Search Results (12)

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

Synchronous transfer has been widely studied in public transport scheduling, with most research focusing on coordination among conventional bus lines. However, with the rapid expansion of urban rail transit systems, metro–bus transfers have become increasingly important for enhancing overall urban public transport network performance. This study investigates the maximum synchronous transfer problem between metro and conventional bus services under passenger flow constraints. Considering the large transfer demand and the pulse-arrival characteristics of metro trains, a passenger waiting constraint at bus stops is incorporated to reflect capacity limitations and crowding effects. A passenger-flow-constrained maximum synchronization model is formulated to optimize bus departure times without increasing service frequency. Dongjiekou Metro Station and three surrounding pairs of bus stops are selected as a case study. Model parameters are determined through field surveys and operational data. The Grey Wolf Optimizer (GWO) and a simulated annealing–improved Grey Wolf Optimizer (SA-IGWO) are employed to solve the proposed model. The results show that both algorithms significantly improve synchronized transfer volumes by adjusting departure times without increasing service frequency. Compared with the original schedule, the SA-GWO achieves an improvement in synchronization performance ranging from 45% to 50%, outperforming the standard GWO. Full article

Interim periods between peak and off-peak operations in urban rail transit networks often suffer from mismatched headways across lines, which increases passenger transfer waiting and operating costs. This paper proposes a passenger-oriented timetable synchronization method for network-wide interim period train service. In this study, based on the AFC data, passengers are assigned to the shortest travel time paths, and passenger transfer flows are linked to connecting train pairs by consideration of the maximum acceptable waiting time. As a result, the transfer waiting time is accurately calculated by matching passengers’ platform arrival times with the departures of feasible connecting trains. A mixed integer nonlinear programming model then jointly optimizes departure headways at each line’s first station, arrival and departure times at transfer stations, subject to safety headways and time bounds. The objective minimizes total cost, combining transfer waiting time cost and train operating cost (depreciation and distance-related cost). A simulated-annealing-based genetic algorithm (SA-GA) is designed to solve the NP-hard problem. A case study on the Nanjing rail transit network from 6:30 to 7:30 reduces total cost by 6.88%, including 3.77% lower transfer waiting time cost and 14.49% lower operating cost, and shows stable results under typical transfer demand fluctuations. Full article

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article

To enhance the accessibility and efficiency of airports, the concept of airport connectivity is extended to High-Speed Rail (HSR), as major hub airports now have direct access to an HSR station. The traditional hub connectivity index is supplemented by the number and quality of connections between train and flight departures/arrivals (or timetables). The methodology is tested at the Paris-Charles de Gaulle airport. The results highlight that air–rail and rail–air connections can represent up to 72% of the total hub connectivity. A disaggregated analysis of connectivity across origin–destination pairs was conducted, revealing potential synchronization gaps. These findings demonstrate that this tool can assist transportation service providers in synchronizing their timetables, by measuring the degree to which it contributes to improve connectivity. Moreover, the findings offer new insights into air–rail timetable coordination and provide policy implications regarding the replacement of feeder flights by HSR. Full article

Performing synchronization in public transport is one of the most challenging tasks that transport managers perform when organizing the processes of passenger servicing. Many variables characterizing existing public transport lines should be considered in the final timetable; in addition, the complexity of the transportation system, the variability in transport demand, and the stochasticity of the servicing process both in time and space have a significant influence on the result of synchronization. The synchronization problem in real-world applications does not have an exact solution, so in practice, a variety of techniques were developed to achieve a rational solution in a reasonable time. In our paper, we classify existing approaches to solving the problem of public transport synchronization, describe the essence of the most promising methods, and study their popularity based on the most recent scientific publications. As the result of our research, we show the most promising directions for the future development of synchronization methods and their application in public transportation. Full article

Modern cities heavily rely on public transport systems to enhance citizen access to urban services and promote sustainability. To optimize public transport, intelligent computer-aided tools play a pivotal role in decision making. This article tackles the complex challenge of bus timetabling, specifically focusing on improving multi-leg trips or transfers. It introduces a novel multi-objective Mixed-Integer Programming Linear (MILP) model that concurrently maximizes passenger transfers and minimizes budgetary costs, while also adhering to the minimum required quality-of-service constraints for regular (non-multi-leg) trips, and an exact resolution approach based on the ε-constraint method to obtain a set of efficient solutions is used. The competitiveness of the model is validated via a computational experimentation performed over real-world scenarios from the public transportation system of Montevideo, Uruguay. The findings evinced that the MILP model was able to compute a set of Pareto efficient solutions that explore the tradeoff between the number of successful transfers and the cost of the system. Moreover, the best tradeoff solutions surpass the current city timetable, excelling in both the number of transfers and cost efficiency. Full article

