A Feature-Based Approach to Large-Scale Freeway Congestion Detection Using Full Cellular Activity Data

Shen Li, Yang Cheng, Peter Jin, Fan Ding, Qing Li, Bin Ran

Research output: Contribution to journalArticlepeer-review

Abstract

Most existing cellular probe-based freeway congestion detection methods rely on on-call WLT (Wireless Location Technologies) signal transition data. However, these techniques facing difficulties such as small sample size, frequent road tests, safety, and privacy issues. This article presents a novel approach using the FCA data for traffic congestion detection on freeways. Two cellular activity features, the link pseudo speed and link probe activity, are defined and calculated. A rule-based algorithm is then developed to determine the traffic congestion state. The proposed method has been implemented and a prototype system has been deployed for a major freeway corridor in China. Validated by fixed-point detector data and incident records, the proposed method is able to identify real-time freeway traffic congestion accurately.

Original languageEnglish (US)
Pages (from-to)1323-1331
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number2
DOIs
StatePublished - Feb 1 2022

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Cellular probe
  • freeway congestion detection
  • full cellular activity data

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