چکیده مقاله
In the science of machine learning Machine Learning , the issue of designing machines that learn by using the examples given to them and their own experiences is discussed In fact, in this science, an attempt is made to design a machine in such a way that it can learn and act without explicitly planning and dictating each and every action by using algorithms Road maintenance operations are very important in preserving and preventing their premature deterioration and using the maximum capacity of the road in its expected service life which needs to be done according to a clearly defined plan and according to the needs and problems of the road In this paper, a framework for finding optimal maintenance policies in a road network was proposed This framework included: identification of effective factors in policy making, network clustering based on these factors, identification of criteria influencing optimal policies and determining optimal policies and periods using these criteria To test the applicability of the framework, it was applied step by step in the road network of Iran To quantify the cost of policies, seven machine learning algorithms including gradient boosting regression, lasso, ridge, random forest regression, elastic network, neural network and multiple linear regression were tested Using the coefficient of determination as an accuracy metric, it was found that in all subnets, the gradient boosting regression has the highest accuracy in the test set while the other algorithms are between 50% and 90% The conditions of the sub grids were modeled using the Markov chain model and measured by the average pavement condition index PCI Having the cost of the policies and the PCI of the subnets, the optimal policy was selected using the technique of priority ordering based on similarity to the ideal solution TOPSIS It was concluded that the maintenance period of four years was optimal in all sub networks Roads in warm regions require the most stringent policies, followed by roads in cold and wet regions The same applies to arterial roads followed by local roads
کلیدواژهها
نویسندگان
مریم رجبی پرور حویق
شهرداری رشت
Zahra Rezaei Barzani
Master's student in Entrepreneurship Management, Development major, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
شیوه ارجاع
Rezaei Barzani, Zahra,1403,An Analysis On The Application Of Machine Learning In Road Maintenance Policy,17th International Conference on Management, World Trade, Economics, Finance and Social Sciences
ارائهشده در
مجموعه مقالات هفدهمین کنفرانس بین المللی مدیریت، تجارت جهانی، اقتصاد، دارایی و علوم اجتماعی25 آذر 1403