Association of Vehicle Count Data Obtained Via Image Processing Techniques Compared with Microsimulation Program Analysis Results

Seyitali İlyas (), Bahadır Ersoy Ulusoy (), Sevil Köfteci () and Yalçın Albayrak ()
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Seyitali İlyas: Akdeniz University
Bahadır Ersoy Ulusoy: Antalya Bilim University
Sevil Köfteci: Akdeniz University
Yalçın Albayrak: Akdeniz University

Networks and Spatial Economics, 2024, vol. 24, issue 3, No 5, 655-680

Abstract: Abstract As the population in cities increases, traffic problems have emerged, especially at intersections with high traffic density. Increasing traffic density leads to longer transportation times, higher fuel consumption, and elevated levels of environmental pollution. Various techniques have been employed to decrease traffic congestion. In order to apply these methods, the degree of traffic density must first be determined. This is typically done through vehicle counting studies in the field using camera images. However, manually counting vehicles from camera images is a very detailed process. Therefore, various automated methods based on image processing techniques are preferred today to perform these operations faster and more accurately. In this study, we designed virtual zones using different vehicle counting methods at intersections based on image processing techniques. We obtained vehicle count data from four methods, including manual counting and three methods based on image processing techniques. We evaluated the accuracy of the counting results using transportation engineering parameters such as density and traffic volume. Additionally, we modeled the signalized intersection in the AIMSUN simulation program. The study found that the “New Type Virtual Zone” method resulted in vehicle counts that were 95% accurate, and the average success rate of the AIMSUN simulation analysis results performed with this data was 83.71% accurate.

Keywords: Signalized intersection analysis; Microsimulation; Traffic management; Vehicle counting; Image processing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11067-024-09630-6

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