scholarly journals Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhipeng Huang ◽  
Huimin Niu ◽  
Ruhu Gao ◽  
Haoyu Fan ◽  
Chenglin Liu
Keyword(s):  
Bilevel Programming ◽  
High Speed ◽  
Service Providers ◽  
Programming Model ◽  
Operating Cost ◽  
User Equilibrium ◽  
Optimal Operation ◽  
High Speed Railway ◽  
Train Timetable ◽  
Bilevel Programming Model

Passengers would like to choose the most suitable train based on their travel preferences, expenses, and train timetable in the high-speed railway corridor. Meanwhile, the railway department will constantly adjust the train timetable according to the distribution of passenger flows during a day to achieve the optimal operation cost and energy consumption saving plan. The question is how to meet the differential travel needs of passengers and achieve sustainable goals of service providers. Therefore, it is necessary to design a demand-oriented and environment-friendly high-speed railway timetable. This paper formulates the optimization of train timetable for a given high-speed railway corridor, which is based on the interests of both passengers and transportation department. In particular, a traveling time-space network with virtual departure arc is constructed to analyze generalized travel costs of passengers of each origin-destination (OD), and bilevel programming model is used to optimize the problem. The upper integer programming model regards the minimization of the operating cost, which is simplified to the minimum traveling time of total trains, as the goal. The lower level is a user equilibrium model which arranges each OD passenger flow to different trains. A general advanced metaheuristic algorithm embedded with the Frank–Wolfe method is designed to implement the bilevel programming model. Finally, a real-world numerical experiment is conducted to verify the effectiveness of both the model and the algorithm.

scholarly journals Simultaneous Optimization of Train Timetabling and Platforming Problems for High-Speed Multiline Railway Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Qin Zhang ◽  
Xiaoning Zhu ◽  
Li Wang ◽  
Shuai Wang
Keyword(s):  
High Speed ◽  
Optimization Problems ◽  
Programming Model ◽  
Simultaneous Optimization ◽  
Resource Assignment ◽  
Railway Network ◽  
High Speed Railway ◽  
Train Timetabling ◽  
Train Timetable ◽  
Railway Operation

The optimization problems of train timetabling and platforming are two crucial problems in high-speed railway operation; these problems are typically considered sequentially and independently. With the construction of high-speed railways, an increasing number of interactions between trains on multiple lines have led to resource assignment difficulties at hub stations. To coordinate station resources for multiline train timetables, this study fully considered the resources of track segments, station throat areas, and platforms to design a three-part space-time (TPST) framework from a mesoscopic perspective to generate a train timetable and station track assignment simultaneously. A 0-1 integer programming model is proposed, whose objective is to minimize the total weighted train running costs. The construction of a set of incompatible vertexes and links facilitates the expression of difficult constraints. Finally, example results verify the validity and practicability of our proposed method, which can generate conflict-free train timetables with a station track allocation plan for multiple railway lines at the same time.

scholarly journals A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ozgur Baskan ◽  
Huseyin Ceylan ◽  
Cenk Ozan
Keyword(s):  
Bilevel Programming ◽  
Road Network ◽  
Programming Model ◽  
Numerical Test ◽  
User Equilibrium ◽  
Reserve Capacity ◽  
Delay Minimization ◽  
Weighted Sum Method ◽  
Capacity Maximization ◽  
Bilevel Programming Model

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

scholarly journals The Shrinking of Beijing and the Rising of Xiong’an: Optimize Population Migration in terms of Transport Service

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hongzhi Lin
Keyword(s):  
Bilevel Programming ◽  
Central Government ◽  
Programming Model ◽  
Service Level ◽  
User Equilibrium ◽  
Population Migration ◽  
Transport Service ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

