Research And Implementation Of Dynamic Pricing Model For Multiple Mode Ride Hailing

The dynamic pricing model of ride hailing can set the ride price dynamically based on the number of ride demand in the market,in order to achieve the purpose of increasing the revenue of ride hailing platform.In the real world,the ride hailing demand is unevenly distributed in time and space.Most of the ride hailing demand occurs during morning and evening rush hours,and the peak areas are mostly densely populated areas.According to this characteristic,the dynamic pricing model can price the online car hailing orders in different regions separately,so as to balance the supply and demand in various regions and increase the revenue of online ride hailing platforms.Therefore,the research on the dynamic pricing algorithm of ride hailing has commercial significance and social significance.However,the current research on the dynamic pricing model of ride hailing focuses on a single model,and the ability to guide vehicle is poor when faced with complex scenarios,resulting in the loss of some online car hailing orders.This thesis proposes a dynamic pricing model MMDP for multiple ride hailing modes.The article introduces the dynamic pricing model of ride hailing under two ride modes at first.In MMDP,the model can calculate the number of ride hailing orders and the revenue of the platform in each region based on the pricing matrix and scheduling matrix.Using the current number of ride hailing orders,the model can deduce the distribution of vehicles in every region in the future period.Combined with the forecast results of ridding demand in each region in the future,the system can estimate the income of ride hailing in the future.Then the article proposes the objective function of the model in this thesis,the function can take into account the income of the car hailing platform in the current period and the future period,so as to achieve the purpose of increasing the platform overall income.In addition,the article expands the MMDP model from two ride modes to any kind of ride mode,and enters the type of ride mode K as a parameter into the model to improve the versatility of the MMDP.After the model objective function is proposed,the article introduces the optimization algorithm of MMDP model.In this thesis,L-BFGS-B algorithm and basic-hopping algorithm are used as the optimization algorithms of MMDP to solve the model parameters.The article analyzes the influence of different variables on the revenue of each ride mode under various ride modes,and solves the partial derivative of the model objective function for each variable under different conditions inversely,and forms the Jacobian matrix as the input parameter of the optimization function.Through several iterations solve the optimal pricing strategy and optimal scheduling strategy for the current period.In the experimental part of this thesis,we compares the performance of the MMDP model with other single mode ride hailing pricing models on the number of ride hailing orders and total platform revenue on real data sets.Experimental results show that the model in this thesis can generate more car hailing orders and platform revenue than other models.Then,the experiment compares the performance of the two order strategies in this thesis under different supply conditions by changing parameters,and analyzes the impact of different penalty parameters on the model.Finally,the experiments verify the total platform revenue and the number of ride-hailing orders of the MMDP model under multiple ride hailing modes.The results show that the more types of ride hailing models,the model can generate more revenue and ride hailing orders.