Network Car Hailing Pricing Model Optimization in Edge Computing-Based Intelligent Transportation System

The purpose of this study is to investigate Network Car Hailing (NCH) price or the deficiency in NCH Platform in Edge Computing (EC)-based Intelligent Transportation System. Aiming at the uncertain capacity and unbalanced load in the car-hailing platform, this work innovatively introduces the EC to unload, constructs an EC-based online car-hailing resource allocation and pricing optimization model by combining with factors such as the number of users and reputation in the network, and further analyzes the performance of the resource allocation and pricing optimization model in the constructed carhailing platform through simulation experiments. The experimental results show that with the increase in the number of vehicles with computing tasks, the amount of resources purchased from various car-hailing vehicles also increases, the cost of paying is showing an increasing trend, and the utility function of NCH platforms and operators has declined. In the task resource analysis, the average unloading utility of the algorithm in this work is the highest, and the average unloading utility is basically stable at about 70% when the number of vehicles is 98. With the increase of the delay weight, the delay is smaller and the energy consumption is lower. Therefore, the model constructed in this work can minimize the average cost and consumes less energy while the delay is small. It can provide a reference for intelligent pricing and resource allocation of the online car-hailing platform in the later period of intelligent transportation.