Research On Dynamic Taxi Sharing Model

In recent years,traffic congestion has become an important factor restricting the rapid development of cities.Improving the average daily traffic volume and traffic sharing rate of urban public transportation in China is one of the important ways to alleviate the problem of urban traffic congestion.As an important part of urban public transportation,taxi sharing is an effective means to improve the taxi sharing rate.taxi sharing refers to a mode of travel in which passengers with the same or similar routes choose to complete their journey in the same taxi.Aiming at the problem of taxi sharing,domestic and foreign scholars have done a lot of theoretical analysis and example verification from the aspects of taxi sharing billing model,taxi sharing optimization goal,taxi sharing problem solving algorithm and so on,and achieved certain results.However,there are still the following shortcomings: the optimization target is too single,and the interests of drivers,passengers and platforms are not taken into consideration comprehensively;Most studies focus on static taxi ride-sharing,and the time complexity of the existing dynamic taxi ride-sharing model is too high to accurately depict the existing problems.Ignoring the influence of road traffic conditions on vehicles in co-occupancy time.In order to solve the above problems,this thesis establishes a dynamic taxi sharing system.The taxi dynamic ride-sharing system includes taxi sharing billing model,taxi dynamic ridesharing model and two-stage genetic algorithm.The billing model meets the conditions that the more number of carpoolers are,the higher the income of drivers and the lower the travel cost of passengers.Based on the billing model,a dynamic taxi sharing model with the goal of maximizing the platform ride rate is established.The model considers the interests of drivers,passengers and platforms,and satisfies various demands and constraints in the process of taxi sharing.Aiming at the dynamic taxi sharing model,the offline shortest path algorithm and twostage genetic algorithm are designed.The shortest path algorithm mainly updates the shortest path in the road network according to the real-time traffic conditions.In the first stage of the two-stage genetic algorithm,the main purpose is to establish the candidate driver set through distance and path index,so as to improve the calculation efficiency of the model.The second stage of the algorithm uses genetic algorithm to solve the model.Finally,the convergence of the two-stage genetic algorithm is proved.This thesis relies on Python language and Beijing Didi taxi travel data for numerical experimental analysis.The experimental results show that 50% of the taxi vehicles can solve 85%of the travel demand under the dynamic taxi sharing model,which verifies the convergence and effectiveness of the algorithm and proves that the model can well alleviate the problem of urban traffic congestion.