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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Benders Decomposition for Robust Tactical Railway Crew Scheduling</title>
      <link>https://trid.trb.org/View/2646849</link>
      <description><![CDATA[We consider robust tactical crew scheduling for a large passenger railway operator that aims to inform crew early on about their work schedules while also maintaining the ability to respond to changes in the daily timetables. To resolve this conflict, the operator considers a template-based planning process, templates being time windows during which duties can later be scheduled. The goal is to select a cost-efficient set of templates that is robust with respect to uncertainty in the work to be performed in the operational phase. A set of templates is deemed robust when few excess duties are required to cover all work in the operational planning phase. To enable the construction of efficient template-based rosters, we impose several template rostering constraints that proxy the actual rostering rules of later planning steps. We propose a two-phase accelerated Benders decomposition algorithm that can incorporate these restrictions. Computational experiments on real-life instances from Netherlands Railways featuring up to 948 tasks per day show that historical planning information can be used to obtain robust templates and that sparse solutions can be obtained at negligible extra costs. Compared with a literature benchmark, our Benders decomposition method solves three times as many instances without rostering constraints to optimality. © © 2025, INFORMS.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646849</guid>
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    <item>
      <title>Learning to Learn HVAC Failures: Layering ML Experiments in the Absence of Ground Truth</title>
      <link>https://trid.trb.org/View/2407644</link>
      <description><![CDATA[Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two steps: 1. human-annotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT); 2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets.]]></description>
      <pubDate>Tue, 19 Aug 2025 15:55:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407644</guid>
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    <item>
      <title>An iterative heuristic for passenger-centric train timetabling with integrated adaption times</title>
      <link>https://trid.trb.org/View/1917868</link>
      <description><![CDATA[In this paper the authors present a method to construct a periodic timetable from a tactical planning perspective. The authors aim at constructing a timetable that is feasible with respect to infrastructure constraints and minimizes total perceived passenger travel time. In addition to in-train and transfer times, the 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 Periodic 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, the authors propose a heuristic approach consisting of two phases that are executed iteratively. First, the authors solve a mixed-integer linear program to determine an ideal timetable that minimizes the total perceived passenger travel time but neglects infrastructure constraints. Then, a Lagrangian-based heuristic makes the timetable feasible with respect to infrastructure constraints by modifying train departure and arrival times as little as possible. The obtained feasible timetable is then evaluated to compute the resulting total perceived 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. The authors 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. ]]></description>
      <pubDate>Tue, 17 May 2022 10:47:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1917868</guid>
    </item>
    <item>
      <title>A Local Search Algorithm for Train Unit Shunting with Service Scheduling</title>
      <link>https://trid.trb.org/View/1906727</link>
      <description><![CDATA[In this paper the authors consider the train unit shunting problem extended with service task scheduling. This problem originates from Dutch Railways, which is the main railway operator in the Netherlands. Its urgency stems from the upcoming expansion of the rolling stock fleet needed to handle the ever-increasing number of passengers. The problem consists of matching train units arriving on a shunting yard to departing trains, scheduling service tasks such as cleaning and maintenance on the available resources, and parking the trains on the available tracks such that the shunting yard can operate conflict-free. These different aspects lead to a computationally extremely difficult problem, which combines several well-known NP-hard problems. In this paper, the authors present the first solution method covering all aspects of the shunting and scheduling problem. They describe a partial order schedule representation that captures the full problem, and they present a local search algorithm that utilizes the partial ordering. The proposed solution method is compared with an existing mixed integer linear program in a computational study on realistic instances provided by Dutch Railways. The authors show that their local search algorithm is the first method to solve real-world problem instances of the complete shunting and scheduling problem. It even outperforms current algorithms when the train unit shunting problem is considered in isolation, that is, without service tasks. Although the authors' method was developed for the case of the Dutch Railways, it is applicable to any shunting yard or service location, irrespective of its layout, that uses self-propelling train units and that does not have to handle passing trains.]]></description>
      <pubDate>Thu, 10 Feb 2022 17:05:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1906727</guid>
    </item>
    <item>
      <title>Increasing the effectiveness of the capacity usage at rolling stock service locations</title>
      <link>https://trid.trb.org/View/1905318</link>
