Optimization Research On Operation Dispatching Program Of Urban Rail Transit

Urban rail transit(URT),such as metro or light rail,has been regarded as the backbone for green transport networks in recent decades with its safety,large transport capacity and efficient energy consumption.A construction boom of URT is developing in China at present.Metropolis such as Beijing and Shanghai have built relatively complete rail transit networks.Many other medium-sized or small cities have also formed rail transit backbone networks.Whereas,with increasing number of commuters taking URT as the main means of daily commuting,challenges are imposed on the URT operational companies to provide a satisfying service quality with limited transportation resources.The service level on rail transit is very much affected by the service frequency.Therefore,more frequent train departure is accepted as a straightforward and effective solution to improve service quality.This,however,incurs increased operating costs to operational companies as the in-service trains have to follow the tight timetable with high service frequencies.Therefore,it is essential to study how to reduce the operating costs on the premise of excellent service quality,by efficiently utilizing of the limited transportation resources.The main research work of this thesis consists of two parts,which are stated as follows.Firstly,this thesis proposes an optimizing strategy of service frequency to achieve a tradeoff between service quality and operating cost.We first analysis the interest conflict between the metro operational company and passengers in the train scheduling process.Then we model the dynamic decision-making process of service frequency based on game theory and traffic assignment model to achieve a tradeoff.In the upper model,the operational company aims to achieve the minimum operating costs by adjusting the service frequency.While in the lower model,passengers expect the minimum travel costs,and they are assigned in different means of transportation based on User Equilibrium.A modified particle swarm optimization is proposed to solve the Stackelberg-based optimizing model of service frequency.Finally,the illustrative example shows the effectiveness of the proposed optimizing strategy of service frequency.In addition,an incentive-based optimizing strategy of service frequency is proposed by using newsvendor model,in which incentive policy is taken into consideration to help smooth passenger demands and relieve the transportation burden in peak hours.By motivating some passengers to advance or postpone their departure time to off-peak hours with incentive measures such as discount coupons,the total cost,consisting of shortage component,surplus component and operational component,can be minimized.Finally,extensive numerical experiments are conducted to verify the efficiency of our approach,showing its ability to effectively reduce the operating costs in peak hours.