Heutagogy and blended learning (BL) are core concepts in the educational discourse post-COVID-19. Conducting a mixed-methods study, we investigate meeting points between heutagogy principles and BL in the context of curricular change in the academic timetable of teacher education college, where pre-COVID most courses have been taught face-to-face (F2F). At present, teacher educators and students meet F2F for three weeks, followed by a week of remote learning, combining synchronous and asynchronous pedagogies. Data have been collected by a closed-ended questionnaire and two focus groups, involving altogether 76 lecturers and 553 students. Findings indicate that heutagogy has been applied in all facets of BL, rather than only with online or digital technological components. This study explores a bottom-up growth of heutagogy expressions in BL at three meeting points. When the core facets of heutagogy principles have been identified, there has been a predominance of the students’ agency and life-long learners, together with facets such as a non-linear learning and capability development that have been underrepresented. This study contributes to the research field of heutagogy in teacher education as it identifies the meaning and the way a structural change in the curriculum can constitute an accelerator and catalyst when implementing heutagogy in practice. Full article

This study investigates a post-COVID-19 curricular change in the blended learning (BL) academic timetable of a teacher education college where, pre-COVID-19, most academic courses were taught face-to-face (F2F) on campus. At present, the meetings are F2F for three weeks, followed by a week of remote learning, combining synchronous and asynchronous pedagogies. This study explores these two aspects of the online component and the considerations for their implementation. In a mixed-method approach, the data were collected using a closed questionnaire and two focus groups involving 76 lecturers and 553 students altogether. Of the wide range of pedagogies identified, the highest success rating was accorded to synchronous frontal lectures via Zoom by the students and to integrating MOOCs, YouTube, and Podcasts by the lecturers. Moreover, compared to the lecturers, the students rated the success of asynchronous self-directed learning considerably higher. Qualitative analysis revealed that pedagogies slated for the online module were frequently negotiated between students and teachers. Findings suggest that a structural change in the curriculum could be a first step in rethinking pedagogies in the post-COVID-19 education arena. The next step should focus on narrowing the gap between lecturers’ and students’ perceptions regarding the success of the various pedagogies. Full article

Intermodal and multimodal door-to-door journeys refer to the usage of various transport modes (air, rail, bus, road or maritime) by the traveler to complete a single journey. The main difference between these two approaches is that multimodal transport is executed under a single transport contract (a single ticket) between the passenger, on the one hand, and transport operators, on the other hand. The benefits of this type of service are reflected in the potential to save time and money. Such systems would make the transport sector greener and more sustainable, promote growth and reduce carbon emissions. The purpose of this paper is to define the concept of an air passenger multimodal transport system, identify factors and challenges that determine such a system’s development within Europe and to provide recommendations and directions for future research. The research carried out so far has indicated that market segmentation and transport system characteristics, as well as economic, social and political factors, have direct impacts on system development. This paper provides the basis for introducing single ticket, timetable synchronization and data sharing services, as well as the need to update the related regulations in order to move towards air passenger multimodality in both research and practice. Full article

This article addresses timetable synchronization in public transportation, an important problem in modern smart cities, in order to guarantee a proper quality of service to citizens. Two variants of the bus timetabling synchronization problem considering extended transfer zones are studied: optimizing offsets and optimizing offsets and headways for each line. An exact mixed integer programming and an evolutionary algorithm are developed to solve both problem variants. The algorithms are evaluated on 45 instances of a real case study, the intelligent transportation system of Montevideo, Uruguay. Experimental results reported significant improvements over the current timetable implemented by the city administration. The number of successful synchronizations improved up to 66.6% and 179.9% for the first and second problem variant, respectively. The average waiting times for transfers improved, especially in tight problem instances (up to 57.8% and 158.3% for the first and second problem variant, respectively). The proposed planning methods are useful to help decision makers to configure public transportation systems. Full article

Transfer synchronization is an important issue in timetable scheduling for an urban rail transit system, especially a cross-platform transfer. In this paper, we aim to optimize the performance of transfer throughout the daily operation of an urban rail transit system. The daily operation is divided into multiple time periods and each time period has a specific headway to fulfill time varied passenger demand. At the same time, the turn-back process of trains should also be considered for a real operation. Therefore, our work enhances the base of the transfer synchronization model taking into account time-dependent passenger demand and utilization of trains. A mixed integer programming model is developed to obtain an optimal timetable, providing a smooth transfer for cross-transfer platform and minimizing the transfer waiting time for all transfer passengers from different directions with consideration of timetable symmetry. By adjusting the departure time of trains based on a predetermined timetable, this transfer optimization model is solved through a genetic algorithm. The proposed model and algorithm are utilized for a real transfer problem in Beijing and the results demonstrate a significant reduction in transfer waiting time. Full article