      <description><![CDATA[Trains consist of one or more railway vehicles called rolling stock, which need interior and exterior cleaning and small technical checks on a daily basis. These services are executed at service locations (SLs). Scheduling rolling stock servicing tasks during an operational day is important to guarantee the fulfilment of servicing deadlines. Public transport companies face large scheduling problems, especially those with 24-hour-a-day operation. The expected increase in transport frequencies enhances the need for improving scheduling servicing tasks during an operational day. Therefore, the Rolling Stock Servicing Scheduling Problem (RS-SSP) is modelled comprising a mixed integer linear programming (MILP) model. Complying with the planned timetable, the RS-SSP maximises the RS units being serviced during daytime. The RS-SSP allows RS exchanges between RS units having completed servicing and operating RS units requiring servicing. Due to this RS Exchange Concept, the number of RS units visiting the SL during daytime can be increased. The proposed RS-SSP model has been tested on a real-life case from the Dutch railways. For multiple scenarios, the model was able to exchange all running RS. Consequently, the capacity usage at SLs can be increased by the RS-SSP by shifting some of the excessive workload to daytime, and thus solving the capacity shortages.]]></description>
      <pubDate>Thu, 10 Feb 2022 17:05:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1905318</guid>
    </item>
    <item>
      <title>Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance</title>
      <link>https://trid.trb.org/View/1897090</link>
      <description><![CDATA[Netherlands Railways has developed driver advisory systems (DAS) to provide the train driver with route context information and coasting advice in order to benefit punctuality and energy efficiency. However, the impact of these DAS on human factors aspects and safety performance is unclear. The current study assesses the impact of two DAS levels (route context information and coasting advice) on mental workload, attention allocation and safety performance, using eye tracking, a subjective mental workload rating scale (RSME) and simulator data. The overall findings suggest that the application of DAS levels has no negative impact on safety performance and attention allocation towards the trackside compared to a control condition with static timetable information. Furthermore, safety performance benefits significantly from DAS with route context information. DAS were originally developed to benefit punctuality and energy efficiency goals. This study implicates that DAS can also benefit safety performance. The current study found that DAS could decrease workload when the functionalities meet the requirements of the situation. The possible presence of mental underload and its effect on driving performance should be taken into consideration when implementing DAS. It is essential in the development of DAS that it meaningfully enriches the train driving task in stead of simply increasing mental workload.]]></description>
      <pubDate>Tue, 21 Dec 2021 09:56:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1897090</guid>
    </item>
    <item>
      <title>The Maintenance Location Choice Problem for railway rolling stock</title>
      <link>https://trid.trb.org/View/1884671</link>
      <description><![CDATA[Due to increasing railway use, the capacity at railway yards and maintenance locations is becoming limiting to accommodate existing rolling stock. To reduce capacity issues at maintenance locations during nighttime, railway undertakings consider performing more daytime maintenance, but the choice at which locations personnel needs to be stationed for daytime maintenance is not straightforward. Among other things, it depends on the planned rolling stock circulation and the maintenance activities that need to be performed. This paper presents the Maintenance Location Choice Problem (MLCP) and provides a Mixed Integer Linear Programming model for this problem. The model demonstrates that for a representative rolling stock circulation from The Netherlands Railways a substantial amount of maintenance activities can be performed during daytime. Also, it is shown that the location choice delivered by the model is robust under various time horizons and rolling stock circulations. Moreover, the running time for optimizing the model is considered acceptable for planning purposes.]]></description>
      <pubDate>Wed, 17 Nov 2021 14:27:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1884671</guid>
    </item>
    <item>
      <title>A Variable Neighborhood Search Heuristic for Rolling Stock Rescheduling</title>
      <link>https://trid.trb.org/View/1768287</link>
      <description><![CDATA[The authors present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, duties, i.e., sequences of trips, must be assigned to the available train units in such a way that both passenger comfort and operational performance are taken into account. For their heuristic, the authors introduce three neighborhoods, which focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips, respectively. These neighborhoods are used for both a Variable Neighborhood Descent local search procedure and for perturbing the current solution in order to escape from local optima. Moreover, the authors show that the heuristic can be extended to the setting of flexible rolling stock turnings at ending stations by introducing a fourth neighborhood. The authors apply their heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions within one minute of solving time. This allows rolling stock dispatchers to use the authors' heuristic in real-time rescheduling.]]></description>
      <pubDate>Mon, 22 Mar 2021 10:34:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1768287</guid>
    </item>
    <item>
      <title>Give Train Passengers Something to Do!</title>
      <link>https://trid.trb.org/View/1729507</link>
