Journal of Rail Transport Planning & Management最新文献

Over time, rail's share of the freight market has steadily decreased and, currently, is significantly lower than that of the road. This study explores what fosters shippers' rail dispreference. The study is conducted in the domain of outbound logistics in the steel-making industry in India. Twenty-one industry experts are interviewed in-depth to capture their perceptions, and their responses are analysed. Of these, seven are industry experts, and the remaining fourteen are logistics managers working across two steel plants, among which the annual output of one is about ten times that of the other. We find that a capacity shortage in the rail sector and the monopoly position of the rail transport provider together foster multiple factors that drive shippers' rail dispreference. Further, shipper firm size moderates the influence of some of these factors, influencing shippers' rail dispreference to a lesser extent in the larger firms than in the smaller ones. The study highlights the realization that while increasing rail capacity is necessary, it is not enough by itself, but must be complemented by targeted policy changes. The study brings to the forefront the roles played by rail capacity shortage, rail monopoly position, and shipper firm size in shippers' rail dispreference.

Railway operations may experience delays due to technical issues or weather conditions. Accurate prediction of such delays can enhance the quality of rail transport services and the effectiveness of railway operations. The study has developed the arrival delay prediction model using random forest regression based on the train operation data from the Ankara - Eskişehir high-speed train line in Turkey. The model can simultaneously predict arrival delays at all downstream stations on this line and continuously update these predictions as new information about train movements becomes available. The accuracy rates of the model vary from 76% to 99% under a 1-min prediction error. The results show that incorporating variables related to weather conditions and technical problems related to train control systems into the model improves prediction performance. The contribution of these variables to the model performance increases as the prediction horizon widens. The model results suggest that the model predictions may assist network managers in making better decisions about train operations. In order to evaluate the model's performance from the passengers' point of view, the study has proposed two methods: the proportion of late predictions and the stability of forecasts. The findings indicate that most trains (between 96.7% and 99%) have stable arrival delay predictions at target stations. The proportion of 2-min (or greater) late predictions, which means that the predicted delay exceeds the actual delay by 2 min or more, fluctuates from 14% to 0.5%, depending on the prediction horizon. Although the ratio for the short horizons (one station ahead) becomes relatively low, it is necessary to be cautious when using the model predictions to inform passengers because a prediction of more than 1 min late for short horizons might have negative consequences (e.g., misleading passengers to leave stations).

This paper evaluates the capacity effect of ERTMS/ETCS Hybrid Level 3 (HL3, also known as Hybrid Train Detection, HTD) on a conceptual level by looking at a scenario with two trains and on a network level. Key performance indicators help evaluate the results of implementing HL3: headway for the conceptual model and capacity utilization and punctuality for the network level. The study uses the simulation tool RailSys for both levels. A case study on the interaction between two trains examines how various lengths of virtual blocks affect the performance indicator headway. The network scale simulations use a real-world infrastructure and a complete timetable. Two cases examine how the performance indicators capacity utilization and punctuality are affected by the share of Level 2 and Level 3 trains in a HL3 system. Results from the conceptual two-train interaction show that HL3 slightly improves the headway, but it is similar for varying virtual block lengths. The results from the network model indicate the share of Level 2 and Level 3 trains has minimal effect on punctuality and capacity utilization. However, we identified some factors influencing the HL3 capacity evaluation, like stations and switches on the line, that affect the potential capacity gains.

Study on the platoon planning of virtual coupling (VC) is indispensable for both passenger and freight railway operation in terms of capacity, scheduling, and timetabling. As an advanced technology in train operation and control systems, VC can significantly improve the performance of rail operations, with regards to capacity, flexibility and robustness. In this study, we mainly focus on the former one, i.e., to maximize the capacity gains, by employing the relative braking distance based spacing policy for heterogenous train traffic. To our knowledge, this article is the first to address the VC intra-platoon planning problem for a railway network. The main function/purpose of this study is to decide a leader train and allocate an order to the heterogeneous follower trains for the virtually coupled platoon in the off-line mode. The objective is transformed to minimize and compare the sum of minimum safe distance for platoons on the common shared route sections in the railway network from various perspectives, so as to seek the desirable orders of potential train consists within the platoons from the perspective of the whole railway network. Our contributions are mainly threefold. First, the conditions for train platooning under virtual coupling are synthesized. Second, the solution flow charts and algorithms for simulation are proposed, including general framework for simulation, classification of prioritizing the shared common route section, and flow charts and algorithms for decision of coupling order to train platoons. Finally, the proposed methodology was tested and discussed on the designed computational experiment (i.e., numerical simulations of a typical case study with two sets of test examples) via NetLogo platform. The numerical simulation results series revealed some key findings and validated the effectiveness of the proposed methodology. Results of this study can provide guidance to decision makers in timetabling and potential capacity bottleneck identification under VC. We believe that it is worth investigating and further advancing this research direction in the future.

