| Expressway tow and rescue refers to the important work of promptly and effectively removing accident or malfunctioning vehicles from the road,restoring road traffic flow,and ensuring traffic safety when traffic incidents occur on expressways.With the rapid development of China’s expressways and the increase in motor vehicles,expressway tow and rescue is facing more and more challenges and difficulties,such as the lack of a sound tow and rescue system,low-quality personnel,and imperfect layout of rescue service points.These problems have had a significant impact on the efficiency and quality of expressway tow and rescue,increased the risk and losses of traffic accidents,and reduced the service level and social image of expressways.Therefore,studying the optimization methods for the layout of expressway tow and rescue service points is of great theoretical significance and practical value for exploring the standardization,specialization,and rational layout of expressway tow and rescue,improving the reliability,service capabilities,and management level of expressway tow and rescue.Through analyzing the characteristics of traffic incidents on expressways and the emergency rescue process,the importance of tow and rescue for ensuring expressway safety and traffic flow was clarified.Based on this,further analysis was conducted on the characteristics of expressway tow and rescue and typical rescue service models at home and abroad.Some shortcomings of the current tow and rescue service model were summarized.Combining with reliability analysis of rescue time,optimization of the layout of expressway tow and rescue service points was proposed from two aspects: optimization of tow and rescue resource allocation and optimization of control measures.This provides a basis for optimizing the layout of tow and rescue service points on expressways.Based on the quantitative analysis of tow and rescue resources on expressways,the advantages and disadvantages of multiple linear regression prediction methods,artificial neural network prediction methods,and case-based reasoning prediction methods were compared.A combined method based on case-based reasoning and entropy was determined for predicting the demand for rescue resources.To address the issue of reduced case retrieval speed due to the increase in the size of the case library,SVM classification method was proposed to classify the case library according to similarity to improve case retrieval speed.Through engineering case analysis,the specific process of predicting the demand for tow and rescue resources on expressways was demonstrated,and the feasibility and accuracy of the method used were verified,laying a foundation for optimizing the allocation of tow and rescue resources.Through analyzing the characteristics of site selection and resource allocation for highway tow and rescue service points,the maximum coverage model is adopted to optimize the site selection.Based on the prediction of rescue resource demand and site selection optimization,a resource allocation model for tow and rescue is established with the goal of achieving the highest rescue efficiency under certain cost constraints.Genetic algorithm is used to solve the model.Control and optimization measures for tow and rescue,such as emergency rescue system,tolls,rescue team construction,network rescue platform construction,and business expansion,are studied.The specific process of optimizing the layout of tow and rescue resources is demonstrated through engineering examples,and the feasibility of the methods used is verified. |
