Intensive Scheduling Method of Ride-Sharing Based on Demand Density Prediction
An intense scheduling strategy for online vehicles based on demand density prediction is suggested to increase the order acceptance rate and profitability of these vehicles as well as attain worldwide supply-demand equilibrium.The first step is to design a deep spatiotemporal residual perception network structure based on a multilayer hybrid perception field using historical data.This structure divides historical spatio-temporal data based on demand frequency and separates different types of spatiotemporal data using a convolutional exponential linear network and residual units.Accurate demand density prediction is achieved by combining the fusion and summation fusion methods based on gating mechanisms to dynamically aggregate temporal,spatial,and external variables.This method also predicts the advantage of demand density clustering of online cars.Second,a scheduling mathematical model is developed,and the sensing neighborhood is created to reduce the sched-uling range and increase search efficiency.This is based on the economic benefits and demand density clustering benefits of online cars.To in-crease the search capacity of the algorithm and prevent gene deficiencies,the genetic algorithm is combined with the Hungarian algorithm.Ad-ditionally,the local random search capacity of the genetic algorithm is improved by enhancing the selection and variation operators to reduce the risk of premature maturation and to achieve the best match between online taxi and passenger,which ensures the equilibrium of supply and demand globally and overall profitability.Finally,using sizable real data sets,the performance of the prediction model and the efficiency of the scheduling technique are confirmed.According to the experimental findings,the prediction model's accuracy can reach 97%,and the scheduling algorithm's solution quality can reach 99%of the best possible result,which can be used to develop scheduling plans for online taxi platforms and guarantee the stability of the transportation system.
万方数据
维普
