Error assessment for emerging traffic data collection devices.

Author(s)
Wang, Y. Araghi, B.N. Malinovskiy, Y. Corey, J. & Cheng, T.
Year
Abstract

Because access to travel time information can significantly influence the decision making of both agencies and travellers, accurate and reliable travel time information is increasingly needed. One important step in providing that information is to identify the sensors best suited to provide travel time data for a given corridor. Currently, few studies have evaluated the effectiveness of various travel time data collection technologies side-by-side. This evaluation was intended to provide decision support for transportation agencies looking to select travel time systems on the basis of accuracy, reliability, and cost. This study focused on two test corridors: State Route 522 (SR 522) (an urban arterial with frequent intersections) between the NE 153rd Street and 83rd Place NE intersections, and I-90 (rural freeway built over Snoqualmie Pass in the Cascade mountains) from milepost 109 (Ellensburg, Wash.) to milepost 32 (North Bend, Wash). The sensor systems tested were Washington State Department of Transportation’s pre-existing automatic license plate reader (ALPR) system, Sensys emplacements, the TrafficCast BlueTOAD system, Blip Systems BlipTrack sensors, and a third-party feed from Inrix. This study’s approach was to look at the Mean Absolute Deviation (MAD) to judge the expected magnitude of error, then examine the Mean Percent Error (MPE) to find any systematic biases in the data. The Mean Absolute Percent Error (MAPE) was useful for finding the relative magnitude of the error, and the Root Mean Square Error (RMSE) was used to determine whether a few large errors or many smaller errors were occurring. Each system in the analysis demonstrated different strengths and weaknesses that should be considered in addition to its accuracy and sample rates. Some systems can provide additional data; others trade accuracy and coverage for cost or portability. Ultimately, engineers will need to weigh their requirements for accuracy and sample rates against the other engineering constraints imposed on their system. (Author/publisher)

Publication

Library number
20140837 ST [electronic version only]
Source

Olympia, WA, Washington State Department of Transportation DOT, Research Office, 2014, XVI + 106 p., 36 ref.; WSDOT Research Report ; WA-RD 810.1

Publication (link)

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