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Quality Assessment of Long-Term Traffic Data Collection by Video Detection Technique in a Busy Urban Intersection

Jenitta Pragalathan and Dieter Schramm
University of Duisburg-Essen, Chair of Mechatronics, Duisburg, Germany
Abstract—This paper examines the performance of video detection technology by comparing the traffic flow data collected through this method with the data obtained by induction loop detectors. The data collection was conducted by using manual counting, inductive loop detectors and video detection technique. Then the quality of inductive loops was analyzed by comparing the traffic data from three short-term random loop datasets with original ground truth data (“Manual Count Made Easy [1]”). The analysis confirmed that the loop data is much reliable to be used for quality assessment of video detection (Vehicle Counter) for long-term continuous data collection. In the next phase of analysis, the data collection was done for the time period of a week and the percentage error values for every one-hour period were calculated. Following this, the trendline of error values were drawn and observed to find the effect of time of day, sunny/cloudy day, location of virtual sensors and type of turning movement. It was found that the daytime data from video detection is within the acceptable range of percentage error (5%). Hence neglecting the night time data, percentage distribution of error values was determined. The evidence from the study confirmed the importance of location of cameras and underlined the efforts needed to configure the virtual sensors and gates.
Index Terms—traffic data collection, performance analysis, video detection, manual counting tool, vehicle counter, MCME, urban intersection, influence of sunlight

Cite: Jenitta Pragalathan and Dieter Schramm, "Quality Assessment of Long-Term Traffic Data Collection by Video Detection Technique in a Busy Urban Intersection," Journal of Traffic and Logistics Engineering, Vol. 7, No. 2, pp. 35-40, December 2019. doi: 10.18178/jtle.7.2.35-40

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