Journal of Rail Transport Planning & Management最新文献

Energy-saving driving is crucial during railway operation, especially in case of disturbances that require timetable rescheduling. This paper presents a method for the dynamic onboard tuning of energy-efficient speed profiles after the real-time train rescheduling process under a fixed block signaling system for mixed traffic. Similar to the idea of connected driver advisory systems, trains constantly communicate with a central traffic management system. After the rescheduling process, this system provides recommended time corridors for the passing of block signals that depend on the predicted clearing times of the block sections. These recommendations are then used for individual energy optimization of single train runs by avoiding unnecessary braking in front of block signals and maximizing cruising distances. The method is tested and evaluated on a representative line segment of the ELVA, the Railway Signaling Lab at RWTH Aachen University under realistic conditions. Energy consumptions are compared for different prediction time horizons at which the recommendations are available to the train's onboard system. In the examined test case of two trains, the energy-consumption could be decreased by up to 53% compared to operation without any rescheduling system. Thus, the proposed method is able to reduce energy consumption significantly.

This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.

Traffic management is crucial for improving the punctuality and reliability of train operations, enabling train operating companies (TOCs) to maintain their competitiveness and further increases the share and profits. A common goal of the train rescheduling problem is to minimize train delays, which fails to examine the results from the perspective of passengers. Moreover, focusing only on the punctuality performance overlooks how the delay is distributed among entities (i.e., trains, passengers, and train operating companies).

We study the train rescheduling problem with the inclusion of passenger choices and the equity concerns. A mixed-integer linear programming (MILP) model is proposed to find the optimal train schedules and the best route for passengers at the same time, with respect to the demanded equity level. Passengers choose a sequence of train services to complete their trip with the least amount of costs (i.e., delays). To evaluate the equity performance of the system, we define equity by means of Gini Coefficient and Maximal Deviation, included in the MILP model as constraints.

Experiments are conducted to explore the impacts of the objective change, i.e., from reducing train delays to reducing passenger delays, and to compare the system performance of using the two equity measures in terms of punctuality and equity. According to the results, the average passenger delay decreases by 34% when minimizing passenger delays, compared with that of minimizing train delays. Moreover, the Gini Coefficient yields less cost of equity (i.e., less increase of delays), compared to that of the Maximal Deviation.

Railway freight wagons have a significant share in the total capital assets of a railway freight company. Due to this fact, the main objective of every company is to maximize the utilization of these resources and on that way minimize its size. In this paper we consider a real-world freight wagon fleet management problem and propose a decomposition approach for optimization of the heterogeneous fleet of flat wagons. The approach has four steps: random container weight generation, optimal container to wagon assignment, empty wagon repositioning and optimal wagon fleet sizing. For the purpose of validation, real-life experiments were conducted based on a rail network composed of 911 origin-destination links and the yearly demand of more than 3 × 10⁶ empty and loaded 20-foot and 40-foot containers. Experimental results show that proposed approach has a practical applicability and that in comparison with existing experience-based practice it represents a significant improvement for the flat wagon fleet management.

Classification yards represent network nodes in the single-wagonload transport system. The processes are complex due to a high number of involved resources and restrictive dependencies. Decisions on job sequencing and resource allocation have a major impact on outbound delays and thus on the quality of service in the network. Due to permanent updates of arrival times and resource availabilities, a constant revision of decisions is necessary. In many cases, considering multiple qualifications of the personnel is crucial for efficient operations. This paper presents an approach for the rescheduling of processes and the assignment of resources in classification yards, which allows to determine best working schedules based on current data such that the cumulative outbound delay of all trains is minimized. Therefore, the paper presents a mixed integer program that includes all essential components (tracks, locomotives and personnel with individual skill patterns). For the real-time capable solution of the optimization problem, four different heuristic approaches based on priority rules are presented. The performance of these approaches is evaluated by a gap analysis with respect to the solutions found by CPLEX. For this purpose, real example data of an operation day of a large classification yard in Germany are used.

Using the output of optimisation models to make real-time changes to railway timetables can be an effective way to reduce the propagation of delay. In this study, we develop a methodology for evaluating the fairness of such optimisation models with respect to competing train operators. Whilst both fairness and optimisation-based railway timetable rescheduling have both been widely studied, they have not previously been studied together. We propose definitions of fairness and efficiency for timetable rescheduling, and analyse the fairness of efficiency-maximising solutions for a case study with seven train operators. We also investigate the pairwise trade-offs between operators and show that the priority given to different train classes has an important impact on fairness.

Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy-efficient train control, which can reduce operating costs and contribute to a reduction in CO${}_2$ emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To our knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. Our results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.

In 2018, China's State Council proposed a 30% increase in railway freight volume (RFV) to 4.79 billion tons in 2020 over 2017. Subsequently, more than 30 provinces and cities in China have issued corresponding transportation structure adjustment plans, but the completion of this task has not been very smooth. The growth rate in 2019 is slower than that in 2018, and the incremental task in 2020 still remains 42.7%. China's railway freight transportation capacity (RFTC) used to be in short supply for a long time, which has only eased in recent years. In order to explore the adaptation of China's current RFTC and incremental targets, and fully tap RFTC potential to formulate reasonable freight increment policies in the future, this article combines the simultaneous production and consumption feature of transportation sector and SBM-GRS (slack based measure-general returns to scale) data envelopment analysis to measure China's RFTC surplus space. The study found that from the supply side the incremental potential of China's railway freight turnover (RFT) is greater than that of RFV, which is caused by the imbalance of regional railway freight transportation. If the current RFV goal was replaced by RFT, RFTC input would save about 3%. This article suggests that China's future railway freight increment policy should take into account the regional imbalance of bulk cargo transportation, pay more attention to the growth of RFT, actively take advantage of railway container long-distance transportation, and make full use of overall RFTC.

The objective of the present work is to demonstrate a methodology for assessment of passenger train reliability from the perspective of a KPI to measure the number of passengers whose travel time has been affected by delays. Based on this indicator, a workflow is proposed to select critical train subsystems and analyze their reliability using the probability distribution that best suits the available failure data. The proposed methodology was applied to the train fleet of the Rio de Janeiro Metro Concession, who was responsible for about 20% of the occurrences that affected the “passengers delayed” indicator in the period from May to December 2019, to evaluate the reliability of the doors subsystem, which was the most critical in terms of failures that affected passenger travel time in 2019 (145 failures). The results demonstrated a 66% drop in the subsystem reliability in five years, ratifying the feasibility and effectiveness of the methodology. The originality of this article is a result of the innovative proposal of a methodology to manage critical assets and systems by evaluating the effect of their failures on quality-of-service attributes valued by railway customers.

In the rail network, providing empty railcars and locomotives at the origin stations of trains and dynamic train formation planning according to the schedule is essential. In the present study, the simultaneous allocation of railcars and locomotives to plan train formation was accomplished according to the schedule. A Mixed Integer Linear Programming (MILP) mathematical model has been developed, with the aim of maximizing the profits of the railway company resulting from customer demand satisfaction by freight trains in the rail network. In this mathematical model, in addition to the simultaneous railcars and locomotives allocation to trains, issues such as the capacity of train stations, the traction of locomotives, cancellation of trains, and active and deadhead consist of locomotives are considered. The Iran railways network was selected as a real-world case study to evaluate the proposed model. As the results show, purchasing a particular combination of railcars and locomotives in the current and future demand situations achieved the greatest increase in the demand satisfaction rate and railway company profit as well in the rail network, and also the productivity indicators of railcars and locomotives were improved. Moreover, the best-case scenario was selected based on the best combination offered for the fleet in the current and future demand situations.