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Over the last years, there have been initiated several pilots with autonomous minibusses. Unlike regular bus services, autonomous minibusses serve a limited number of stops and have more flexible schedules since they do not require bus drivers. This allows the operation of a line through a flexible combination of sublines, where a subline serves a subset of consecutive stops in the same order as the original line. This paper studies the subline frequency setting (SFS) problem under uncertain passenger demand. We present a frequency setting model that assigns autonomous minibusses to sublines in order to exploit the available resources as much as possible and minimize the operational and passenger waiting time costs. Passenger waiting time costs may depend on the combination of several lines whose frequencies cannot be perfectly aligned for each passenger journey. We present a new estimation of the expected waiting time for passengers to improve the accuracy of the passenger waiting time costs in the case of sublines. Our SFS model is originally formulated as a MINLP and reformulated as a MILP that can be solved to global optimality. Further, we explicitly consider the uncertainty of passenger demand in the optimization process by formulating a stochastic optimization model. The performances of our stochastic and deterministic models that assign minibusses to sublines are tested under various passenger demand scenarios in the 14-stop autonomous minibus line in Eberbach, Germany and a fictional bus line with 20 bus stops. Results show potential improvements in operational costs in the range of 10-40% depending on the passenger demand profile.

We present a mixed-integer linear program (MILP) for line planning with integrated mode and route choice. In contrast to existing approaches, the mode and route decisions are modeled according to the passengers' preferences while commercial solvers can be applied to solve the corresponding MILP. The model aims at finding line plans that maximize the profit for the public transport operator while estimating the corresponding passenger demand with choice models. Both components of profit, revenue and cost, are influenced by the line plan. Hence, the resulting line plans are not only profitable for operators but also attractive to passengers. By suitable preprocessing of the passengers' utilities, we are able to apply any choice model for mode choices using linear constraints. We provide and test means to improve the computational performance. In experiments on the Intercity network of the Randstad, a metropolitan area in the Netherlands, we show the benefits of our model compared to a standard line planning model with fixed passenger demand. Furthermore, we demonstrate with the help of our model the possibilities and limitations for operators when reacting to changes in demand in an optimal way. The results suggest that operators should regularly update their line plan in response to changes in travel demand and estimate the passenger demand during optimization.

In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times. In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers’ perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers’ origins and destinations. We model the problem as a mixed integer quadratic program and linearize it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the trade-offs between dwell times and regularity of trains in two case studies based on the Dutch railway network.

01 februari 2020, Contains fulltext : 220990.pdf (Publisher’s version ) (Closed access), PURPOSE: Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCT) to investigate moderators of exercise intervention effects on cancer-related fatigue. METHODS: We used individual patient data from 31 exercise RCT worldwide, representing 4366 patients, of whom 3846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test. RESULTS: Exercise interventions had statistically significant beneficial effects on fatigue (beta = -0.17; 95% confidence interval [CI], -0.22 to -0.12). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (betadifference = -0.18; 95% CI -0.28 to -0.08). Supervised interventions with a duration

We consider a basic vehicle scheduling problem that arises in the context of travel demand models: Given demanded vehicle trips, what is the minimal number of vehicles needed to fulfill the demand? In this paper, we model the vehicle scheduling problem as a network flow problem. Since instances arising in the context of travel demand models are often so big that the network flow model becomes intractable, we propose using a rolling horizon heuristic to split huge problem instances into smaller subproblems and solve them independently to optimality. By letting the horizons of the subproblems overlap, it is possible to look ahead to the demand of the next subproblem. We prove that composing the solutions of the subproblems yields an optimal solution to the whole problem if the overlap of the horizons is sufficiently large. Our experiments show that this approach is not only suitable for solving extremely large instances that are intractable as a whole, but it is also possible to decrease the solution time for large instances compared to a comprehensive approach.

In this paper we present a method to construct a periodic timetable from a tactical planning perspective. We aim at constructing a timetable that is feasible with respect to infrastructure constraints and minimizes average perceived passenger travel time. In addition to in-train and transfer times, our notion of perceived passenger time includes the adaption time (waiting time at the origin station). Adaption time minimization allows us to avoid strict frequency regularity constraints and, at the same time, to ensure regular connections between passengers’ origins and destinations. The combination of adaption time minimization and infrastructure constraints satisfaction makes the problem very challenging. The described periodic timetabling problem can be modelled as an extension of a Peri- odic Event Scheduling Problem (PESP) formulation, but requires huge computing times if it is directly solved by a general-purpose solver for instances of realistic size. In this paper, we propose a heuristic approach consisting of two phases that are executed iteratively. First, we solve a mixed-integer linear program to determine an ideal timetable that mini- mizes the average perceived passenger travel time but neglects infrastructure constraints. Then, a Lagrangian-based heuristic makes the timetable feasible with respect to infras- tructure constraints by modifying train departure and arrival times as little as possible. The obtained feasible timetable is then evaluated to compute the resulting average per- ceived passenger travel time, and a feedback is sent to the Lagrangian-based heuristic so as to possibly improve the obtained timetable from the passenger perspective, while still respecting infrastructure constraints. We illustrate the proposed iterative heuristic approach on real-life instances of Netherlands Railways and compare it to a benchmark approach, showing that it finds a feasible timetable very close to the ideal one.