A key strategy for increasing railway capacity is utilizing infrastructure more efficiently. While much research has been completed on methods for assessing railway capacity, very little has focused on the details of capacity utilisation, such as assessing the various ways trains use capacity, the impacts of specific blocking time components, and how train dynamics (accelerating, cruising, braking, and dwelling) affect capacity.

This paper presents a methodology for comparing planned occupancy to actual occupancy under real operations and applies it in a case study. The methodology is based on identifying a critical path which represents an extension of bottleneck concept presented in UIC leaflet 406. The methodology was applied in a case study to determine the specific blocking times and train dynamics which cause a blocking time gap for a sequence of trains, both a-priori and a-posteriori, after considering the operational variations. The analysis of real operations with variations in train trajectories shows that capacity occupation is mostly influenced by train sequence heterogeneity in the original schedule. The varying effects of operations have a smaller but relevant impact. The methods developed in this paper can be used to help assess railway capacity under real operations.

A foreseeable challenge with a substantial increase in railway mode share will be how to uphold punctuality. Higher volumes of train traffic will result in timetables that are more sensitive to disruptions; whose severity and frequency is also expected to increase in light of greater asset utilization and climate change. This calls for a definitive understanding of the relationship between incidents and train delays as a prerequisite to developing robust timetables and disruption management strategies. In this paper we propose a novel framework for quantifying the impact of railway incidents on train delays. Using a case of the Swedish Railway Network, we compare the impact of different incidents on train delays. The impact of delay is defined as a factor of the incident rate, exposure rate, delay rate and historical average delay minutes per incident. A logistic model that estimates the probability of delay for any train, in the event of a failure, is also developed. Snow on track was established as most critical, resulting in the highest normalized delay minutes per train and the largest increase in the odds of delay for individual trains. The proposed framework & approach can be applied to other networks.

Energy and environmental sustainability in transportation are becoming ever more important. In Brazil, the system electric traction represents the largest consumption of electric energy in the subway system. Electrified railway systems play an important role in contributing to the reduction of energy usage and CO2 emissions compared with other transport modes. For subway transit systems with frequently cycles of departures and braking of trains, the effective use of regenerative braking energy is a significant way to reduce the net energy consumption. The recovery of regenerative energy produced by braking trains of a subway system is essential to increase its energy efficiency, however difficult to apply in the São Paulo subway due to the short headway between train. This paper proposes evaluate through traction power network modeling and the computer simulation the application and feasibility of using inverter substation (ISS) to maximize the recovery of regenerative braking energy. The results of the study of Line 1 - Blue of the São Paulo’s metro city indicate that using inverter substation could reduce the energy consumption at the rectifier substation (RSS) by nearly 12,94% compared to the system without the inverter substation.

We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.

International rail transport plays an essential role in the supply chain. Eurasian rail transport has been a successful example since its launch in 2011 and has increased over the last decade. However, a crucial question arises: Does this intercontinental rail service fulfill the transport requirements of the supply chain, and can it be quantified? Cost, reliability, transport time, and flexibility are essential factors in selecting transport modes in the supply chain. Current performance parameters mainly focus on efficiency, which may conflict with supply chain performance requirements. Before this research, it was difficult to quantify the international rail services; the frequently used tonne-based, volume-based (TEU), number of block trains, loaded, or empty container measurement forms cannot adequately capture the requirements of the global supply chain. Our literature review highlighted the need for standardized service attributes in international rail transport. Additionally, we identified a misalignment between rail performance and the demands of the global supply chain. This misalignment could impede the integration of international rail transport into the global supply chain. This paper contributes by developing universally applicable SCOR-Rail Key Performance Indicators (KPIs) for international rail transport based on the Supply-chain Operations Reference (SCOR) model. It bridges the measurement gap between the supply chain and international rail transport, giving railway companies a tool to design and monitor their services. They could also be easily integrated into rail planning for future research. Our initial assessment based on the proposed measurement framework revealed that Eurasian rail transport has the potential for improvement.

Planned maintenance and unplanned incidents cause service disruptions in freight railway classification yards, creating congestion, delaying railcars, and even impacting mainline operations. Understanding the recovery time and lingering performance impacts of yard disruptions is vital for the industry to plan disruption responses, promote efficient resource utilization, and improve resiliency. This paper compares two major types of yard disruptions (temporary closures of hump process and pulldown process) and quantifies the recovery pattern, measured by multiple performance metrics. The authors propose an analytical approach for estimating classification yard recovery time as a function of disruption duration and baseline capacity utilization. To validate the hypothetical approach, a series of experiments are conducted across a wide range of disruption durations and throughput volumes in a representative hump classification yard simulation model constructed using AnyLogic. The results indicate that recovery time is proportional to shutdown duration with a near constant recovery rate, and recovery rate increases approximately exponentially with throughput volume. These results are consistent with the hypothesized analytical relationships, suggesting that yard capacity may be estimated from disruption recovery rate. The methodology developed also enables future studies on interactions between yards and mainlines and developing planning-level parametric models of classification yard capacity and performance.