Assessment of the tradeoff between energy efficiency and transfer opportunities in an urban rail transit network

Urban rail transit (URT) in metropolitan areas consumes huge energy. Energy-efficient timetabling (EET) of URT is an essential measurement of URT management and technologies toward carbon neutralization initiatives. However, the majority EET studies focus on single URT lines ignoring passenger transfer and path choice in the entire URT network. As passenger path choice and timetabling are interdependent in a URT network, the ignorance of passenger transfers potentially results in irrelevant energy efficiency of a URT network. This paper proposes a bi-objective EET model incorporating the minimization of passenger transfer times as an objective in addition to energy efficiency. The timetabling objectives and constraints are linearized, and the bi-objective is transformed into a single objective by a linear weighting method. Utilizing the passenger demand and speed profile data of URT in the City of Xi'an (China), a case study is performed to demonstrate the effectiveness of the proposed EET model. The numerical results show that an optimized timetable solution can reduce 25.1% energy consumption and save 3.3% passenger transfer time.</p

A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”

Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail

An intelligent framework and prototype for autonomous maintenance planning in the rail industry

This paper details the development of the AUTONOM project, a project that aims to provide an enterprise system tailored to the planning needs of the rail industry. AUTONOM extends research in novel sensing, scheduling, and decision-making strategies customised for the automated planning of maintenance activities within the rail industry. This paper sets out a framework and software prototype and details the current progress of the project. In the continuation of the AUTONOM project it is anticipated that the combination of techniques brought together in this work will be capable of addressing a wider range of problem types, offered by Network rail and organisations in different industries

An approximate dynamic programming approach for designing train timetables

Traditional approaches to solving the train timetabling problem—the optimal allocation of when each train arrives and departs each station—have relied on Mixed-Integer Programming (MIP) approaches. We propose an alternative formulation for this problem based on the modeling and algorithmic framework of approximate dynamic programming. We present a Q-learning algorithm in order to tractably solve the high-dimensional problem. We compare the performance of several variants of this approach, including discretizing the state and the action spaces, and continuous function approximation with global basis functions. We demonstrate the algorithms on two railway system cases, one minimizing energy consumption subject to punctuality constraints, and one maximizing capacity subject to safety constraints. We demonstrate that the ADP algorithm converges rapidly to an optimal solution, and that the number of iterations required increases linearly in the size of the rail system, in contrast with MIP approaches whose computation time grows exponentially. We also show that an additional benefit to the ADP approach is the intuition gained from visualizing the Q-factor functions, which graphically capture the intuitive tradeoffs between efficiency and constraints in both examples

An optimization model for line planning and timetabling in automated urban metro subway networks

In this paper we present a Mixed Integer Nonlinear Programming model that we developed as part of a pilot study requested by the R&D company Metrolab in order to design tools for finding solutions for line planning and timetable situations in automated urban metro subway networks. Our model incorporates important factors in public transportation systems from both, a cost-oriented and a passenger-oriented perspective, as time-dependent demands, interchange stations, short-turns and technical features of the trains in use. The incoming flows of passengers are modeled by means of piecewise linear demand functions which are parameterized in terms of arrival rates and bulk arrivals. Decisions about frequencies, train capacities, short-turning and timetables for a given planning horizon are jointly integrated to be optimized in our model. Finally, a novel Math-Heuristic approach is proposed to solve the problem. The results of extensive computational experiments are reported to show its applicability and effectiveness to handle real-world subway networksComment: 30 pages, 6 figures, 9 table

A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem

Trains move along the railway infrastructure according to specific timetables. The timetables are based on the running time calculation and they are usually calculated without considering explicitly energy consumption. Since green transportation is becoming more and more important from environmental perspectives, energy consumption minimization could be considered also in timetable calculation. In particular, the Energy-Efficient Train Timetabling Problem (EETTP) consists in the energy-efficient timetable calculation considering the trade-off between energy efficiency and running times. In this work, a solution approach to solve a multi-objective EETTP is described in which the two objectives are the minimization of both energy consumption and the total travel time. The approach finds the schedules to guarantee that the train speed profiles minimize the objectives. It is based on modeFRONTIER and OpenTrack that are integrated by using the OpenTrack Application Programming Interface in a modeFRONTIER workflow. In particular, the optimization is made by modeFRONTIER, while the calculation of the train speed profiles, energy consumption and total travel time is made by OpenTrack. The approach is used with Multi-objective Genetic Algorithm-II and the Non-dominating Sorting Genetic-II, which are two genetic algorithms available in modeFRONTIER. The solution approach is tested on a case study that represents a real situation of metro line in Turkey. For both algorithms, a Pareto Front of solution which are a good trade-off between the objectives are reported. The results show significant reduction of both energy consumption and total travel time with respect to the existing timetable

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown