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Research Articles

Challenges faced in heterogeneous traffic data collection: a comparison of traffic data collection technologies

Authors:

DND Jayaratne ,

University of Moratuwa, LK
About DND
Lecturer, Department of Civil Engineering, Faculty of Engineering
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CJ Vidanapathirana,

University of Moratuwa, LK
About CJ
Research Assistant, Department of Civil Engineering, Faculty of Engineering
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HR Pasindu

University of Moratuwa, LK
About HR
Senior Lecturer, Department of Civil Engineering, Faculty of Engineering
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Abstract

Traffic data are the fundamental inputs to traffic flow analysis and simulation studies, which facilitate decision making in the fi eld of traffic engineering. Hence, the accuracy of traffic data is of paramount importance. This study compares new technologies available for traffic data collection considering their accuracy and applicability in the Sri Lankan context. Traffic in Sri Lanka is of heterogeneous nature, as opposed to the homogeneous nature observed in most developed countries. Hence, collection of traffic data poses several challenges that affects its accuracy. Three techniques, the infrared driven TIRTL instrument, the video image processing-based TRAZER application and the traffic data collection method using the Google distance matrix application programming interface (API), with respect to their data collection accuracy are reviewed in this study. The fundamental macroscopic traffic data variables (fl ow and speed) were evaluated against control surveys. It was found that each technology has its strengths and weaknesses and needs to be used appropriately. The TIRTL instrument fared better on road sections on level terrain when the crossfall did not obstruct the infrared beams. Such occasions provided a rich set of microscopic traffic data. The TRAZER software delivered data up to a 100 % accuracy. However, this required the user to go through a lengthy postprocessing routine to extract the final set of traffic data. Google traffic data collection provides highly accurate results when estimating link speeds. This method is ideal for collection of bulk data with spatio-temporal variations and the process can be fully automated to reduce the human resource requirement.

How to Cite: Jayaratne, D., Vidanapathirana, C. and Pasindu, H., 2020. Challenges faced in heterogeneous traffic data collection: a comparison of traffic data collection technologies. Journal of the National Science Foundation of Sri Lanka, 48(3), pp.227–237.
Published on 06 Oct 2020.
Peer Reviewed

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