A Study Of Bus Scheduling Optimization Based On CCBI

With the rapid development of the economy and the accelerating level of urbanization,people's living standards are getting higher and higher.In addition to,the demand for passengers' travel is also increasing.Therefore,road traffic problem severity is attracting more and more people's attention.It is difficult to solve the problem of road traffic congestion by relying on the traditional public transportation dispatching scheme.In the traditional bus dispatching,it was used the whole journey bus and the scheduling form is relatively simple.When the line has an unbalanced passenger flow along the line,the whole journey bus cannot play a good optimization effect.Based on the passenger flow data,this paper predicts the crowding coefficient in the bus in a certain period of time in the future.When optimizing the combination of short-turn bus and the whole journey bus,the congestion factor is considered in the bus dispatch optimization model.The departure interval between the whole journey bus and the short.turn bus is determined by reasonable selection of the bus dispatch form,and then this paper formulates a reasonable calculation method of time table.Finally,this paper sets out the optimization scheduling plan of line between short.turn bus and the whole journey bus.Firstly,this paper introduces the classification of bus dispatch,the summary of bus dispatching methods and the influencing factors of bus comfort.By analyzing the spatio-temporal characteristics of the passenger flow data of the bus station,and finding out the distribution characteristics of the passenger flow in time and space.Based on the statistics of the number of people getting on and off the bus and the number of people in the bus,this paper uses the RBF neural network algorithm to predict the number of people in the bus in a certain period of time in the future.By analyzing the evaluation indicators,it is found that the established prediction model has a good prediction effect.Secondly,this paper takes the bus station Gaizhou Street to Software Park Road as an example and the cloud model is used to determine the congestion coefficient in the bus based on the passenger flow data.Through the simulation results,it is found that the congestion from the Gaizhou Street to the Zhilin Park is getting higher and higher and the service level is getting lower and lower and the service level is reduced from C to F.It is also finds that from the Zhilin Park to the Software Park,the congestion in the bus has eased,and the service level has risen from F to C,but the bus is still in a crowded state.The simulation results show that it is effective to use the cloud model judge the crowding coefficient in the bus.Finally,this paper takes the 10 bus in Dalian as the research object,and the bus scheduling optimization model is established based on the historical data and this paper calculates and verifies the model.At the same time,compared with a single bus dispatching form,the simulation results show that the established bus dispatching optimization model can effectively reduce the total cost of the system and further prove the superiority of the established model.