The adherence to a timetable with precise departure and arrival times becomes increasingly challenging in real-world scenarios due to the daily fluctuations in rail traffic, leading to uncertainties that complicate effective real-time traffic management. In this paper, we introduce and optimise timetable flexibility to enhance operational robustness and reduce conflicts resulting from minor train path deviations. We propose a Train Rescheduling with Flexibility (TRF) model, relying on a Mixed Integer Linear Programming (MILP) formulation. The primary objective is to minimise timetable deviation, while maximising timetable flexibility. The punctuality threshold is utilised to optimise time allowances within the real-time traffic plan, considering passenger connections and preventing early departures. A real-life case study that focuses on part of the Dutch railway characterised by complex track layouts and heterogeneous rail traffic is used to validate our model. Furthermore, we investigate the impact of predictive delays on flexibility, along with conducting sensitivity analyses on key parameters such as flexibility weight and punctuality threshold. The results of our optimisation model demonstrate its effectiveness in exploiting timetable flexibility to deal with disturbances.

Semi-flexible transit, integrating fixed-route and on-demand services, offers a demand-adaptive and cost-effective alternative for public transit users, particularly in low-demand conditions. Despite the growing interest in this system, existing approaches have failed to develop comprehensive optimization methods for managing demand fluctuations across distinct scenarios, thereby significantly constraining operational adaptability in semi-flexible transit services. To address this research gap, we propose a scenario-based optimization model that jointly determines the fleet size and master routes at the tactical level as well as sub-routes at the operational level. The objective is to minimize travel costs while ensuring service feasibility under varying passenger demand scenarios, accounting for constraints such as travel time, state changes, time windows, and route consistency. Then, an Augmented Lagrangian Relaxation under Alternating Direction Method of Multipliers (ALR-ADMM) decomposition solution framework is introduced to decouple the proposed integrated problem into three sub-problems, namely master route, sub-route and service planning problems. Numerical experiments on the Sioux-Falls network validate the proposed model and solution approach, achieving a 94.93 % reduction in computation time while maintaining an average optimality difference of 0.57 % compared to the Gurobi optimizer. Sensitivity analysis further examines the effects of vehicle capacity limits, penalty parameters, and demand stop selection, revealing their impact on computational efficiency and operational costs. The applicability of our approach is further assessed through a real-world case study on the West Jordan network, which provides evidence of the ALR-ADMM-based algorithm in terms of both solution quality and computational efficiency. Our findings illustrate the feasibility and potential of the proposed model and algorithm in navigating both the tactical and operational scheme of semi-flexible transit within modern urban transit systems.

Automatic Train Operation (ATO) aims to enhance punctuality, energy efficiency, and reliability by automating driving tasks. Specifically, for mainline railways, an ATO onboard component generates and tracks optimised train trajectories based on time targets or windows at critical network locations, known as timing points, across train routes. These timing points and their associated constraints are specified in the Train Path Envelope (TPE), computed to ensure conflict-free operations. The generation of TPEs relies on dynamic updates of the real-time traffic plan from the Traffic Management System and real-time train statuses (e.g., position and speed). Understanding how TPEs are affected by these updates is essential for effective ATO deployment. To address this, this paper proposes a sensitivity analysis using elementary effects of a TPE generation algorithm, evaluating its response to variations in real-time traffic plans and train status updates. A real-life case study on a Dutch rail corridor with heterogeneous traffic reveals that control timing points can be introduced into the TPE as headways decrease, to homogenise traffic by aligning speed profiles and thus resolving conflicts. Timing point locations remain mostly unchanged, while their associated time windows become more sensitive when placed further along the route. Operational tolerance, which defines the latest conflict-free passing time, becomes more sensitive to headway changes and the distance from the previous stop.

Efficient railway operations are essential to accommodate growing traffic demand and to sustain high levels of system performance on heavily utilized corridors. Conventional train scheduling methodologies often face challenges in preventing train path conflicts arising from deviations in planned trajectories or operational uncertainties. To address this, we developed a framework to automatically generate conflict-free Train Path Envelopes (TPEs) for successive scheduled trains from a real-time traffic plan in a designated railway corridor. Specifically, the TPE is defined as a sequence of time targets or windows at key network locations (known as timing points) and serves as train trajectory constraints in generating conflict-free train trajectories aligned with the real-time traffic plan. The computational framework processes infrastructure and timetable data autonomously, identifies potential track occupation conflicts using blocking time theory across three typical train driving strategies and resolves them through the automated determination of intermediate timing points and dynamic adjustment of departure tolerances. Buffer times are incorporated into the blocking time bounds to tolerate train trajectory tracking errors.Lastly, the framework computes the earliest and latest feasible trajectories for each train. From this the TPEs are derived as a list of timing points with their time windows or targets. This framework not only optimizes track utilization by ensuring conflict-free train operations but also promotes energy efficiency by defining flexible and robust time-distance boundaries for train movements. The efficacy of the proposed framework has been validated through integration with FRISO (Flexible Rail Infrastructure Simulation of Operations), a microscopic simulation tool with discrete, dynamic, stochastic and deterministic properties. This development marks a first step towards a better link between railway traffic management and automatic train operation and is a cornerstone in Europe's Rail FP1-MOTIONAL project.

