Journal of Rail Transport Planning & Management最新文献

This paper investigates the acceptance of a mega rail freight infrastructure as a sustainable alternative to road transport for domestic freight movements in India. The dedicated rail freight corridors (DFCs) are the freight-only rail corridors proposed by the Indian government to improve freight mobility from a sustainable outlook. A shipper/freight forwarder survey was conducted to gather information on mode attributes and their stated preferences toward DFCs. We employ discrete choice (binary logit) and machine learning algorithms (random forest and extreme gradient boosting) to analyse the choice behaviour. The machine learning methods exhibited higher prediction accuracy, while discrete choice models offered better interpretability. On-time performance and transport costs are crucial factors that influence mode choice. Large-scale companies are more willing to shift to DFCs compared to small and medium firms. The policy scenario analysis indicates that providing a better on-time performance can gain a substantial share of DFCs.

The study explores the relationship between the determinants and the ridership decrease incorporating spatial heterogeneity. ARIMA model is utilized to estimate the normal ridership assumed absence of COVID-19. Geography weighted regression (GWR) with Gaussian kernel function is constructed for regression. The K-means algorithm is applied to cluster the stations based on coefficients. Stations of Tokyo case are clustered into 2 groups: city area and western ward which represents mainly suburban areas. City stations are mainly influenced by the number of transfer lines, distance to the CBD, number of jobs and residents. In the western ward, the level of importance that residents place on public health primarily influences the ridership decrease. The implementation of work-from-home policies makes number of jobs a positive impactor on the decrease in ridership, with a greater impact observed on urban stations compared to suburban stations. City residents tend to engage in more travel than suburban residents because of less spacious living environments, which partially offsets the decrease in ridership. The findings offer parameters for predicting ridership of both city and suburban stations during public health emergency events, such as COVID-19. They can assist URT operators in developing strategies for balancing passenger demand and operational costs.

The railway sector is a relevant segment for the transportation of people and goods, and its efficiency and sustainability are crucial for fostering economic development and minimizing environmental impact. This research proposes an Industry 4.0 maturity model for passenger railway companies to manage emerging technologies, aiming to enhance them holistically to leverage the internal and external business dimensions. An exploratory and qualitative approach was employed, involving semi-structured interviews with experts in Brazilian railway transport. Based on the opportunities and Key Performance Indicators identified within the sector, we developed an Industry 4.0 maturity model for companies and subsequently tested it in collaboration with a transport operator. The findings suggest that the proposed Industry 4.0 maturity model can effectively guide practitioners in applying advanced technologies for robust and efficient train operations, prescriptive maintenance, strong supply chain management, and improved passenger experience and worker performance. This research provides a noteworthy and substantial contribution by introducing unprecedented frameworks that offer a holistic view of Industry 4.0 in the context of passenger railways. The study's practical impact is aiding passenger railway companies to navigate their Industry 4.0 journey. Academically, the research contributes to advancing the holistic understanding of Industry 4.0 as an ongoing phenomenon, enriching the academic discourse by discussing published works and presenting empirical data from railway companies.

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.

Heavy-haul railways (HHRs) pose significant challenges due to their substantial traction weight, extended train length, and complex operational environments. Heavy-haul trains (HHTs), equipped with traditional pneumatic control braking systems, must adopt cycle braking strategies on long downhill slopes. The varying traction masses of HHTs on these railways lead to diverse maneuvering characteristics, presenting challenges for drivers and dispatchers in unforeseen circumstances. To enhance transportation efficiency and mitigate operational complexities, a trajectory optimization method is formulated for determining the optimal trajectory of HHTs with different traction masses under complex conditions, including long downhill slopes, temporary speed limit sections, and regular sections. It considers the dynamics of train traction, braking, and coasting at each phase, optimizing objectives such as train operation efficiency, energy consumption, and pneumatic braking times. A linear weight search algorithm ensures punctuality, and the model is linearized into a mixed-integer linear programming (MILP) form using segmented and stepwise functions to align with operational realities. Simulation experiments utilizing real data and various HHT configurations validate the efficacy of the proposed approach against alternative methods. This method offers precise trajectory optimization under complex conditions, providing valuable guidance for dispatchers and drivers in the heavy-haul railway sector.

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.

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.

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 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.