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

Abstract

Traffi c data are the fundamental inputs to traffi c fl ow analysis and simulation studies, which facilitate decision making in the fi eld of traffi c engineering. Hence, the accuracy of traffi c data is of paramount importance. This study compares new technologies available for traffi c data collection considering their accuracy and applicability in the Sri Lankan context. Traffi c in Sri Lanka is of heterogeneous nature, as opposed to the homogeneous nature observed in most developed countries. Hence, collection of traffi c data poses several challenges that aff ects its accuracy. Three techniques, the infrared driven TIRTL instrument, the video image processing-based TRAZER application and the traffi c 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 traffi c 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 traffi c 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 fi nal set of traffi c data. Google traffi c 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.