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.
Highlights
Bicycles are the most popular travel means in China, which contribute to 30% to 70% of daily trips
Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor
Using the obtained coefficients described in the section of considering low sampling rates, the proposed algorithms can be applied to low levels of market penetration; 4
Summary
Bicycles are the most popular travel means in China, which contribute to 30% to 70% of daily trips. With the increased penetration of Bluetooth on the roadways in China, nearly 2% to 3% of bicycles in Hangzhou had active Bluetooth devices in 2010 These sensors became a viable alternative to other methods of bicycle travel time data collection [4,5,6]. Bluetooth is a short-range communications protocol developed by the Special Interests Group for interdevice communications This protocol creates some challenges for its additional uses, which could be optimized for travel time collection based on Bluetooth MAC (Media Access Control) address matching [7]. The proposed algorithms can handle both stable (constant mean) and unstable (varying mean) traffic conditions, and correct abrupt changes in the means of bicycle travel times and spurious spikes; 3. The proposed algorithms can be used for offline, and for online data filtering
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