Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates

  • Zhenyu Mei
  • Dianhai Wang Zhejiang University, School of Traffic and Transportation Engineering Zijingang Campus, Hangzhou, 310058, China
  • Jun Chen Southeast University, School of Traffic and Transportation Engineering Sipailou Campus, Nanjing, 210096, China
  • Wei Wang Southeast University, School of Traffic and Transportation Engineering Sipailou Campus, Nanjing, 210096, China
Keywords: bluetooth sensor, bicycle travel time, low sampling rates, filtering algorithm,

Abstract

Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor. The current paper describes an adaptive filtering algorithm for estimating bicycle travel times using Bluetooth data, with consideration of low sampling rates. The data for bicycle travel time using Bluetooth sensors has two characteristics. First, the bicycle flow contains stable and unstable conditions. Second, the collected data have low sampling rates (less than 1%). To avoid erroneous inference, filters are introduced to “purify” multiple time series. The valid data are identified within a dynamically varying validity window with the use of a robust data-filtering procedure. The size of the validity window varies based on the number of preceding sampling intervals without a Bluetooth record. Applications of the proposed algorithm to the dataset from Genshan East Road and Moganshan Road in Hangzhou demonstrate its ability to track typical variations in bicycle travel time efficiently, while suppressing high frequency noise signals.

Author Biography

Zhenyu Mei
Assistant Professor, Department of Civil Engineering , Zhejiang Univ.

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Published
2014-10-31
How to Cite
1.
Mei Z, Wang D, Chen J, Wang W. Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates. PROMET [Internet]. 2014Oct.31 [cited 2019Aug.22];26(5):383-91. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1343
Section
Articles