Optimization And Simulation Of Urban Conventional Transit Network Considering Urban Rail Transit Influence Area

Urban public transportation is an important part of the integrated transport system.It is one of the significant carriers of carbon peaking and carbon neutrality goals and public transport priority strategy.Although the scale of transit network expands with the increasing travel demand,the sharing rate of conventional transit has been declining.This was mainly caused by three reasons.Firstly,conventional transit is inefficient.Secondly,there are few effective methods to evaluate the effectiveness of bus network from a systematic perspective.Thirdly,the rapid growth of rail transit and private cars has shared bus ridership.Therefore,the idea of“partition modeling and system optimization”was proposed and the optimization model of conventional transit network considering rail transit influence area was constructed.Then a case study was carried out.The simulation test was used to verify the model results.The contents of this paper are as follows:Firstly,the theory of transit network optimization based on parsimonious continuum model was studied.The applicability of network structure was analyzed.The basic model was constructed based on the grid network and the algorithm was compared.Secondly,the idea of“partition modeling and system optimization”was proposed to characterize urban spatial layout.An optimization model of conventional transit network considering rail transit influence area was constructed.The heterogeneity of internal and external travel demand in the rail transit influence area was described by setting the passenger flow influence coefficient,so the effect of rail transit on bus travel demand under the relationship of competition and cooperation can be described.The objective function of the model was to minimize the sum of agency cost,user cost and emission cost.The decision variables were line spacing,stop spacing and headway.The constraints were capacity limitation,minimum headway and line and stop structure.Genetic Algorithm was designed to solve the model.Thirdly,the model was applied to calculate the optimal network scale and the system cost in Suzhou.The results of this model were compared with the operating status data,the basic model,the model without considering the influence area and the model without considering emission cost.Sensitivity analysis was conducted to explore the pattern of decision variables and key parameters.The results showed that compared with the basic model,the optimized model can save 17.6%agency cost,33.1%user cost and 0.3%emission cost.The effect of passenger flow influence coefficient on stop spacing decreases with the increase of travel demand density.When travel demand density exceeds 130pax/km~2/h,the effect can be ignored.Compared with expanding the scale of network,it is better to adjust the headway in cities with rapid economic development or large travel demand.At the same time,the results also verify the model hypothesis that the relationship of competition and cooperation between rail transit and conventional transit has a significant impact on the optimization of the conventional transit network.Finally,a simulation test was used to verify the effectiveness of the optimization model.The behavior of passengers and vehicles was simulated from the micro point of view under different demand density scenarios.The simulation results showed that the model results were accurate in different scenarios.