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• Five propulsion systems and six energy carriers for regional trains are analyzed. • Dutch regional railway network with sixteen rail passenger services is considered. • Energy carrier production pathway is the main driver of energy use and emissions. • Battery-electric system running on green electricity offers net-zero emissions. • Hydrogen provides emissions reduction only if produced from green electricity. This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, providing the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during 2-h operation, reaching the overall cogeneration efficiency of 66.81%. • Backward-looking simulation model for fuel cell hybrid-electric train is presented. • Passenger transport service and vehicle-to-grid (V2G) applications are analyzed. • Real-time equivalent consumption minimization strategy is developed. • Overall fuel cell electrical efficiency of 38.89% is achieved for the round trip. • The overall cogeneration efficiency in V2G application of 66.81% is reached. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for evaluating the performance of railway crossing rails after long-term service. The method includes 1) 3D profile and hardness measurements; 2) finite element simulation of wheel/rail interaction; and 3) numerical prediction of rail degradation. We conducted a case study on a crossing that had been in service for several years. The results indicate that the crossing experienced a run-in process in the major traffic direction, manifested as a widening of the running band, an enlargement of the contact patch size, a decrease in contact stress and eventually a reduction in plastic deformation and wear. However, the wheel/rail interaction was exacerbated in the minor traffic direction which induced more severe plastic deformation and wear. [ABSTRACT FROM AUTHOR]

To ensure the reliability of power systems, the independent system operator (ISO) manages the planning process of the maintenance of generation units for generation companies (GENCOs). This paper focuses on a widely studied two-layer long-term predictive maintenance decision making framework in a deregulated environment. In the first layer the ISO-wide maintenance schedule is optimized for the GENCOs, targeting minimal total maintenance cost and degradation statuses. In the second layer, a bidding mechanism is designed for GENCOs who are not satisfied with the time slots scheduled by the first layer, so that they can bid for their preferred time slots. A novel bidding mechanism for the host ISO (i.e., the ISO that manages the maintenance planning process) is proposed, called interchangeable bidding mechanism for maintenance (IBMM). In this mechanism, the GENCOs of the host ISO can use their bid price to purchase the supportive energy from the GENCOs of the neighbor ISOs. Furthermore, they also can pay a penalty fee for reducing the amount of energy transmitted from the host ISO to the neighbor ISO with respect to what has been stipulated in the long-term inter-ISO power exchange contract. IBMM provides more opportunities for GENCOs of the host ISO to obtain their preferred maintenance time slots. Additionally, the power system reliability can be ensured. IBMM is formulated as a mixed-integer non-linear bidding programming problem. Then, the bidding programming problem is recast into a mixed-integer second-order cone programming (MISOCP) problem that can be solved using Gurobi. In the case study, the IEEE 118-bus network is studied to illustrate the performance of the proposed bidding strategy. • The interchangeable bidding mechanism is proposed for maintenance of generation units. • GENCOs can change maintenance slots by buying supportive energy from neighbor ISOs. • Centralized, Priority and Impromptu starting bidding processes are discussed. • GENCOs have more flexibility to maintain their generation units. [ABSTRACT FROM AUTHOR]

Non-electrified regional railway lines with typically employed diesel-electric multiple units require alternative propulsion systems to meet increasingly strict emissions regulations. With the aim to identify an optimal alternative to conventional diesel traction, this paper presents a model-based assessment of hydrogen-powered propulsion systems with an internal combustion engine or fuel cells as the prime mover, combined with different energy storage system configurations, based on lithium-ion batteries and/or double-layer capacitors. The analysis encompasses technology identification, design, modelling and assessment of alternative powertrains, explicitly considering case-related constraints imposed by the infrastructure, technical and operational requirements. Using a regional railway network in the Netherlands as a case, we investigate the possibilities in converting a conventional benchmark vehicle and provide the railway undertaking and decision-makers with valuable input for planning of future rolling stock investments. The results indicate the highest fuel-saving potential for fuel cell-based hybrid propulsion systems with lithium-ion battery or a hybrid energy storage system that combines both energy storage system technologies. The two configurations also demonstrate the highest reduction of greenhouse gas emissions compared to the benchmark diesel-driven vehicle, by about 25% for hydrogen produced by steam methane reforming, and about 19% for hydrogen obtained from electrolysis of water with grey electricity. • Conceptual design of hydrogen propulsion systems for a regional train is presented. • Two prime mover and three energy storage system technologies are considered. • A backward-looking quasi-static simulation model is provided. • A real-time energy management and control strategy is developed. • Potential GHG emissions reduction of up to 25.5% is identified. [ABSTRACT FROM AUTHOR]

