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.