Journal of Rail Transport Planning & Management最新文献

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.

Applying the surplus capacity of the metro system to carry out urban logistics distribution can efficiently ease traffic congestion and improve the efficiency of logistics distribution in an urban area. Taking the mode of setting up freight carriages at both ends of passenger trains as the research target, a collaborative transport model combining passengers and goods is developed to quantitatively analyze the actual performance of the logistics function in the metro system while making assumptions about uncertain factors. The method is implemented in a simulated analysis on the basis of the Chengdu metro network and current freight demand. Results show that the collaborative transport mode can meet 98.95% of the freight demand and alleviate 98.86% of the traffic congestion. Meanwhile, the average delivery time of collaborative transportation is 31.07 min, which is less than the 40.98 min delivery time of ground transportation, indicating that the former is more efficient. Moreover, the collaborative transportation model is more sensitive to changes in passenger flow than changes in freight demand. Therefore, separating the freight and passenger transport functions of the metro system will effectively reduce the impact of passenger flow changes on collaborative transportation.

Railway maintenance works are performed to reduce the probability of the occurrence of a failure on the components of the railway infrastructure. The scheduling of maintenance work is important in supporting the normal daily operation of the railway. By proper maintenance scheduling, operational hindrance in terms of extra travel costs due to detouring can be minimized. In addition, contractors can be given more space to execute maintenance activities with more flexibility. This study aims to optimize the maintenance schedules for both train operators and maintenance contractors by considering hindrance and flexibility for both stakeholders, respectively. This study tries to achieve this by modeling important factors contributing to both objectives and relevant constraints in a multi-objective optimization problem. The methods presented for solving the multi-objective model are the $\varepsilon$-constraint method and NSGA-II. Two path finding algorithms are modified to consider train travel limitations and are used to support the solutions methods. Both solution strategies are initially tested on fictive networks to analyze the performance. In a case study, the Dutch railway network is assessed and used to create new maintenance schedules based on the new model.

Capacity analysis is of central importance in railway operation. Existing methods divide the infrastructure of question into smaller sections when computing the consumed capacity, which makes them nontransferable for real-world operation. We first review and enhance the UIC compression method, which results in a combination–reconstruction (ComRec) method to compute the compressed timetable graph of the whole infrastructure. Secondly, we propose a triangular-gap-problem-based (TGP) method to compute the headway times of train pairs when no more than one train lies within the separation gap of two trains. Then we show TGP method produces an compressed timetable graph equivalent to that by the ComRec method. Max-plus algebra approach determines the consumed capacity by solving an eigenvalue problem, and the solution corresponds to a timed event network as the compressed timetable. And by their correspondence, we show that these three methods are equivalent. Finally, we establish correspondences between the capacity methods and linear programming models. In this way, we were able to specify the condition when they give the same result and how infrastructure dividing underestimates capacity.

Providing a comprehensive overview of the safety status of similar large-scale rail spatial units (such as existing Rail Regions (RRs) in a country) is a first but very important step in identifying policies that can accelerate progress in improving rail safety. The aim of the current study is to present a new approach to assess RR's safety performance with an emphasis on the Resources and Equipment (R&E) of each RR. To reach the study goal, first, a conceptual framework is developed to make a relationship among exposure, safety, and R&E of RRs. In the next step, Data Envelopment Analysis (DEA) model with Fuzzy-Delphi method is used to evaluate the safety performance of different RRs. In this evaluation, the data regarding safety status of 20 RRs of Iran in 2020 are used. Results showed that Region #1 has the best safety efficiency and Northeast Region #20 has the lowest safety efficiency among other regions. It was also revealed how much of each RR's resources (e.g. track repairmen and maintenance crews, etc.) are using inefficiently. Moreover, some suggestions for improving safety performance of each region were also presented based on the amount of resources using inefficiently. By considering R&E of each region in the proposed approach, a more impartial comparison can be made on the RR's safety performance. Therefore, the decision maker could have a more realistic and fairer view of the safety status of different RRs. Finally, it is worth mentioning that previous studies generally aimed at assessing RRs' safety based on their safety level without involving R&E of each RR. However, it seems an incomplete assessment considering the fact that different RRs have different amount of R&E. Thus, the current study is trying to fill this gap by taking into account R&E of each RR.

Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by $R^2$ of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.