On-Demand Ride Service System Optimization Based On Cellular Automaton Model

With the rise of the mobile internet economy,on-demand ride services(or ride-sourcing services)provided by transportation network companies(TNCs)have become prosperous as an emerging mobility service mode.In this paper,we present a simulation framework to study how such an on-demand ride services platform should optimize li considering traffic dynamics,that it,the platform charges the passengers and pays the ride-sourcing drivers with the wage.Specifically,we use the cellular automaton model to simulate route choice behavior of both regular cars that impact background traffic dynamics and ride-sourcing cars that provide on-demand ride services in the Manhattan-like urban network.In this model,passengers are sensitive to the waiting time and price,while ride-sourcing drivers are sensitive to the wage.The proposed model framework is capable of simulating the on-demand travel behavior of multiple types of stakeholders including the on-demand ride services platform,ride-sourcing drivers and cars,and passengers.The model also realizes the process of trip requests,passenger-driver matching,dispatching,and dynamic path planning.The main work of the thesis is as follows:1.The on-demand ride services single travel model is proposed,and the three global densities of the background traffic flow in the free-flow range are considered to represent the urban traffic flow in off-peak period,flat hump period and peak period.Research the platform's optimal strategy to assess the impact of various operational strategies on drivers and passengers.The results show that it is optimal for the platform to charge a higher price and offer a higher payout ratio as demand increases,while the platform should lower its payout ratio as the number of service providers increases.Furthermore,the optimal price and payout ratio are not necessarily monotonic when the travel distance increases.2.On the basis of the on-demand ride services single travel model,construct on-demand ride services sharing model.The ride-sourcing cars can take multiple passengers to realize the simulation function of ridesharing.The OD uniform distribution situation and the OD uneven distribution situation are simulated.were simulated.The simulation optimization is carried out with the maximum benefit of the on-demand ride services platform as the objective function,and the sensitivity analysis of the parameters affecting the revenue of on-demand ride services platform is carried out.The distribution of surge pricing ratio,wage rate,fare,travel distance and waiting time in different spatial areas of the city was analyzed.