Analysis of the Information Entropy on Traffic Flows

Zhiyuan Liu, Yunshan Wang, Qixiu Cheng, Hai Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper aims to reveal the uncertainty of traffic flow by introducing a new quantity based on the concept of information entropy (IE). We discover the existence and analyze the properties of IE of traffic flows. It is revealed by both real-world trajectory data and simulation data that the IE of traffic flows can be clearly measured and observed. More importantly, the relationships between IE and other key quantities, those are space mean speed and density, in traffic flow analysis can be described by linear and parabolic functions. We also discover that these relationships are not sensitive to traffic volume. With the inspiration from IE, another new quantity termed speed entropy (SE) is then proposed. Tests with aggregated traffic data from Performance Measurement System (PeMS) show that the pattern of the relationship between SE and flow-weighted average speed illustrates different traffic conditions. In general, a key achievement of the IE analysis is that it gives us a new pathway to better capture the intricate traffic flows from the dimension of uncertainty, thus it has the potential to enhance existing models for traffic data analysis.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusAccepted/In press - 2022


  • Analytical models
  • Data models
  • Entropy
  • Indexes
  • Information entropy
  • quantitative analysis
  • speed entropy.
  • Standards
  • traffic flow data
  • Uncertainty
  • uncertainty

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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