In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times. In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers’ perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers’ origins and destinations. We model the problem as a mixed integer quadratic program and linearise it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the type of solutions found by our approach on two case studies based on the Dutch railway network and analyse trade-offs that are made to balance dwell times and regularity of trains.

In high-speed railway operations, a trip sequence plan is made once the timetable is determined, and serves as a reference in the subsequent operations of train units scheduling. In light of the maintenance requirements of train units and periodicity characteristics of trip sequences, we introduce a trip sequence graph to describe the train units’ movement and coupling/splitting in a railway network. Based on the trip sequence graph, two integer linear programming models are then formulated, namely a path-based model and an arc-based model. Integrated with the characteristics of the trip sequence graph, a customized branch-and-price algorithm is developed to solve the path-based model. The two models are applied to the high-speed railway network in eastern China, and through numerical experiments, the effectiveness and applicability of the models are discussed.

Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that passenger distribution on routes can be modeled with a discrete choice model, this has not been considered in timetabling yet. We investigate how a passenger distribution can be integrated into an optimization framework for timetabling and present two mixed-integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices, the other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS).

We compare different evaluation functions that are all designed to mea- sure the quality of a timetable from passengers’ perspective. Already in small examples fundamentally different timetables can be preferred by evaluation functions that seem to be similar. To investigate this effect in practice, we design a set of evaluation functions as representatives for a wide range of commonly used evaluation functions in optimization models, evaluation applications, or choice models. These functions are compared by analyzing their evaluation values of multiple timetables in three case studies. To investigate to what extent these evaluation functions agree on a good or a bad timetable, we apply cluster analysis as well as a novel methodology to quantify the similarity of pairs of evaluation functions based on the values they yield on different timetables. We empirically show that the choice of the evaluation function can have a significant impact on the assessed quality of timetables, and thus also on which timetable is considered optimal, even though all evaluation functions are meant to evaluate the same - the quality of a timetable from passengers’ perspective. Due to the structure of the designed evaluation functions, it is further possible to identify which components of the func- tions influence the results of an evaluation and under which conditions they this is most pronounced. This can be very beneficial when designing timetable evaluation functions for passengers.

Item does not contain fulltext, Background: Exercise effects in cancer patients often appear modest, possibly because interventions rarely target patients most in need. This study investigated the moderator effects of baseline values on the exercise outcomes of fatigue, aerobic fitness, muscle strength, quality of life (QoL), and self-reported physical function (PF) in cancer patients during and post-treatment. Methods: Individual patient data from 34 randomized exercise trials (n = 4519) were pooled. Linear mixed-effect models were used to study moderator effects of baseline values on exercise intervention outcomes and to determine whether these moderator effects differed by intervention timing (during vs post-treatment). All statistical tests were two-sided. Results: Moderator effects of baseline fatigue and PF were consistent across intervention timing, with greater effects in patients with worse fatigue (Pinteraction = .05) and worse PF (Pinteraction = .003). Moderator effects of baseline aerobic fitness, muscle strength, and QoL differed by intervention timing. During treatment, effects on aerobic fitness were greater for patients with better baseline aerobic fitness (Pinteraction = .002). Post-treatment, effects on upper (Pinteraction < .001) and lower (Pinteraction = .01) body muscle strength and QoL (Pinteraction < .001) were greater in patients with worse baseline values. Conclusion: Although exercise should be encouraged for most cancer patients during and post-treatments, targeting specific subgroups may be especially beneficial and cost effective. For fatigue and PF, interventions during and post-treatment should target patients with high fatigue and low PF. During treatment, patients experience benefit for muscle strength and QoL regardless of baseline values; however, only patients with low baseline values benefit post-treatment. For aerobic fitness, patients with low baseline values do not appear to benefit from exercise during treatment.

A promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combini

One of the key assumptions of timetabling algorithms is that a solution exists that meets the pre-specified constraints, like driving times, transfer constraints and headway constraints. If this assumption is satisfied, in most cases a timetable can be found rapidly. Nowadays, railways are being used more intensively, which leads to a higher utilization of the network. Due to this increased utilisation, capacity conflicts occur, so that no feasible solution to the timetabling models can be found, without making subtle but non-trivial changes to the initial input. Resolving these conflicts is essential for railway companies with high utilization of infrastructure. In this paper, we consider infeasible timetabling instances together with a list of allowed modifications of the constraints. We iteratively identify local conflicts in these instances and resolve them by adapting some of the constraints, until there are no more conflicts. The adaptations of the constraints are changes in the right-hand sides that we try to make as small as possible but that resolve the infeasibility. We empirically show that our method can be improved by enriching the initial minimal conflicts found with more constraints. In order to keep the problems tractable, an iterative procedure is used to find solutions to subproblems corresponding to conflicts in the complete timetabling instance. In a case study on instances from the Dutch railway network, we show that these instances can be made feasible within a few minutes.

Contains fulltext : 170325.pdf (Publisher’s version ) (Open Access), This individual patient data meta-analysis aimed to evaluate the effects of exercise on quality of life (QoL) and physical function (PF) in patients with cancer, and to identify moderator effects of demographic (age, sex, marital status, education), clinical (body mass index, cancer type, presence of metastasis), intervention-related (intervention timing, delivery mode and duration, and type of control group), and exercise-related (exercise frequency, intensity, type, time) characteristics. Relevant published and unpublished studies were identified in September 2012 via PubMed, EMBASE, PsycINFO, and CINAHL, reference checking and personal communications. Principle investigators of all 69 eligible trials were requested to share IPD from their study. IPD from 34 randomised controlled trials (n=4519 patients) that evaluated the effects of exercise compared to a usual care, wait-list or attention control group on QoL and PF in adult patients with cancer were retrieved and pooled. Linear mixed-effect models were used to evaluate the effects of the exercise on post-intervention outcome values (z-score) adjusting for baseline values. Moderator effects were studies by testing interactions. Exercise significantly improved QoL (beta=0.15, 95%CI=0.10;0.20) and PF (beta=0.18, 95%CI=0.13;0.23). The effects were not moderated by demographic, clinical or exercise characteristics. Effects on QoL (betadifference_in_effect=0.13, 95%CI=0.03;0.22) and PF (betadifference_in_effect=0.10, 95%CI=0.01;0.20) were significantly larger for supervised than unsupervised interventions. In conclusion, exercise, and particularly supervised exercise, effectively improves QoL and PF in patients with cancer with different demographic and clinical characteristics during and following treatment. Although effect sizes are small, there is consistent empirical evidence to support implementation of exercise as part of cancer care.

A promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combining a truck and a drone gives rise to a new planning problem that is known as the Traveling Salesman Problem with Drone (TSP-D). This paper presents exact solution approaches for the TSP-D based on dynamic programming and provides an experimental comparison of these approach. Our numerical experiments show that our approach can solve larger problems than the mathematical programming approaches that have been presented in the literature thus far. Moreover, we show that restrictions on the number of locations the truck can visit while the drone is away can help significantly reduce the solution times while having relatively little impact on the overall solution quality.

In this paper, we develop a new framework for strategic planning purposes to calculate railway infrastructure occupation and capacity consumption in networks, independent of a timetable. Furthermore, a model implementing the framework is presented. In this model different train sequences are generated and assessed to obtain timetable independence. A stochastic simulation of delays is used to obtain the capacity consumption. The model is tested on a case network where four different infrastructure scenarios are considered. Both infrastructure occupation and capacity consumption results are obtained efficiently with little input. The case illustrates the model's ability to quantify the capacity gain from infrastructure scenario to infrastructure scenario which can be used to increase the number of trains or improve the robustness of the system.

This paper addresses the railway rolling stock rescheduling problem, while taking maintenance appointments into account. After a disruption, the rolling stock of the disrupted passenger trains has to be rescheduled to restore a feasible rolling stock circulation. Usually, a number of train units have a scheduled maintenance appointment during the day: these appointments must be taken into account while rescheduling the rolling stock. In this paper we propose three mixed-integer programming models for this purpose. All models are extensions of the composition model from the literature, which does not distinguish individual train units. The extra unit type model adds an additional rolling stock type for each train unit that requires maintenance. The shadow-account model keeps track of a shadow account for each train unit that requires maintenance. The job-composition model creates a path for each train unit such that the train units that require maintenance are on time for their maintenance appointments. All models are tested on instances of Netherlands Railways. The results showthat especially the shadow-account model and the job-composition model are effectively able to take maintenance appointments into account during real-time rescheduling. It depends on the characteristics of an instance whether the shadow-account model or the job-composition model performs best.

The correct identification of the source of a propagation process is crucial in many research fields. As a specific application, we consider source estimation of delays in public transportation networks. We propose two approaches: an effective distance median and a backtracking method. The former is based on a structurally generic effective distance-based approach for the identification of infectious disease origins, and the latter is specifically designed for delay propagation. We examine the performance of both methods in simulation studies and in an application to the German railway system, and we compare the results with those of a centrality-based approach for source detection..