scholarly journals Optimal Operation Scheme with Short-Turn, Express, and Local Services in an Urban Rail Transit Line

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Tao Feng ◽  
Siyu Tao ◽  
Zhengyang Li
Keyword(s):  
Search Algorithm ◽  
Real Life ◽  
Optimal Operation ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Nonlinear Program ◽  
Urban Rail ◽  
Local Services ◽  
Minlp Model

Flexible railway operation modes combining different operation strategies, such as short-turn, express, and local services, can significantly reduce operator and user costs and increase the efficiency and attractiveness of rail transit services. It is therefore necessary to develop optimization models to find optimal combinations of operation strategies for urban rail transit lines. In this paper, a model is proposed for solving the urban rail transit operation scheme problem. The model considers short-turn, express, and local services with the aim of minimizing the operator’s and users’ costs. The problem is first decomposed into two subproblems: the service route design problem and the passenger assignment problem. Then, a mixed-integer nonlinear program (MINLP) model is formulated, and linearization techniques are utilized to transform the MINLP model into a mixed-integer linear programming (MILP) model that can be easily solved by commercial optimization solvers. To accelerate the solution process, a heuristic search algorithm is proposed to obtain (nearly) optimal solutions based on the characteristics of the model. The two subproblems are solved iteratively to improve the quality of solutions. A real-life case study in Chengdu, China, is performed to demonstrate the effectiveness and efficiency of the proposed model and algorithm.

scholarly journals A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Miao Zhang ◽  
Yihui Wang ◽  
Shuai Su ◽  
Tao Tang ◽  
Bin Ning
Keyword(s):  
Urban Traffic ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Train Scheduling ◽  
Passenger Demand ◽  
Urban Rail ◽  
Train Services ◽  
Minlp Model ◽  
Beijing Subway

In urban rail transit systems, train scheduling plays an important role in improving the transport capacity to alleviate the urban traffic pressure of huge passenger demand and reducing the operation costs for operators. This paper considers the train scheduling with short turning strategy for an urban rail transit line with multiple depots. In addition, the utilization of trains is also taken into consideration. First, we develop a mixed integer nonlinear programming (MINLP) model for the train scheduling, where short turning train services and full-length train services are optimized based on the predefined headway obtained by the passenger demand analysis. The MINLP model is then transformed into a mixed integer linear programming (MILP) model according to several transformation properties. The resulting MILP problem can be solved efficiently by existing solvers, e.g., CPLEX. Two case studies with different scales are constructed to assess the performance of train schedules with the short turning strategy based on the data of Beijing Subway line 4. The simulation results show that the reduction of the utilization of trains is about 20.69%.

A cooperative evacuation strategy for mass passenger flow in urban rail transit transfer stations

2020 ◽  
pp. 2150007
Author(s):  
Jia Hong-Fei ◽  
Sang Heng ◽  
Luo Qing-Yu ◽  
Yang Jin-Ling ◽  
Miao Hong-Zhi
Keyword(s):  
Real Life ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Nsga Ii ◽  
Transit System ◽  
Passenger Flow ◽  
Evacuation Strategy ◽  
Urban Rail ◽  
The Status ◽  
Transfer Stations

In reviewing the evacuation problem of mass passenger flow in urban rail transit transfer stations, the cooperative evacuation strategy considering urban rail transit and emergency bus simultaneously is found to be an efficient way. In this work, firstly, the dynamic characteristics of mass passenger flow are analyzed based on the abstraction and simplification of major entities of urban rail transit, including passengers, stations and trains. Then, the operations of the urban rail transit system are modeled, including the boarding, landing and transferring processes of the passengers and the status updating flows of the trains and stations. To realize the cooperative evacuation, a multi-objective optimization model considering the evacuation speed, the number of passengers transferred, and the amount of emergency buses is proposed. The NSGA-II algorithm is adopted to solve the proposed model, which can balance the theoretical validity and computational convenience. Last, the proposed strategy is applied in a real-life case based on the Shanghai Metro line, and the results verify its effectiveness and efficiency.

Schedule Coordination Method for Last Train Transfer Problem

2017 ◽  
Vol 2648 (1) ◽  
pp. 86-95 ◽  
Author(s):  
Xueping Dou ◽  
Xiucheng Guo
Keyword(s):  
Transfer Problem ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Transit System ◽  
Schedule Coordination ◽  
Urban Rail ◽  
Integer Nonlinear Programming ◽  
Minlp Model ◽  
Optimization Solvers

This paper proposes a schedule coordination method for last train service in an urban rail transit system. The method offsets and perturbs the original train schedule to reduce transfer failures across different lines, and it considers the effect of schedule adjustments. The proposed problem is formulated as a mixed-integer nonlinear programming (MINLP) model. The MINLP model is equivalently transformed into a mixed-integer linear programming (MILP) model that can be exactly solved by commercial optimization solvers. A case study based on the mass rapid transit system in Singapore was conducted. The results of the case study indicate that the train schedule that is coordinated by the developed model is capable of substantially improving operational connectivity. Therefore, the model proposed in this study can be employed as a viable tool to assist with the coordination of train schedules for public transport operators.