A multiobjective-model-based predictive control approach is proposed to solve a dynamic pickup and delivery problem in the context of a potential dial-a-ride service implementation. A dynamic objective function including two relevant dimensions, user and operator costs, is considered. Because these two components typically have opposing goals, the problem is formulated and solved using multiobjective model predictive control to provide the dispatcher with a more transparent tool for his/her decision-making process. An illustrative experiment is presented to demonstrate the potential benefits in terms of the operator cost and quality of service perceived by the users. [ABSTRACT FROM AUTHOR]

In this paper, two hybrid Model Predictive Control (MPC) approaches for freeway traffic control are proposed considering variable speed limits (VSL) as discrete variables as in current real world implementations. These discrete characteristics of the speed limits values and some necessary constraints for the actual operation of VSL are usually underestimated in the literature, so we propose a way to include them using a macroscopic traffic model within an MPC framework. For obtaining discrete signals, the MPC controller has to solve a highly non-linear optimization problem, including mixed-integer variables. Since solving such a problem is complex and difficult to execute in real-time, we propose some methods to obtain reasonable control actions in a limited computation time. The first two methods ( θ -exhaustive and θ -genetic discretization) consist of first relaxing the discrete constraints for the VSL inputs; and then, based on this continuous solution and using a genetic or an exhaustive algorithm, to find discrete solutions within a distance θ of the continuous solution that provide a good performance. The second class of methods split the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits. The speed limits optimization, which is much more time-consuming than the ramp metering one, is solved by a genetic or an exhaustive algorithm in communication with a non-linear solver for the ramp metering. The proposed methods are tested by simulation, showing not only a good performance, but also keeping the computation time reduced. [ABSTRACT FROM AUTHOR]

Highlights: [•] A survey on identification of both hybrid systems and hybrid-fuzzy systems is presented. [•] The definition of the identification problem of the class of hybrid-fuzzy models considered in the paper. [•] A hierarchical methodology based on the same principles of conventional Takagi–Sugeno identification (principal components and fuzzy clustering), that first determines the hybrid behavior of the system and then all the other non-linearities. [•] Two examples, one more academic one, and based on real data measured from a traffic process. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state–space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach. [Copyright &y& Elsevier]

• Optimal Lithium-ion battery sizing for hybrid diesel-electric train is presented. • Bi-level optimization approach integrates optimal sizing and control levels. • Two energy management strategies are developed and compared. • Trade-off between fuel savings and hybridization cost is analyzed. • Fuel savings and CO 2 emissions reduction of up to 34.5% is obtained. Hybridization of diesel multiple unit railway vehicles is an effective approach to reduce fuel consumption and related emissions in regional non-electrified networks. This paper is part of a bigger project realized in collaboration with Arriva, the largest regional railway undertaking in the Netherlands, to identify optimal solutions in improving trains' energy and environmental performance. A significant problem in vehicle hybridization is determining the optimal size for the energy storage system, while incorporating an energy management strategy as well as technical and operational requirements. With the primary requirement imposed by the railway undertaking to achieve emission-free and noise-free operation within railway stations, we formalize this as a bi-level multi-objective optimization problem, including vehicle performance, the trade-off between fuel savings and hybridization cost, influence of the energy management strategy, and other constraints. By deriving a Li-ion battery parameters at the cell level, a nested coordination framework is employed, where a brute force search finds the optimal battery size using dynamic programming for full controller optimization for each feasible solution. In this way, the global minimum for fuel consumption for each battery configuration is achieved. The results from a Dutch case study demonstrated fuel savings and CO 2 emission reduction of more than 34% compared to a standard vehicle. Additionally, benefits in terms of local pollutants (NO x and PM) emissions are observed. Using an alternative sub-optimal rule-based control demonstrated a significant impact of the energy management on the results, reflected in higher fuel consumption and increased battery size together with corresponding costs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter challenges in handling large space–time domains, even for problems with closed-form analytical solutions. A causality-respecting PINN loss function is employed to overcome this limitation, effectively capturing the underlying physics. However, it is observed that the causality-respecting PINN lacks generalizability. We propose using solutions to similar problems instead of training from scratch by employing transfer learning while adhering to causality to accelerate convergence and ensure accurate results across diverse scenarios. The primary contribution of this paper lies in introducing a causality-respecting PINN loss function in the context of structural engineering and coupling it with transfer learning to enhance the generalizability of PINNs in simulating the dynamics of beams on elastic foundations. Numerical experiments on the Euler–Bernoulli beam highlight the efficacy of the proposed approach for various initial conditions, including those with noise in the initial data. Furthermore, the potential of the proposed method is demonstrated for the Timoshenko beam in an extended spatial and temporal domain. Several comparisons suggest that the proposed method accurately captures the inherent dynamics, outperforming the state-of-the-art physics-informed methods under standard L 2 -norm metric and accelerating convergence. [ABSTRACT FROM AUTHOR]

