Urban travel time data cleaning and analysis for Automatic Number Plate Recognition

Jie Li*, Henk Van Zuylen, Yuansheng Deng, Yun Zhou

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
21 Downloads (Pure)


Data recorded by Automated Number Plate Recognition (ANPR) cameras can be used to determine several important traffic characteristics, such as real time travel time, travel time statistics, travel time reliability and OD matrices. In this paper ANPR data collected in Chinese city Changsha have been validated. Travel time extracted from ANPR data includes some outliers which are often caused by drivers who have an intermediate stop between two observation points or deviate from the straight route. Exceptional travel time reduces the validity of the estimation of the travel time and reliability. Firstly, the Rapid-Moving Window method is introduced to identify outliers. Afterwards, another method based on wavelet analysis is put forward to identify and remove the outliers in the travel time series. The wavelet analysis method is compared with the Rapid-Moving Window method and shows to be more accurate in outlier identification. The method for eliminating outliers in travel times can be implemented in real time to enhance the data quality for traffic network monitoring and management. After the removal of the outliers, the resulting travel times are used for the analysis of the relation between average travel time and standard deviation/skewness.

Original languageEnglish
Pages (from-to)712-719
Number of pages8
JournalTransportation Research Procedia
Publication statusPublished - 2020
Event22nd EURO Working Group on Transportation Meeting, EWGT 2019 - Barcelona, Spain
Duration: 18 Sep 201920 Sep 2019


  • Automated Number Plate Recognition data
  • Data clearning
  • Travel time reliability
  • Wavelet analysis


Dive into the research topics of 'Urban travel time data cleaning and analysis for Automatic Number Plate Recognition'. Together they form a unique fingerprint.

Cite this