Matching path optimization of carpooling mode for private cars considering real-time traffic conditions and passenger satisfaction

Private carpooling is an effective choice to ensure travel comfort and improve resource utilization. This article aims to improve the quality and efficiency of private carpooling services, in order to alleviate urban traffic congestion and environmental pollution. The paper introduces real-time traffic data from multiple sources and combines passenger satisfaction evaluation indicators (waiting time, maximum riding time, carpooling preferences, etc.) to construct a dynamic path matching optimization model for private carpooling modes. This model achieves goals such as optimal path, high passenger travel satisfaction, and maximum carpooling ratio. The methods used in this study include real-time traffic data collection and processing methods, construction and optimization methods of passenger satisfaction evaluation indicators, the design of a path matching optimization model based on traffic conditions, and a description of the private carpooling mode matching path optimization method considering real-time traffic conditions and passenger satisfaction. The method uses passenger satisfaction as the optimization goal, and combines traffic condition information to match the path, thereby achieving path optimization for private carpooling services. The experimental results show that this method can effectively improve the quality and efficiency of private carpooling services, while reducing travel time and costs. Under the influence of real-time traffic data and passenger satisfaction evaluation indicators, the method can choose a more suitable travel path, improve passengers’ travel experience and satisfaction.