Journal of Rail Transport Planning & Management最新文献

This paper presents a discrete-event model for a mass-transit line operated with a two-service skip-stop policy while allowing for train dwell times to vary according to passengers’ demand volumes. The model is formulated by two mathematical constraints on the train’s travel and safe separation times that govern the train dynamics on the line. In addition, the model takes into account trains’ dwell times, which are affected by both the services offered by the operator and passenger demand. The model is written in the max-plus algebra, a mathematical framework that allows us to derive interesting analytical results, including the fundamental diagram of the line, which describes the relationship between the average train time headway (or frequency), the number of trains running on the line and the passenger travel demand. The paper also derives indicators that are capable of quantifying and, thus, assessing the impact of a skip-stop policy on passengers’ travel. Finally, the paper compares two different passenger demand profiles. Results show that long-distance passengers mainly benefit from skip-stop policies, while short-distance travelers may experience an increase in their travel time. For long-distance passengers, the increase in the waiting time is counterbalanced by the decrease in the in-vehicle time, leading to an overall decrease in total passenger travel time.

the transport infrastructure, particularly the railway infrastructure plays a vital role in the delivery of freight and the transportation of people. The ability and reliability of the railway infrastructure to deliver goods and transport people are challenged by train derailments and collisions caused by infrastructure breakdowns. Lack of maintenance has been identified as one of the causes of infrastructure breakdowns leading to accidents. The current paper proposes that if the railway infrastructure safety, availability, capacity, and cost are modeled using system dynamics, the impact of infrastructure operation and maintenance on safety can be predicted more accurately. The paper follows systems thinking approach that aims to understand the railway infrastructure as a system, by defining the system structure, system component relationships, and system behavior. The impact on railway infrastructure is modeled using system dynamics by developing causal loop diagrams and stock and flow diagrams which define the system structure, and system component relationships, and models the system behavior of safety, availability, capacity, and cost.

The train routing selection problem (TRSP) addresses the optimized selection of alternative routes as a preliminary step for real-time railway traffic management problem (rtRTMP). In the TRSP, route selection relies on estimating potential delays resulting from scheduling decisions. The selected routes are then exclusively applied in the rtRTMP. While prior research established the mathematical model and solution algorithms for the TRSP, its practical application in real-time rail traffic management remains limited. The existing TRSP model focuses on a single objective function for the rtRTMP. However, in practice, various stakeholders may prioritize different objectives, leading to diverse objective functions employed in the rtRTMP. This paper extends the TRSP model by considering a range of suitable objectives for the rtRTMP. We formulate the TRSP for each objective function and enhance the cost estimation model to evaluate the correspondence between the TRSP and rtRTMP objective functions. We then assess the overall effectiveness of the TRSP for the rtRTMP through an evaluation that takes into account several configurations of the model and the rtRTMP solution approach used. Our purpose is to enlarge the applicability of the TRSP and enhance the efficiency of the rtRTMP for real-world systems. The paper includes an in-depth computational analysis of two French case studies to investigate the performance of the TRSP across different rtRTMP configurations.

With the liberalisation of the rail freight market, the number of railway undertakings in operation is increasing. Trains run by several railway undertakings (RUs) converge at industrial lines leading up to terminals. Here, uncoordinated interaction between mainline RUs and limited infrastructure capacity leads to bottlenecks, which reduces resource utilisation of railway undertakings. Outsourcing last-mile operations to an independent local railway undertaking can improve capacity utilisation and decrease the time engines of mainline engines spend within the considered network. In this paper, we propose the Industrial Line Scheduling Problem with multiple RU, a resource scheduling model for freight trains at industrial lines. The aim is to minimise unproductive time of mainline engines and the number of deployed local engines. The results show that the potential savings per employed local engine are highly dependent the timetables of inbound and outbound trains within the network, the dwell time of railcars and on the degree of local railway undertaking involvement.

Train passenger demand fluctuates throughout the day. In order to let train services, such as the line plan and timetable, match this fluctuating demand, insights are needed into how the demand is changing and for which periods the demand is relatively stable. Hierarchical clustering on both regular and normalized origin–destination (OD) data is used to determine for each workday continuous time-of-day periods in which the passenger demand is homogeneous. The periods found for each workday are subsequently used as input in a clustering algorithm to look for similarities and differences between workdays. The methods for finding homogeneous periods during the day and week are applied to a case study covering a large part of the railway network in the Netherlands. We find large differences between the periods based on regular OD matrices and those based on normalized OD matrices. The periods based on regular OD matrices are more compact in terms of passenger volumes and average kms travelled and therefore more suitable to use as input for designing a service plan. Comparison of different workdays shows that mainly the peak periods on Friday are far away from Monday to Thursday, and hence could benefit from an altered service plan.

In understudied regional railway operations, this study explores passenger boarding and alighting patterns and how station design impacts them, particularly dwell times. Despite extensive metropolitan and suburban train research, regional railways have been overlooked. This study investigates regional rail passenger flow and dwell time to bridge this gap. This article studies dwell times and passenger boarding and alighting at two regional stations in Victoria, Australia, using CCTV data. The objective is to identify insights that might improve regional railway services' efficiency and user experience and advocate for sector-specific solutions. Analysis indicates distinct station boarding and alighting features, highlighting the discovery of the ‘blinded phenomenon’ for train conductors particularly in the afternoon peak (PMP). The results of the study showed that PMP services, which prioritise alighting passengers, had higher dwell times than the morning peak (AMP) services, which emphasise boarding passengers. Obstructed views make it difficult for train conductors to monitor passenger alighting, prolonging dwell times. Better human resource strategies, artificial intelligence for crowd surveillance, and strategic CCTV system deployment to streamline operations and improve passenger experience on regional railways are proposed in the paper, laying the groundwork for future research and operational changes in this vital transportation sector.

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.

Train timetabling poses inherent challenges, prompting the need to enhance existing schedules by extracting valuable insights from the current timetable structure. By such philosophy, this paper studies the impact of timetable elements on the consumed capacity. These elements primarily encompass train operation parameters, including running times and stop plans. Their impact is defined as the deviation of consumed capacity relative to their variations. We initially establish a link between consumed capacity and timetable elements. This relationship is articulated as the signed sum of timetable elements along a designated ”critical path”. Then, the limited impact of any timetable element is clarified, namely changing an element can impact the consumed capacity with its neighbor trains in a combinatorial way. With this knowledge, we analyze the impact of a single element, using stop plans for example. This result is then generalized into analyzing the impact of several dependent and independent stop plans. The findings on capacity calculation and impact analysis of a single element are tested through real-world numerical computations and then extended to analyzing various capacity factors, such as average speed and heterogeneity.

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.

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.