Mean-variance model for optimization of the timetable in urban rail transit systems

2017 ◽  
Vol 232 (4) ◽  
pp. 1005-1020
Author(s):  
Hui Yang ◽  
Xiang Li ◽  
Xin Yang
Keyword(s):  
Real Life ◽  
Urban Rail Transit ◽  
Data Driven ◽  
Rail Transit ◽  
Variance Model ◽  
The Real ◽  
Urban Rail ◽  
Mean Variance ◽  
Cooperative Scheduling ◽  
Operational Data

Regenerative braking is an energy-efficient technology that converts kinetic energy to electrical energy during braking phases. For more efficient recovered energy utilization, the stochastic cooperative scheduling approach has been proposed for determining the dwell times at stations, wherein the accelerating trains can use the energy recovered from the adjacent braking trains as much as possible. Here, running times at the sections are considered as random variables with given probability functions. In this paper, the authors develop a data-driven stochastic cooperative scheduling approach in which the real data of the speed of trains are recorded and used in the place of motion equations. First, the authors formulate a stochastic mean-variance model, which maximizes the expected utilization and minimizes the variance of the quantity of the recovered energy. Second, a genetic algorithm that utilizes particle swarm optimization has been designed to find the optimal dwell times at stations. Finally, numerical examples are presented based on the real-life operational data from Beijing Yizhuang urban rail transit line in China. The results illustrate that the real-life operational data in the data-driven stochastic cooperative scheduling approach can provide a more accurate description about the movement of trains, which would result in more efficient energy saving, i.e. by 1.66%, in comparison with the stochastic cooperative scheduling approach. Most importantly, the data-driven stochastic cooperative scheduling approach results in lower variance by 68.69% and higher robustness.

Research on Urban Rail Transit Resource Allocation Based on K-Shortest Paths Algorithm

2013 ◽  
Vol 748 ◽  
pp. 1285-1289 ◽  
Author(s):  
Ju Mei Shen ◽  
Yong Sheng Shen ◽  
Lin Xu
Keyword(s):  
Resource Allocation ◽  
Search Algorithm ◽  
Shortest Paths ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Rail Network ◽  
Assignment Model ◽  
Urban Rail ◽  
Major Factors

This paper considers the major factors of passenger flow distribution and the running cost of train, explains and demonstrates how to sove the problem of urban rail transit resource allocation. The paper at first constructs urban rail network assignment model in the multiple operating lines based on the k-shortest paths algorithm, then proposes the k shortest paths search algorithm, finally the effectiveness of the model and algorithm are verified with the data from the Beijing urban rail transit network.

scholarly journals An Energy-Efficient Timetable Optimization Approach in a Bi-DirectionUrban Rail Transit Line: A Mixed-Integer Linear Programming Model

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2686 ◽  
Author(s):  
Huanhuan Lv ◽  
Yuzhao Zhang ◽  
Kang Huang ◽  
Xiaotong Yu ◽  
Jianjun Wu
Keyword(s):  
Linear Programming ◽  
Energy Consumption ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Operational Cost ◽  
System Cost ◽  
Urban Rail

The quick growth of energy consumption in urban rail transit has drawn much attention due to the pressure of both operational cost and environmental responsibilities. In this paper, the timetable is optimized with respect to the system cost of urban rail transit, which pays more attention to energy consumption. Firstly, we propose a Mixed-Integer Non-Linear Programming (MINLP) model including the non-linear objective and constraints. The objective and constraints are linearized for an easier process of solution. Then, a Mixed-Integer Linear Programming (MILP) model is employed, which is solved using the commercial solver Gurobi. Furthermore, from the viewpoint of system cost, we present an alternative objective to optimize the total operational cost. Real Automatic Fare Collection (AFC) data from the Changping Line of Beijing urban rail transit is applied to validate the model in the case study. The results show that the designed timetable could achieve about a 35% energy reduction compared with the maximum energy consumption and a 6.6% cost saving compared with the maximum system cost.

scholarly journals A Mixed Integer Linear Programming Model for Rolling Stock Deadhead Routing before the Operation Period in an Urban Rail Transit Line

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Qingwei Zhong ◽  
Yongxiang Zhang ◽  
Dian Wang ◽  
Qinglun Zhong ◽  
Chao Wen ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rolling Stock ◽  
Rail Transit ◽  
Urban Rail ◽  
Operation Period ◽  
Train Services