      <description><![CDATA[Give train passengers something to do! Train Operating Companies (TOC) are keen to attract new customers. The way to be successful is high performance and make customers happy. As part of its strategy, at the NS (the Dutch Railways) the customer comes first and passenger satisfaction is now the number one target and performance indicator. NS supports this ambition with a wealth of insights in the drivers of passenger satisfaction. The key challenge is how to successfully convert these insights into action; meaningful innovation that drives successful business. The authors already know that a happy customer is a loyal customer (Van Hagen, de Bruyn &Ten Elsen, 2016) and emotions play a key role in satisfaction. NS found out that train passengers make decisions on three core emotions which drives behaviour: feeling in control, feeling appreciated and experiencing freedom. The combined set of principles – three per core need (control, appreciation, freedom) – are linked to nine stages of the customer journey making up the companies ‘Customer Experience Innovation Framework’ (Van Hagen & van der Made, 2017). Based on the innovation framework, this paper describes three new initiatives which NS initiated with different design partners and in which the focus is on the core emotions of appreciation and freedom: 1. Travel yourself fit For the Dutch Design Week of 2017, the Dutch design studio Enrichers developed for NS an interactive furniture collection, ranging from water floors (Floatile) to 'surf' on the movements on the train, a water sofa (Bambata) to increase conversations among travelers and a moving cushion (Macaron) to facilitate sweat-less workouts. In carriage heading for Eindhoven passengers could use the furniture to get fitter. The aim of the project was to influence the mood of passengers in a positive way using the moving furniture in the train at service. 2. Mindfulness At the same moment but on another train to Eindhoven, Studio Kaptijn has given passengers the possibility to use an app with tips and instructions for meditation. Goal of the mindfulness app was to cool passengers down from daily stress and make them relax by using the app. 3. Station language During the Dutch Design Week of 2018, Wouter Corvers en Bouke Bruins have used 65.000 magnetic letters which passengers could use to make words, sentences or poetry on steel parts of Eindhoven Central station. Goal was to kill the waiting time and to connect people with each other. Using the methodology of evidence based design, all three projects have been tested in a life environment with customers. The results of all three projects were very positive and recommendations are to use this kind of interventions more often to surprise the passengers and influence emotions in a positive way and enhance the image of the Netherlands Railways.]]></description>
      <pubDate>Thu, 27 Aug 2020 11:08:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1729507</guid>
    </item>
    <item>
      <title>The stochastic maintenance location routing allocation problem for rolling stock</title>
      <link>https://trid.trb.org/View/1713432</link>
      <description><![CDATA[The authors study a problem where– given a railway network and different fleets– we have to locate maintenance locations and allocate the fleets to these locations. The allocation of fleets to the maintenance locations complicates the maintenance location routing problem. For each candidate location different facility sizes can be chosen and for each size there is an associated annual facility cost that captures the economies of scale in facility size. Because of the strategic nature of facility location, these facilities should be able to handle changes such as adjustments to the line plan and the introduction of new rolling stock types. The authors capture these changes by discrete scenarios and they formulate this two-stage stochastic problem as a mixed integer problem. Furthermore, the authors  perform a case study with the Netherlands Railways that provides novel managerial insights by showing that the number of opened maintenance facilities highly depends on the allocation restrictions.]]></description>
      <pubDate>Tue, 30 Jun 2020 17:41:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1713432</guid>
    </item>
    <item>
      <title>Interactions and Equilibrium Between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study</title>
      <link>https://trid.trb.org/View/1709326</link>
      <description><![CDATA[In the last decade, optimization models for railway traffic rescheduling mostly focused on incorporating an increasing detail of the infrastructure, with the goal of proving feasibility and quality from the point of view of the managers of the infrastructure (tracks and stations). Different approaches that manage only the passenger flows instead focus more explicitly on the quality of service perceived by the passengers. This paper investigates microscopic railway traffic optimization models and algorithms, merging these two streams of research. In particular, the authors analyze the characterization of an equilibrium point between the reordering choices of train dispatchers in railway traffic optimization and the route choice of passengers in the available services of the railway transport network. The authors describe how passenger choice at stations along the route intertwines deeply with the problem of rescheduling trains over tracks and station resources in a very complicated setting that might not exhibit equilibrium points in general. Delaying trains and/or dropping passenger connections and/or giving particular route advice to passengers might influence the behavior of traffic controllers and passengers, determining a trade-off between the delays of trains, weighted by the passenger load, and the travel time of passengers. The authors study this problem with a game theoretical approach, focusing on the solutions corresponding to Nash equilibria of a game involving passengers and infrastructure managers. The proposed game theoretical approach is able to easily consider information and interdependence of the actions of multiple stakeholders. Computational results based on a real-world Dutch railway network quantify the trade-off between the minimization of train delays and passenger travel times and the performance, stability, and convergence of the equilibrium point given different algorithms and information available. The final aim of this work is to study the impact of effective implementations of railway traffic management and dissemination of information to passengers and operators.]]></description>
      <pubDate>Fri, 26 Jun 2020 09:51:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1709326</guid>
    </item>
    <item>
      <title>Reducing Passenger Delays by Rolling Stock Rescheduling</title>
      <link>https://trid.trb.org/View/1709323</link>