The population of Beijing has already come to its loading capacity. The China central government plans to build an ideal city named Xiong’an nearby Beijing. The city is expected to work as a carrying hub for noncapital functions of Beijing. The central government does not rush to build before a deliberated urban planning is accomplished. For sustainable development, a difficulty faced by urban planners is that the maximum number of people can be migrated from Beijing to Xiong’an with constraint on level of transport service. This paper developed a specialized bilevel programming model where the upper level is to ensure a predetermined transport service level regarding to population migration, while the lower level is feedback equilibrium between trip generation and traffic assignment. To be more specific, trip is generated by the gravity model, and traffic is assigned by the user equilibrium model. It is well known that the bilevel programming problem is tough and challenging. A try-and-error algorithm is designed for the upper-level model, and a method of successive average (MSA) is developed for the lower-level model. The effectiveness of the model and algorithm is validated by an experimental study using the current transport network between Beijing and Xiong’an. It shows that the methods can be very useful to identify the maximum population migration subject to level of transport service.

scholarly journals High-occupancy Vehicle Lanes and Tradable Credits Scheme for Traffic Congestion Management: A Bilevel Programming Approach

2018 ◽  
Vol 30 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Guangzhi Zang ◽  
Meng Xu ◽  
Ziyou Gao
Keyword(s):  
Bilevel Programming ◽  
Traffic Congestion ◽  
Programming Model ◽  
High Capacity ◽  
Congestion Management ◽  
User Equilibrium ◽  
Programming Approach ◽  
Tradable Credits ◽  
Bilevel Programming Model ◽  
Hov Lanes

High-occupancy vehicle (HOV) lanes, which are designed so as to encourage more people to use high-capacity travel modes and thus move more people in a single roadway lane, have been implemented as a lane management measure to deal with the growing traffic congestion in practice. However, the implementation has shown that some HOV lanes are not able to achieve the expected effects without proper HOV lane settings. In this study, the tradable credits scheme (TCS) is introduced to improve the HOV lane management and an optimal capacity of HOV lanes in a multilane highway is investigated to match TCSs. To approach the investigation, a bilevel programming model is proposed. The upper-level represents the decision of the highway authority and the lower-level follows the commuters’ user equilibrium with deterministic demand. The potential influence of TCSs is further investigated within the proposed framework. A modified genetic algorithm is proposed to solve the bilevel programming model. Numerical examples demonstrate that combining TCSs with the HOV lane management can obviously mitigate traffic congestion.

scholarly journals A Bilevel Programming Model to Optimize Train Operation Based on Satisfaction for an Intercity Rail Line

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhipeng Huang ◽  
Huimin Niu
Keyword(s):  
Bilevel Programming ◽  
Programming Model ◽  
Lower Layer ◽  
Flow Distribution ◽  
User Equilibrium ◽  
Passenger Travel ◽  
Train Operation ◽  
Rail Line ◽  
Bilevel Programming Model ◽  
Model Features

The passenger travel demands for intercity rail lines fluctuate obviously during different time periods, which makes the rail departments unable to establish an even train operation scheme. This paper considers an optimization problem for train operations which respond to passenger travel demands of different periods in intercity rail lines. A satisfactory function of passenger travelling is proposed by means of analyzing the passengers’ travel choice behavior and correlative influencing factors. On this basis, the paper formulates a bilevel programming model which maximizes interests of railway enterprises and travelling satisfaction of each passenger. The trains operation in different periods can be optimized through upper layer planning of the model, while considering the passenger flow distribution problem based on the Wardrop user equilibrium principle in the lower layer planning. Then, a genetic algorithm is designed according to model features for solving the upper laying. The Frank-Wolfe algorithm is used for solving the lower layer planning. Finally, a numerical example is provided to demonstrate the application of the method proposed in this paper.

scholarly journals Networked Timetable Stability Improvement Based on a Bilevel Optimization Programming Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xuelei Meng ◽  
Bingmou Cui ◽  
Limin Jia ◽  
Yong Qin ◽  
Jie Xu
Keyword(s):  
Bilevel Programming ◽  
Programming Model ◽  
Bilevel Optimization ◽  
Particle Swarm Algorithm ◽  
Index Number ◽  
Train Timetable ◽  
Operating Rules ◽  
Buffer Time ◽  
The Status ◽  
Bilevel Programming Model