In an urban rail transit line, train services are performed by the rolling stocks that are initially stored at depots. Before the start of the operation period, rolling stocks consecutively leave the depots and run without passengers (deadhead routing) to the origin station of their corresponding first departure train service in an operation day (first train service) using either direct or indirect routes. This paper investigates the rolling stock deadhead routing problem in an urban transit line with multiple circulation plans, depots, and rolling stock types. Given the rolling stock circulation plans, the problem is to identify a deadhead route for the rolling stock required by the train services to cover the initial operation. By pregenerating all direct and indirect candidate deadhead routes in a polynomial manner, the problem is then nicely formulated as a mixed integer linear programming (MILP) model to minimize the total deadhead mileages. A real-world case from the urban rail transit line 3 of Chongqing in China is adopted to test the proposed method. Computational results demonstrate that the problems of large-scale instances can be quickly solved to optimality by commercial optimization solvers on a personal computer. In addition, our optimization method is better than the empirical practices in terms of the solution quality. Meanwhile, alternative measures can further decrease the total deadhead mileages according to the proposed model, e.g., opening idle switch stations and prolonging the time that is used for the rolling stock departure. Finally, the model is further extended to consider operating costs, and more computation cases are tested for better adapting to the practical operating conditions.

scholarly journals Transfer Robustness Optimization for Urban Rail Transit Timetables

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Liqiao Ning ◽  
Peng Zhao ◽  
Wenkai Xu ◽  
Ke Qiao
Keyword(s):  
Nonlinear Model ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Travel Costs ◽  
Transit Networks ◽  
Urban Rail ◽  
Integer Linear Model ◽  
Robustness Optimization ◽  
Approximate Linearization

A good timetable is required to not only be efficient, but also yield effectiveness in preventing and counteracting delays. When travelling via urban rail transit networks, transferring passengers may miss their scheduled connecting train because of a feeder train delay that results in them experiencing increased travel costs. Considering that running time supplements and transfer buffer times yield different effects on the travel plans of transferring and nontransferring passengers, we formulate an expected extra travel cost (EETC) function to appropriately balance efficiency and robustness, which is then implemented in the construction of a robust transfer optimization model with the objective of minimizing the total EETC. Next, to improve the computational efficiency, we propose an approximate linearization approach for the EETC function and introduce two types of binary variables and auxiliary substitution variables to convert the nonlinear model to a mixed-integer linear model. Experimental results show that our proposed method can yield practically applicable solutions with significant reductions in both EETC and probability of missing a transfer.

scholarly journals Equity-Oriented Train Timetabling with Collaborative Passenger Flow Control: A Spatial Rebalance of Service on an Oversaturated Urban Rail Transit Line

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chaoqi Gong ◽  
Baohua Mao ◽  
Min Wang ◽  
Tong Zhang
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Optimal Solution ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Optimization Approach ◽  
Train Timetabling ◽  
Passenger Flow ◽  
Urban Rail

On an oversaturated urban rail transit line, passengers at downstream stations have to wait for more trains until they get aboard, resulting in service imbalance problem. To improve the service quality, this paper proposes an integrated optimization approach combining the train timetabling and collaborative passenger flow control, with the aim of minimizing indicators associated with the passenger service imbalance and train loading capacity utilization. Considering train regulation constraints and passenger loading dynamics, a mixed-integer linear programming model is formulated. Based on the linear weighting technique, an iterative heuristic algorithm combining the tabu search and Gurobi solver is designed to solve the proposed model. Finally, a simple case with different-scale instances is used to verify that the proposed algorithm can obtain near-optimal solution efficiently. Moreover, a real-world case of Beijing Subway Batong Line is implemented to compare performances of the proposed approach with those under the original timetable and noncollaborative passenger flow control.

scholarly journals Multiperiod Transfer Synchronization for Cross-Platform Transfer in an Urban Rail Transit System

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1665
Author(s):  
Nan Cao ◽  
Tao Tang ◽  
Chunhai Gao
Keyword(s):  
Waiting Time ◽  
Transfer Problem ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Multiple Time ◽  
Transit System ◽  
Passenger Demand ◽  
Urban Rail ◽  
Cross Platform

Transfer synchronization is an important issue in timetable scheduling for an urban rail transit system, especially a cross-platform transfer. In this paper, we aim to optimize the performance of transfer throughout the daily operation of an urban rail transit system. The daily operation is divided into multiple time periods and each time period has a specific headway to fulfill time varied passenger demand. At the same time, the turn-back process of trains should also be considered for a real operation. Therefore, our work enhances the base of the transfer synchronization model taking into account time-dependent passenger demand and utilization of trains. A mixed integer programming model is developed to obtain an optimal timetable, providing a smooth transfer for cross-transfer platform and minimizing the transfer waiting time for all transfer passengers from different directions with consideration of timetable symmetry. By adjusting the departure time of trains based on a predetermined timetable, this transfer optimization model is solved through a genetic algorithm. The proposed model and algorithm are utilized for a real transfer problem in Beijing and the results demonstrate a significant reduction in transfer waiting time.

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