The COVID-19 pandemic has imposed a dramatic effect on the mobility habits of both passengers and freight in the rail sector. Since the relaxation of COVID-19 restrictions worldwide, rail transport has been revitalised gradually. However, the new normal emerges with unprecedented issues, such as changed travel behaviour, lost profits, and a lack of personnel. In this paper, we determine the arising challenges due to COVID-19 and pandemics in general and subsequently propose several solutions to tackle these challenges in rail transport. These solutions cover multidisciplinary aspects such as passenger demand management, freight demand management, service design, automation, decentralisation and advanced railway technologies. By reviewing the relevant literature on COVID-19, public transport and particularly rail transport, we synthesise and identify promising lines of research that should devote more attention to a more efficient, effective and sustainable rail transport service. This paper provides policymakers, researchers, railway infrastructure managers and undertakings with an overview and an outlook for the impacts of the pandemic crisis and similar situations. It supports decision-making with more evidence and facilitates rail transport to restore its performance and reach its societal goal.

Automatic Train Operation (ATO) is a technology to support or automate train driving for increasing service punctuality, energy efficiency and rail infrastructure capacity. Conflict-free train path planning is crucial to the effective deployment of ATO, which allows ATO-equipped trains to operate according to schedule with different train driving strategies. As different train driving strategies lead to various passing times, current planning practice is inadequate to avoid route conflicts as it only sets target arrival or passing times at stops or major junctions. Therefore, conflict-free train path planning needs the definition of a Train Path Envelope (TPE) that contains time targets or windows defined at discrete locations called timing points to tolerate schedule deviations due to different driving styles. The number and location of the timing points, as well as the associated time targets or windows, is a decision problem. This paper proposes a framework to design a robust set of timing points and their associated time windows in a TPE to enable operational conflict-free train path planning against the driving strategies utilised. This framework relies on a Train Path Slot model which extends the definition of TPE from time windows at a discrete set of locations to an integrated blocking time stairway pattern continuously defined across all locations over a train route. The Train Path Slot model considers three relevant train driving strategies, i.e., energy-efficient driving with or without coasting as well as minimum-time driving considering slight delays. A Linear Programming model is formulated to compute the conflict-free Train Path Slots as constraints for train operation. To meet the optimised Train Path Slots, we analyse several possible sets of timing points in a TPE that are only located at stops or signal positions along the train routes. Those timing point sets are then compared in terms of total Train Path Slot overlap time, capacity, energy efficiency and driving performance indicators. Our research supports infrastructure managers in resolving the imminent problem of timing point determination and TPE computation to reach their capacity goals. At the same time, it allows sufficient driving flexibility for railway undertakings.

Network hierarchy describes the relative arrangement of network elements and reflects its fundamental structure. We propose a multi-dimensional topology-based method for quantifying and comparing the extent to which different Public Transport Networks (PTNs) exhibit a hierarchical structure. The proposed method considers the uneven distribution of node importance with different definitions (e.g., degree centrality and betweenness centrality) in a PTN, the clustering of nodes and the node connection patterns. We apply the developed method on 63 high-capacity PTNs worldwide using General Transit Feed Specification (GTFS) data. In addition to global indicators, we use the goodness-of-fit between the probability density function of local indicators and a skew-normal distribution to quantify the extent of PTN hierarchy. Results show that the scale-free network structure and preferential attachment do not vary much across PTNs. In contrast, stop accessibility and traffic intermediacy vary considerably across PTNs as reflected by the closeness centrality and betweenness centrality distributions. Lastly, metro systems exhibit a more hierarchical structure than their tram and Bus Rapid Transit (BRT) counterparts. This work makes a first step towards a better mapping and comparison of different PTNs, which can assist academics and practitioners in better (re)designing and planning the PTNs of the future.