This study evaluates the degradation of wheels and rails at railway crossings. The evaluation method is composed of 1) finite element simulation of dynamic wheel/crossing interaction and 2) multi-criteria analysis of wheel/rail degradation in terms of yield behavior, rolling contact fatigue (RCF) and wear. With the aid of this method, we conducted a case study identifying the proper yield strength of rail steel material for a 54E1-1:9 crossing under a specified traffic condition. The case study indicates that the wear of contact bodies is more sensitive to train speed compared with yield and RCF; the increase of rail yield strength suppresses rail degradation while exacerbating wheel degradation; and rail yield strength in the range of 500–600 MPa is preferred to achieve a good trade-off between the wheel and rail degradations. • A method is proposed to evaluate the degradation of wheels and rails at railway crossings. • The method uses finite element simulation and multi-criteria analysis. • Wheel/rail degradation is evaluated in terms of yield behavior, rolling contact fatigue (RCF) and wear. • A case study is conducted to identify the proper yield strength of rail steel material for a 54E1-1:9 crossing. [ABSTRACT FROM AUTHOR]

• A methodology for condition-based maintenance planning and crew scheduling for railway track. • A distributed optimization scheme to apply the proposed approach to large-scale railway networks. • A chance-constrained formulation to achieve a robust but conservative maintenance plan. We develop a multi-level decision making approach for optimal condition-based maintenance planning of a railway network divided into a large number of sections with independent stochastic deterioration dynamics. At higher level, a chance-constrained Model Predictive Control (MPC) controller determines the long-term section-wise maintenance plan, minimizing condition deterioration and maintenance costs for a finite planning horizon, while ensuring that the deterioration level of each section stays below the maintenance threshold with a given probabilistic guarantee in the presence of parameter uncertainty. The resulting large MPC optimization problem containing both continuous and discrete decision variables is solved using Dantzig-Wolfe decomposition to improve the scalability of the proposed approach. At a lower level, the optimal short-term scheduling of the maintenance interventions suggested by the high-level controller and the optimal routing of the corresponding maintenance crew is formulated as a capacitated arc routing problem, which is solved exactly by transforming it into a node routing problem. The proposed approach is illustrated by a numerical case study on the optimal treatment of squats of a regional Dutch railway network. Simulation results show that the proposed approach is robust, non-conservative, and scalable. [ABSTRACT FROM AUTHOR]

