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https://doi.org/10.7307/ptt.v26i5.1343
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
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.
Keywords
bluetooth sensor; bicycle travel time; low sampling rates; filtering algorithm
Hrčak ID:
130137
URI
Publication date:
30.10.2014.
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