Research On The Matching Mechanism And Pricing Strategy Related To The Capacity Of Online Vehicles

In recent years,online ride-hailing has gradually become an important way for people to travel daily with its convenience and convenience.As an emerging mode of travel,online car-hailing has brought great convenience to the public.On the one hand,the problem of information asymmetry between drivers and passengers has been solved for a long time.On the other hand,it greatly complements the available transport capacity in the city and improves the utilization rate of vacant vehicles in the city.As an intermediary to improve services for drivers and passengers,the core functions of ride-hailing platforms are matching and pricing.However,in the existing online ride-hailing platforms,the demand expression ability of passengers is weak,the accuracy of supply-demand matching results is low,a large number of transactions are lost,and the platform income is damaged.In a city,the demand distribution of online ride-hailing is extremely uneven,and the starting point or finishing point of orders is highly concentrated in some time periods.Most of the existing online ride-hailing platforms adopt a tier-type price function with a price increase mechanism during the peak period of car use.Passengers have the same cost of car use at the same time,which cannot attract part-time drivers to provide transport capacity,and the transaction volume and success rate of rental are relatively low.In view of the shortcomings of the existing matching mechanism and pricing method,the work and research contents of this paper are as follows:(1)This paper proposes a personalized matching mechanism for online ride-hailing under the condition of capacity overflow,which consists of platform benefit calculation algorithm and driver assignment algorithm.The platform benefit calculation algorithm mainly calculates the platform’s benefit after the completion of the order,while the driver allocation algorithm satisfies the demand of passengers and drivers to choose between each other.Through experiments and theoretical analysis,the mutual selection mechanism proposed in this paper can significantly improve the satisfaction of passengers compared with the order snatching mechanism and order sending mechanism,and it is verified that the revenue of the platform increases with the increase of user satisfaction,which solves the problem of insufficient fairness of passenger selection under the existing platform.(2)This paper proposes a regional scheduling method under the condition of transport capacity shortage,and encourages drivers to take orders across regions by using an early-warning incentive mechanism,so as to achieve the goal of trans-regional transport capacity rebalance.By analyzing and processing the order information,the transport capacity early warning mechanism of adjacent area is established.When regional transport capacity is tight,neighboring drivers are encouraged to accept cross-regional orders,so as to reduce the number of unmatched orders in the region during the period of regional transport capacity tension,and improve platform utility and passenger satisfaction.By comparing the trans-regional transport capacity rebalancing mechanism with other mechanisms through examples,this paper proves that the trans-regional transport capacity rebalancing mechanism can improve platform revenue and drivers’ utility,and is effective in rebalancing regional supply and demand relations,which provides a reference for online ride-hailing platforms to balance supply and demand relations on a macro level.(3)This paper proposes a dynamic pricing strategy.By increasing the capacity of part-time drivers and reducing the number of orders from price-sensitive users,the order volume of the platform in the period of tight capacity can be increased,which significantly alleviates the pressure of the platform in the period of tight capacity and improves user satisfaction.The price function is simulated through experiments to obtain the optimal demand and supply under the optimal pricing,which verifies the effectiveness of the dynamic pricing mechanism proposed in this paper in alleviating the load pressure during the peak period of online ride-hailing.