Context: Demoralization is a state of existential distress in patients with advanced illness, typically with coping difficulties, feelings of loss of sense, and purpose in life and despair, among other things. The New Demoralization Scale (DS-II) is an evaluation tool for this syndrome, which has recently been reformulated on a shorter scale.Objectives: The objective of this study was to obtain a Spanish version of the DS-II and to assess its psychometric properties in advanced cancer patients in Spain and a number of Latin American countries.Methods: Following a translation-back translation process, a validation study and a confirmatory analysis using structural equation models with their corresponding latent constructs were undertaken. Patients completed the DS-II in Spanish (DS-II (es)), the Hospital Anxiety and Depression Scale, and the Edmonton Symptom Assessment System-revised. Reliability was studied according to internal consistency; construct validity and concurrent validity with the Hospital Anxiety and Depression Scale and the Edmonton Symptom Assessment System-revised; discriminant validity using the Karnofsky Performance Status scale; and feasibility, with response ratio and required time. Cutoff points were established, and sensitivity and specificity were studied.Results: The DS-II (es) was obtained. One hundred fifty patients completed the validation study. The confirmatory analysis showed coherence, and all items correlated positively with their subscales and with the overall scale. Cronbach's alpha for the DS-II (es) was 0.88, for the sense and purpose subscale 0.83, and for the coping ability 0.79. Demoralization correlated significantly with emotional distress (rho = 0.73, P < 0.001). The tool distinguished between patients with diverse functional levels (rho = -0.319, P = 0.001). Cutoff points at 10 and 20 out of 32 were established. The scale showed high sensitivity (81.97%) and specificity (80.90%). The prevalence of demoralization was 33% in our sample.Conclusion: The Spanish version of the new Kissane DS-II demoralization scale has shown to be valid, reliable, and feasible with adequate psychometric properties. [ABSTRACT FROM AUTHOR]

This paper develops a multi-level decision making approach for the optimal planning of maintenance operations of railway infrastructures, which are composed of multiple components divided into basic units for maintenance. Scenario-based chance-constrained Model Predictive Control (MPC) is used at the high level to determine an optimal long-term component-wise intervention plan for a railway infrastructure, and the Time Instant Optimization (TIO) approach is applied to transform the MPC optimization problem with both continuous and integer decision variables into a nonlinear continuous optimization problem. The middle-level problem determines the allocation of time slots for the maintenance interventions suggested at the high level to optimize the trade-off between traffic disruption and the setup cost of maintenance slots. Based on the high-level intervention plan, the low-level problem determines the optimal clustering of the basic units to be treated by a maintenance agent, subject to the time limit imposed by the maintenance slots. The proposed approach is applied to the optimal treatment of squats, with real data from the Eindhoven-Weert line in the Dutch railway network. [ABSTRACT FROM AUTHOR]

• A methodology to monitor transition zones using multi-ABA measurements is proposed. • Four KPIs are proposed to quantify local multi-ABA energy differences. • The method is suitable for multi-ABA systems mounted on operational trains. • Nine double-track railway bridges on the Dutch railway are analyzed. This paper presents a methodology for monitoring transition zones using responses of multiple-axle box acceleration (multi-ABA) measurements. The time–frequency characteristics of the vertical ABA signals from four wheelsets are analyzed. The major contributions are as follows. (1) We propose four key performance indicators (KPIs) to quantify local multi-ABA energy differences at different abutments, tracks, entrance and exit sides, and inner and outer rails. (2) The same dominant spatial frequencies are obtained with different measurement speeds, so the proposed method is suitable for multi-ABA systems mounted on operational trains. Transition zones at nine double-track railway bridges are selected as the case study. The KPIs indicate that (1) the energy differences between abutments are above 80% in three bridges; (2) two abutments show that the energy differences between tracks are higher than 100%; (3) three tracks have energy differences above 100% between the entrance and exit sides; and (4) the energy differences between rails are above 80% on three sides. Finally, using measurement with 7 years of difference, the KPIs and track quality index are discussed. These findings suggest that multi-ABA measurement can be used as a health condition monitoring method for railway transition zones to support condition-based maintenance. [ABSTRACT FROM AUTHOR]

This paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment, the most relevant being a set of frequency bands in the ABA power spectrum. A detection algorithm is proposed based on the derived frequency characteristics of the ABA signal. We compared the responses of IRJs in good condition with those in poor condition. Track inspections were performed to validate the health condition monitoring methodology in different IRJs of the Dutch railway network. The hit rate of IRJs detection was 84% for two validated tracks. The damage assessment procedure allowed to prioritize the IRJs that require maintenance. This information is useful for the railway inframanagers as it allows to predict where safety compromises will be faced. [ABSTRACT FROM AUTHOR]