Predictions on public transport ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. At an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer lag times, in contrast, trip planner data is often available in (near) real-time. This paper analyzes how such data from a trip planner app can be utilized for short-term bus ridership predictions. This is combined with AFC data (in this case smart card data) to construct a ground-truth on actual ridership. The trip planner data is studied using correlation analysis to select informative variables, that are then used to develop 4 supervised machine learning models (linear, k-nearest neighbors, random forest, and gradient boosting decision tree). The best performing model relies on random forest regression and reduces the error by approximately half compared to a baseline model based on the weekly trend. We show that this model performance is maintained even for prediction lead times up to 30 minutes ahead, and for different periods of the day.

Automatic Train Operation (ATO) is well-known in urban railways and gets increasing interest from mainline railways at present to improve capacity and punctuality. A main function of ATO is the train trajectory generation that specifies the speed profile over the given running route considering the timetable and the characteristics of the train and infrastructure. This paper proposes and assesses different possible ATO architecture configurations through allocating the intelligent components on the trackside or onboard. The set of analyzed ATO architecture configurations is based on state-of-the-art architectures proposed in the literature for the related Connected Driver Advisory System (C-DAS). Results of the SWOT analysis highlight that different ATO configurations have diverse advantages or limitations, depending on the type of railway governance and the technological development of the existing railway signaling and communication equipment. In addition, we also use the results to spotlight operational, technological, and business advantages/limitations of the proposed ATO-over-ETCS architecture that is being developed by the European Union Agency for Railways (ERA) and provide a scientific argumentation for it.

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.

Predictions on Public Transport (PT) ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. On an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer lag times and contain no travel intentions, in contrast, trip planner data are often available in (near) real-time and are used before traveling. In this paper, we investigate how such data from a trip planner app can be utilized for short-term bus ridership predictions. This is combined with AFC data (in this case smart card data) to construct a ground truth on actual ridership. Using informative variables from the trip planner dataset through correlation analysis, we develop 3 supervised Machine Learning (ML) models, including k-nearest neighbors, random forest, and gradient boosting. The best-performing model relies on random forest regression with trip planner requests. Compared with the baseline model that depends on the weekly trend, it reduces the mean absolute error by approximately half. Moreover, using the same model with and without trip planner data, we prove the usefulness of trip planner data by an improved mean absolute error of 8.9% and 21.7% and an increased coefficient of determination from a 5-fold cross-validation of 7.8% and 18.5% for two case study lines, respectively. Lastly, we show that this model performance is maintained even for the trip planner requests with prediction lead times up to 30 min ahead, and for different periods of the day. We expect our methodology to be useful for PT operators to elevate their daily operations and level of service as well as for trip planner companies to facilitate passenger replanning, in particular during peak hours.

Automatic Train Operation (ATO) is well-known in urban railways and it gets increasing interest from mainline railways nowadays to improve capacity and punctuality. For the past few years, the European Union Agency for Railways (ERA) has been developing a set of technical specifications for ATO over ETCS, in which it defines a specific architecture between the trackside subsystem and the onboard subsystem. However, there is no clear scientific argumentation supporting the choice of this architecture. Therefore, this paper proposes and assesses different possible ATO architecture configurations to spotlight operational, technological, and business advantages/limitations of ATO over ETCS architecture from the ERA. The set of analyzed ATO architecture configurations are based on state-of-the-art architectures proposed in the literature for the related Connected Driver Advisory System (C-DAS). Results of the assessment highlight that different ATO configurations might have diverse advantages or limitations depending on the type of railway organization and the technological development of the existing railway signaling and communication equipment.

Hierarchy is regarded as a natural phenomenon of public transport networks (PTN). The imbalanced distribution of passenger flow result in a hierarchical structure of PTN and it is also related to the development of technology and the introduction of new modes. However, there is still a lack of knowledge on how to identify the hierarchical structure of the multi-layer PTN. This study proposes a three-step passenger transfer flow based methodology for separating and ranking the PTN: (1) using passenger journey data to derive transfer flow matrix; (2) applying network representation with Louvain method of community detection to separate the PTN layers; (3) performing ranking method, separating inner-transfer and inter-transfer flow. To demonstrate our method, we use one-month smart card data of The Hague, the Netherlands provided by the PTN operator HTM. Our results show that our method is able to, regardless of the geographic location and the mode of transportation, identify the hierarchy of PTN based on the passenger transfer flow pattern. Temporal attributes are also discussed to illustrate how hierarchy is time-dependent, e.g. with respect to the day of the week and the time of the day. Our method supports public transport (PT) operators during design and optimization of PTN and in determining which sets of higher-level service to prioritize during different time periods.