Distribution Function Similarity of Overloaded Commercial Trucks Detection (Case Study: Iran)
Abbas Mahmoudabadi1, Hassan Abdous2, Fatemeh Pourhossein Ghazimahalleh3
1Dr. Abbas Mahmoudabadi, Director, Master Program in Industrial Engineering, MehrAstan University, Gilan, Iran.
2Hassan Abdous, Deputy of Traffic Safety Department of Road Maintenance and Transport Organization, Tehran, Iran.
3Fatemeh Pourhossein Ghazimahalleh, Ph.D. Student, Department of Information Technology, University of Tehran, Tehran, Iran.
Manuscript received on 25 November 2024 | First Revised Manuscript received on 02 February 2025 | Second Revised Manuscript received on 20 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 34-41 | Volume-13 Issue-4, March 2025 | Retrieval Number: 100.1/ijese.A101514011224 | DOI: 10.35940/ijese.A1015.13040325
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Monitoring overloaded vehicles is an operational aspect of ensuring the safe operation of vehicles and mitigating damages to road pavement. Since, the concentration on overloading is claimed to differ for the type of trucks, where trailers are more allegedly engaged under enforcement, this research aims to explore the differences in overload monitoring among various kinds of vehicles, including trailers, heavy trucks, and light trucks in intercity transportation. To this purpose, at the first stage, the effect of the number of weighing stations and cargo trips on overloading detection has been investigated through analysis of variance. It is followed by considering the number of registered trucks as an exposure factor to standardize the enforcement rate in the second stage. An adapted version of the statistical method of Kolmogorov-Smirnov, known as three samples, is finally utilized to investigate the similarity of distribution functions of overloading detection for three types of trucks including trailers, heavy trucks, and light trucks. Data, for registered trucks as well as detected trucks as overloading across the road network in the West-Asian country of Iran, has been collected for a year followed by categorizing into thirty-one provinces. The results revealed that whereas the number of weighing stations and cargo trips do not have significant effects on overloading detection, the similarity of distribution functions for overloading detection is different for three types of trucks over the provinces. Therefore, all types of trucks are not equally under overloading control and transport authorities should redesign overloading detection approaches throughout the enforcement instruction applied in intercity transportation.
Keywords: Trucks’ Overloading, Traffic Enforcement, Similarity Distribution Functions, Intercity Transportation.
Scope of the Article: Data Analytics