Train timetable stability is the possibility to recover the status of the trains to serve as arranged according to the original timetable when the trains are disturbed. To improve the train timetable stability from the network perspective, the bilevel programming model is constructed, in which the upper level programming is to optimize the timetable stability on the network level and the lower is to improve the timetable stability on the dispatching railway segments. Timetable stability on the network level is defined with the variances of the utilization coefficients of the section capacity and station capacity. Weights of stations and sections are decided by the capacity index number and the degrees. The lower level programming focuses on the buffer time distribution plan of the trains operating on the sections and stations, taking the operating rules of the trains as constraints. A novel particle swarm algorithm is proposed and designed for the bilevel programming model. The computing case proves the feasibility of the model and the efficiency of the algorithm. The method outlined in this paper can be embedded in the networked train operation dispatching system.

scholarly journals An Efficient Hybrid Approach for Scheduling the Train Timetable for the Longer Distance High-Speed Railway

2021 ◽  
Vol 13 (5) ◽  
pp. 2538
Author(s):  
Zeyu Wang ◽  
Leishan Zhou ◽  
Bin Guo ◽  
Xing Chen ◽  
Hanxiao Zhou
Keyword(s):  
Real World ◽  
High Speed ◽  
Programming Model ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Railway Network ◽  
High Speed Railway ◽  
Train Timetable ◽  
Passenger Travel ◽  
Periodic Model

Compared with other modes of transportation, a high-speed railway has energy saving advantages; it is environmentally friendly, safe, and convenient for large capacity transportation between cities. With the expansion of the high-speed railway network, the operation of high-speed railways needs to be improved urgently. In this paper, a hybrid approach for quickly solving the timetable of high-speed railways, inspired by the periodic model and the aperiodic model, is proposed. A space–time decomposition method is proposed to convert the complex passenger travel demands into service plans and decompose the original problem into several sub-problems, to reduce the solving complexity. An integer programming model is proposed for the sub-problems, and then solved in parallel with CPLEX. After that, a local search algorithm is designed to combine the timetables of different periods, considering the safety operation constraints. The hybrid approach is tested on a real-world case study, based on the Beijing–Shanghai high-speed railway (HSR), and the results show that the train timetable calculated by the approach is superior to the real-world timetable in many indexes. The hybrid approach combines the advantages of the periodic model and the aperiodic model; it can deal with the travel demands of passengers well and the solving speed is fast. It provides the possibility for flexible adjustment of a timetable and timely response to the change of passenger travel demands, to avoid the waste of transportation resources and achieve sustainable development.

scholarly journals A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network

2020 ◽  
Vol 12 (3) ◽  
pp. 1131
Author(s):  
Wenliang Zhou ◽  
Xiaorong You ◽  
Wenzhuang Fan
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
High Speed ◽  
Programming Model ◽  
Mixed Integer ◽  
High Speed Rail ◽  
Train Timetabling ◽  
Train Timetable ◽  
The Impact

To avoid conflicts among trains at stations and provide passengers with a periodic train timetable to improve service level, this paper mainly focuses on the problem of multi-periodic train timetabling and routing by optimizing the routes of trains at stations and their entering time and leaving time on each chosen arrival–departure track at each visited station. Based on the constructed directed graph, including unidirectional and bidirectional tracks at stations and in sections, a mixed integer linear programming model with the goal of minimizing the total travel time of trains is formulated. Then, a strategy is introduced to reduce the number of constraints for improving the solved efficiency of the model. Finally, the performance, stability and practicability of the proposed method, as well as the impact of some main factors on the model are analyzed by numerous instances on both a constructed railway network and Guang-Zhu inter-city railway; they are solved using the commercial solver WebSphere ILOG CPLEX (International Business Machines Corporation, New York, NY, USA). Experimental results show that integrating multi-periodic train timetabling and routing can be conducive to improving the quality of a train timetable. Hence, good economic and social benefits for high-speed rail can be achieved, thus, further contributing to the sustained development of both high-speed railway systems and society.

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