can help in travel planning, but it is essential to consider the status of the road itself. Poor road conditions can exacerbate the negative impacts of traffic on driving experience, making travel more difficult and potentially dangerous. Relevant surrounding factors, such as weather and time of day, can also influence traffic and road conditions. Real-time updates on road and traffic status can help drivers make informed decisions about their routes, avoiding congested areas and improving their overall driving experience. Ultimately, effective management of traffic and road conditions can have significant impacts on driver satisfaction and road safety.

Student cohort

Double Semester


The aim of this research project is to design and develop big data models to integrate relevant data from various sources for better road and traffic analysis. 

  • exploring real-world data relevant to road and traffic analysis.
  • applying appropriate data wrangling techniques on the collected data.
  • create road networks to demonstrate the changes of the traffic within a specific time-period.
  • develop machine learning models to perform road and traffic study.

Required knowledge

  • Students have knowledge about big data and machine learning.
  • Student familiar with Python, R, or MATLAB.
  • Experience with data wrangling, graph/network analysis is preferred.