Squats are associated with high frequency vibrations of the wheel-rail system and can be registered using axle box acceleration (ABA) measurements. In this paper a three-dimensional finite-element (FE) model is proposed to capture the dynamic features of the ABA related to squats in the high frequency range. The FE model is validated relying on a set of real-life ABA measurements at two characteristic rail surface defects (light and severe squats), located in a section of the railway network in Assen. The Netherlands. The FE model managed to properly capture the dynamic features in the range of frequencies up to 2000 Hz. [ABSTRACT FROM AUTHOR]

In this paper we describe the design of hybrid fuzzy predictive control based on a genetic algorithm (GA). We also present a simulation test of the proposed algorithm and a comparison with two hybrid predictive control methods: Explicit Enumeration and Branch and Bound (BB). The experiments involved controlling the temperature of a batch reactor by using two on/off input valves and a discrete-position mixing valve. The GA-hybrid predictive control strategy proved to be a suitable method for the control of hybrid systems, giving similar performance to that of typical hybrid predictive control strategies and a significant saving with respect to the computation time. [Copyright &y& Elsevier]

Highlights • Fuzzy and neural network prediction intervals are derived based on fuzzy numbers. • Fuzzy and neural network prediction intervals are derived based on fuzzy numbers. • The new method provides more informative prediction intervals for load forecasting. Abstract Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario. [ABSTRACT FROM AUTHOR]

Highlights • A decision support approach is proposed for condition-based maintenance of rails. • The methodology uses big data analysis and expert systems. • ABA measurements, video images and prior knowledge of the track are employed. • Integrated estimation of the rail health condition supports maintenance decisions. • Measurements from the track Amersfoort-Weert in the Dutch railway network are used. Abstract In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making. [ABSTRACT FROM AUTHOR]

This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers and was developed in the MATLAB/Simulink ® environment. A MPC (model predictive control) strategy established at a supervisory level is presented. The intent of the strategy is to regulate the steam pressure of the superheater of the ISCC plant. The combined use of the simulator and the supervisory control strategy allows for the quantification of the reduction in fuel consumption that can be achieved when integrated solar collectors are used in a combined cycle plant. The ISCC plant simulator is suitable for designing, evaluating and testing control strategies and for planning the integration of solar and combined cycle plants. [ABSTRACT FROM AUTHOR]

Abstract: A hybrid predictive control formulation based on evolutionary multi-objective optimization to optimize real-time operations of public transport systems is presented. The state space model includes bus position, expected load and arrival time at stops. The system is based on discrete events, and the possible operator control actions are: holding vehicles at stations and skipping some stations. The controller (operator) pursues the minimization of a dynamic objective function to generate better operational decisions under uncertain demand at bus stops. In this work, a multi-objective approach is conducted to include different goals in the optimization process that could be opposite. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side, and impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. From the conducted experiments considering a single bus line corridor, we found that the two objectives are opposite but with a certain degree of overlapping, in the sense that in all cases both objectives significantly improve the level of service with respect to the open-loop scenario by regularizing the headways. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing dynamically finding the pseudo-optimal Pareto front and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds. [Copyright &y& Elsevier]

• Sleeper hanging defects are studied with a wheel-rail transient rolling contact model. • Rail irregularities are an input for the analysis of wheel-rail contact behavior and rail degradation. • Both rail irregularity geometry and its position affect severity of sleeper hanging. • Sleeper hanging defects exacerbate the plastic deformation of the rail material. • Sleeper hanging defects rarely affect the distribution of adhesion-slip states and rolling contact fatigue. In this study, a wheel-rail transient rolling contact model capable of accounting for the nonlinear displacement-force properties of hanging sleepers is proposed. The sleeper hanging status affected by rail irregularities is an input for an analysis of the wheel-rail contact behavior and related rail degradation in terms of plastic deformation and rolling contact fatigue. The results indicate that the severity of sleeper hanging is significantly affected by the geometric characteristics and the relative position with respect to the sleepers of the rail surface irregularity. The sleeper hanging defects aggravate the wheel-rail impact and increase the wheel-rail contact force amplitude, contact patch size, pressure, and von Mises stress, thus exacerbating the plastic deformation of the rail material. It was also found that the sleeper hanging defects rarely affect the distribution of the adhesion-slip states and rolling contact fatigue. The knowledge gained can serve as guidance for evaluating the condition and conducting the maintenance of ballasted railway tracks. [ABSTRACT FROM AUTHOR]