      <description><![CDATA[Delays are a major nuisance to railway passengers. The extent to which a delay propagates, and thus affects the passengers, is influenced by the assignment of rolling stock. The authors propose to reschedule the rolling stock in such a way that the passenger delay is minimized and such that objectives on passenger comfort and operational efficiency are taken into account. The authors refer to this problem as the passenger delay reduction problem. The authors propose two models for this problem, which are based on two dominant streams of literature for the traditional rolling stock rescheduling problem. The first model is an arc formulation of the problem, whereas the second model is a path formulation. The authors test the effectiveness of these models on instances from Netherlands Railways (Nederlandse Spoorwegen). The results show that the rescheduling of rolling stock can significantly decrease passenger delays in the system. Especially, allowing flexibility in the assignment of rolling stock at terminal stations turns out to be effective in reducing the delays. Moreover, the authors show that the arc formulation?based model performs best in finding high-quality solutions within the limited time that is available in the rescheduling phase.]]></description>
      <pubDate>Fri, 26 Jun 2020 09:51:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1709323</guid>
    </item>
    <item>
      <title>Economies of scale in recoverable robust maintenance location routing for rolling stock</title>
      <link>https://trid.trb.org/View/1654836</link>
      <description><![CDATA[The authors consider the problem of locating maintenance facilities in a railway setting. Different facility sizes can be chosen for each candidate location and for each size there is an associated annual facility cost that can capture economies of scale in facility size. Because of the strategic nature of facility location, the opened facilities should be able to handle the current maintenance demand, but also the demand for any of the scenarios that can occur in the future. These scenarios capture changes such as changes to the line plan and the introduction of new rolling stock types. The authors allow recovery in the form of opening additional facilities, closing facilities, and increasing the facility size for each scenario. The authors provide a two-stage robust programming formulation. In the first-stage, one decides where to open what size of facility. In the second-stage, one solves a NP-hard maintenance location routing problem. The authors reformulate the problem as a mixed integer program that can be used to make an efficient column-and-constraint generation algorithm. To show that their algorithm works on practical sized instances, and to gain managerial insights, the authors perform a case study with instances from the Netherlands Railways. A counter intuitive insight is that economies of scale only play a limited role and that it is more important to reduce the transportation cost by building many small facilities, rather than a few large ones to profit from economies of scale.]]></description>
      <pubDate>Wed, 18 Dec 2019 15:46:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1654836</guid>
    </item>
    <item>
      <title>Optimal infrastructure capacity of automated on-demand rail-bound transit systems</title>
      <link>https://trid.trb.org/View/1654831</link>
      <description><![CDATA[Fully-automated services potentially allow for greater flexibility in operations and lower marginal operational costs. The objective of this study is to determine the capacity requirements of an envisaged automated on-demand rail-bound transit system which offers a direct non-stop service. An optimization model is formulated for determining the optimal track and station platform capacities for an on-demand rail transit system so that passenger, infrastructure and operational costs are minimized. The macroscopic model allows for studying the underlying relations between technological, operational and demand parameters, optimal capacity settings and the obtained cost components. The model is applied to a series of numerical experiments followed by its application to part of the Dutch railway network. The performance is benchmarked against the existing service, suggesting that in-vehicle times can be reduced by 10% in the case study network while the optimal link and station capacity allocation is comparable to those currently available in the case study network. While network geometry and demand distribution are always the underlying determinants of both service frequencies and in-vehicle times, line configuration is only a determinant in the conventional system, whereas the automated on-demand rail service better caters for the prevailing demand relations, resulting in greater variations in service provision. A series of sensitivity analyses are performed to test the consequences of a range of network structures, technological capabilities, operational settings, cost functions and demand scenarios for future automated on-demand rail-bound systems.]]></description>
      <pubDate>Wed, 18 Dec 2019 15:46:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1654831</guid>
    </item>
    <item>
      <title>An adjustable robust optimization approach for periodic timetabling</title>
      <link>https://trid.trb.org/View/1653967</link>
      <description><![CDATA[In this paper, the authors consider the Robust Periodic Timetabling Problem (RPTP), the problem of designing a periodic timetable that can easily be adjusted in case of small periodic disturbances. The authors develop a solution method for a parametrized class of uncertainty regions. This class relates closely to uncertainty regions known in the robust optimization literature, and naturally defines a metric for the robustness of the timetable. The proposed solution method combines a linear decision rule with well-known reformulation techniques and cutting-plane methods. The authors show that the RPTP can be solved for practical-sized instances by applying the solution method to practical cases of Netherlands Railways (NS). In particular, the authors show that the trade-off between the efficiency and robustness of a timetable can be analyzed using their solution method.]]></description>
      <pubDate>Tue, 15 Oct 2019 17:07:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1653967</guid>
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