Passenger-Oriented Multi-Objective Optimization Of Energy-Efficient Timetables For Metro Systems

Metro system,which plays an important role in urban economies and the social developments of big cities due to their high loading capacity,high running speed,punctuality,and low energy consumption.Compared with the buses and prnvate cars,metro systems have expanded rapidly in big cities all over the world due to its energy-saving advantages.However,with the large-scale construction and investment,the prolem of massive energy consumption by trains may led to high operating cost and pollution.Therefore,to facilitate the construction of environmental-friendly transport mode and sustainable society,we focus on the passenger-oriented and eco-friendly train timetabling optimization problemThe main purposes of this thesis are aiming to optimize the timetables for saving energy consumption of metro trains with considering the properties of the train electricity supply,passenger demand,and metro line itself.In this thesis,in order to improve the service quality and reduce the operating cost,we collaboratively optimize the speed profiles,the departure and arrival times of trains.Then,the multi-objective timetable optimization model for metro system is proposed,and the algorithm is designed.In detail,the following reaserch works have been done,and shown as follows(1)In this study,we provide a precisely computing model for train speed profiles First,according to the multi-phase speed limits in the sections in a metro line,each section will be divided into several speed-limited segments.Second,the trains adopt different running strategies(acceleration-coasting-deceleration and acceleration curising-coasting-deceleration)based on the different distances of the segments.Third,the optimal coasting-switching points on each segment are theoretically analyzed.Thus,we develop an integrated energy-efficient timetable and speed profile optimization model,and then verify the primal problem can be transformed as a strictly convex probem.The active set method will be used to solve.Finally,we present a numerical example with real-world operational data from the Beijing Metro Line.The results show that the provided method can save energy consumption for one train in one cycle by 4.52%(2)In this study,we provide a piece-wise computing model for train regenerative energy.First,we analyze the characteristics of passenger demands in metro system After introduced regenerative energy devices,the function of utilization rate of regenerative energy is calculated based on the overlapping times of traction and decelerating trains.Second,since the trains adopt different running strategies acceleration-curising-deceleration based on the different distances of the segments,the optimal cruising-switching time points on each segment are calculated.Thus,we develop a bi-objective nonlinear programming model to determine the optimal timetable and speed profile with minimum energy consumption and passenger waiting time.After that,the weighted sum method is introduced to make the bi-objective prolem as single objective,and primal problem can be transformed as a strictly convex probem.Then,the optimization solver LINGO is used to solve the problem.Finally,we conduct a numerical example based on the real-world data from the Beijing Metro Line.The results show that the proposed method can save energy consumption by 6.0%and decrease the average passenger wating time by 10.9%simitaneously.(3)In this study,we provide a multi-train energy allocation and passenger demand assignment model.First,we analyze the complex passenger demands in peak hours and electricity transmission processes in metro systems.Based on energy-regenerative technologies and smart-card data,this study formulates an optimization model incorporating energy allocation and passenger assignment to balance energy use and passenger travel time.To generate an irregular and cyclic timetable,a parallelogram-based method is developed to generate randomly feasible timetables.The Non-Dominated Sorting Genetic Algorithm-? is applied and the core components are redesigned to obtain an efficient Pareto frontier of timetables.The suggested approach is illustrated using a bi-directional metro line in Beijing.The results show that the proposed approach significantly improves regenerative energy use by 38%and reduces total travel time by 3055 h,compared to the current timetable.(4)In this study,we introduce a train stop-skipping pattern in a precisely computing model.First,according to the different distances of each segment,we adopt the trains running strategies as acceleration-coasting-deceleration and acceleration-cruising-coasting-deceleration.After introduced the train stop-skipping patterns,the boarded and alighted passengers at stations will be calculated during the off-peak hours.Second,we develop a two-stage stop-skipping strategy to illustrate that the skipped stations are chosen with passenger demands and energy consumption.Third,we present a non-linear programming problem with minimum traction energy consumption.Then,to verify the primal problem can be reformualated as a convex quadratic programming problem.Finally,the optimization solver CPLEX is adopted,a numerical example is conducted of a metro line in Beijing.The results show that the developed approach can reduce the traction energy consumption by 15.39%and increases the loading volume 3.56 person per train per minute.