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Under the dual pressure of resources and environment, many countries have focused on the role of railways in promoting low-carbon development of integrated transportation and of even the whole society. This paper aims to provide a comprehensive study on methods to improve railway energy efficiency in other national railways and achievements made by China’s railways in the past practice, and then to propose ways in which in the future China’s railways could rationally select the path of improving energy efficiency regarding the needs of the nation's ever-shifting development and carry out the re-engineering for mechanism innovation in energy conservation and emission reduction process.

This paper first studies other national railways that have tried to promote the improvement of railway energy efficiency by the ways of technology, management and structural reconstruction to reduce energy consumption and carbon emissions. Among them, the effect of structural energy conservation and emission reduction has become more prominent. It has become the main energy conservation and emission reduction measure adopted by foreign railway sectors. The practice of energy conservation and emission reduction of railways in various countries has tended to shift from a technical level to a structural one.

Key aspects in improving energy efficiency include re-optimization of energy structure, re-innovation of energy-saving technologies and optimization of transportation organization. Path selection includes continuing to promote electrified railway construction, increasing the use of new and renewable energy sources, and promoting the reform of railway transportation organizations.

This paper provides further challenges and research directions in the proposed area and has referential value for the methodologies, approaches for practice in a Chinese context. To achieve the expected goals, relevant supporting policies and measures need to be formulated, including actively guiding integrated transportation toward railway-oriented development, promoting innovation in energy-saving and emission reduction mechanisms and strengthening policy incentives, focusing on improving the energy efficiency of railways through market behavior. At the same time, it is necessary to pay attention to new phenomena in the railway industry for track and analysis.

For billing purposes, heavy-haul locomotives in Sweden are equipped with on-board energy meters, which can record several parameters, e.g., used energy, regenerated energy, speed and position. Since there is a strong demand for improving energy efficiency in Sweden, data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.

To monitor energy efficiency, the present study, therefore, develops key performance indicators (KPIs), which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation. Energy meter data of IORE class locomotives, hauling highly uniform 30-tonne axle load trains with 68 wagons, together with additional data sources, are analysed to identify significant parameters for describing driver influence on energy usage.

Results show that driver behaviour varies significantly and has the single largest influence on energy usage. Furthermore, parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions, e.g., axle loads and number of wagons, on energy usage.

Based on the parametric studies, some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived. In the end, some possible measures for improving energy performance in heavy-haul operations are given.

This paper discusses logistics service providers' (LSPs’) energy efficiency initiatives for sustainable development, both from an evolutionary perspective and based on a framework consisting of actions, processes (i.e. at the operations interface) and services (i.e. at the customer interface).

Following a qualitative research design, semi-structured interviews were conducted with sustainability managers at LSPs and the data were analysed via inductive coding. Based on the results and the literature, the authors developed a maturity model for LSPs' transitions to environmental sustainability.

LSPs' sustainable development occurs via operational processes, services at the customer interface, and actions that support those processes and services. Energy efficiency efforts are characterised by process depth that helps LSPs to align with their customers' energy efficiency improvement processes. While services related to energy efficiency connect LSPs and their customers, actions in support vary depending on the logistics activities in which LSPs participate.

Further research is needed to test and verify the maturity model and to clarify the interdependency of its three dimensions.

By categorising energy efficiency initiatives and proposing a maturity model for LSPs' sustainable development via energy efficiency, the authors have developed a tool for logistics actors to assess their progress towards improved sustainability.

The paper contributes to the literature by providing a three-pillar framework to understand the sustainability transitions of LSPs through energy efficiency. Developing a maturity model using this framework also contributes to the literature with an approach to assess sustainability advancement in the logistics industry.

To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

This paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.

Firstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.

The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.

With the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

Massive open online courses (MOOCs) have been gaining popularity as non-formal lifelong learning educational platforms. However, they have been criticized for their low completion rate and low ability for networking. The purpose of this paper is to analyze how incorporating entrepreneurial competencies in MOOCs develops attributes of educational innovation and collaborative projects.

The research followed a three-stage process: in first stage, a comprehensive literature review was conducted to identify dimensions of entrepreneurial skills and attributes of educational innovation in MOOCs. In the second stage, a quantitative study was carried out, based on the analysis of pre- and post-test surveys taken by a sample of 6,517 participants. In the last stage, the interaction analysis model/computer-mediated communication analysis model was applied through qualitative analysis, using the MAXQDA tool to identify if entrepreneurship opportunities were generated in the interactions within the discussion forums of the MOOCs.

The results show that the analyzed MOOCs have an overall completion rate of 12.55 per cent, above the average of the rates found in the literature review. However, only 14.29 per cent of the participants expressed at least one opportunity to generate ventures related to the topics of energy in the discussion forums.

This research could help instructional designers and universities to consider the inclusion of entrepreneurship issues in the design of MOOCs’ content and to encourage more activities that promote networking among participants, to identify business potential from the educational materials.

This research is one of the very few studies on entrepreneurship competencies in MOOCs to understand how the inclusion of issues related to entrepreneurship in MOOCs can generate a positive impact on participants.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

Despite the global resolves to curtail fossil fuel consumption (FFC) in favour of clean energies, several countries continue to rely on carbon-intensive sources in meeting their energy demands. Financial constraints and limited knowledge with regards to green energy sources constitute major setbacks to the energy transition process. This study therefore aims to examine the effects of financial development and human capital on energy consumption.

The empirical analysis is based on the system generalised method of moments (SGMM) for a panel of 134 countries from 1996 to 2019. The SGMM estimates conducted on the basis of three measures of energy consumption, notably fossil fuel, renewable energy as well as total energy consumption (TEC), provide divergent results.

While financial development significantly reduces FFC, its effect is positive though non-significant with regards to renewable energy consumption. Conversely, financial development has a positive and significant effect on TEC. Moreover, the results reveal that human capital development has an enhancing though non-significant effect on the energy transition process. In addition, the results reveal that resource rents have an enhancing effect on the energy transition process. However, when natural resources rents are disaggregated into various components (oil, coal, mineral, natural gas and forest rents), the effects on energy transition are divergent. Although our findings are consistent when the global panel is split into developed and developing economies, the results are divergent across geographical regions. Contingent on these findings, actionable policy implications are discussed.

The study complements extant literature by assessing nexuses between financial development, human capital and energy transition from a global perspective.

At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.

To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.

The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.