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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>Automated prognostics and diagnostics of highspeed railway faults using machine learning approaches</title>
      <link>https://trid.trb.org/View/2700580</link>
      <description><![CDATA[This study explores machine learning approaches to predict multi-facet faults affecting operations of a railway network. Our study has established machine learning models and then benchmarked their performance including Extreme Gradient Boosting, Random Forest, and SVM. Field datasets have been collected over 6 years in collaboration with train operating companies and the infrastructure manager for operational, infrastructural, and environmental features. The main emphasis is placed on the high-speed railway network (linking three airports) in Thailand. Our research ensures robust model development through data preprocessing, data transformation, and then hyperparameter tuning. Our new results reveal that Extreme Gradient Boosting's superior predictive capability is evident, which can be attributed to its effective handling of non-linearity, feature interactions. The results also highlight the significant possibilities of the machine learning in proactive maintenance and mitigating risks. Additionally, our study is the first to explore the complex intercorrelation among vast amounts of historical accident data, recognising the intricacy that arises from technical, human-related, and environmental elements. Although some existing machine learning methods show promising outcomes, they encounter challenges when it comes to generalising and categorising complex data. Our study demonstrates the necessity of implementing innovative techniques to reveal intricate relationships in the accident data. This new approach results in improved prediction accuracy and contributes to creating a railway system that is safer and more reliable.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700580</guid>
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    <item>
      <title>An explainable framework integrating machine learning and life cycle assessment for balancing performance, carbon emissions, and costs in asphalt mixtures</title>
      <link>https://trid.trb.org/View/2702921</link>
      <description><![CDATA[This study proposed an optimization method for Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied the optimized NSGA-II to the multi-objective optimization problem involving the performance, carbon emissions, and costs of asphalt mixtures. Firstly, the Marshall stability (MS) predictive equation of asphalt mixtures was established. By leveraging life cycle assessment (LCA), the asphalt mixtures were partitioned into three stages: raw materials production, transportation, and mixtures mixing. The carbon emissions and costs calculation objective functions for asphalt mixtures have then been constructed. Subsequently, a hybrid machine learning model combining the NSGA-II and Cauchy distribution crossover operator (NSCCGA-II) was developed to address the issue of easily getting trapped in local optima that exists in NSGA-II. Through multiple evaluation indicators, it is demonstrated that the solving performance of NSCCGA-II is superior to that of NSGA-II. The solutions of the balanced design model were obtained and evaluated. The findings indicated that the MS of the acquired Pareto solution set are all above 11kN, the carbon emissions are all below 57 kg, and the costs are all below 595RMB. When the weights of the three optimization goals are set to be equally important, the basalt fiber (BF) content, nano-TiO2/CaCO3 (NTC) content, and asphalt-aggregate ratio of the top-ranked Pareto solution are 3.93%, 5.57%, and 4.35%, respectively. The corresponding MS, carbon emissions, and costs are 12.22kN, 53.62kg, and 558.80RMB, respectively. Finally, the importance analysis of Gradient Boosting Regression (GBR) and Shapley Additive Explanation (SHAP) indicated that asphalt-aggregate ratio is of the highest importance to MS and is much higher than BF content and NFT content.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702921</guid>
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    <item>
      <title>Towards timetable redesign : from path insertions for train rerouting to an entirely new timetabling process</title>
      <link>https://trid.trb.org/View/2752095</link>
      <description><![CDATA[Railway timetables are constructed yearly and contain the plan for the complete subsequent year. However, over the course of that year, they have to be modified frequently. A common cause is the closure of a line due to maintenance works, but also disruptions can cause railway lines to be closed for multiple days. In these scenarios, regional trains are often partially cancelled and replaced by buses, while it is preferable to keep long-distance trains running by rerouting them. In order to reroute a train, planners must change its schedule - also known as a train path. At the moment, train paths are altered manually. We propose algorithms that help to automate these procedures. These algorithms also have other use cases for an infrastructure manager or a railway undertaking: they can add or request an ad-hoc train path, or verify when capacity is still available.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:37:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752095</guid>
    </item>
    <item>
      <title>Deep learning methods in transportation and urban planning : advancing data collection and inference methods</title>
      <link>https://trid.trb.org/View/2752087</link>
      <description><![CDATA[Rapid urban development and the overwhelming amount of data provided by connected sensors, mobile devices, and open-source datasets challenge traditional transport-analysis methodologies. These methodologies largely depend on rigid mathematical models, static assumptions, and sparse measurements. This thesis demonstrates how deep learning (DL) has the potential to build a systematically better data collection and inference capability across three critical domains of the LUTI cycle: traffic management, population synthesis, and workplace location choice. Through five research papers, this thesis demonstrates that DL methods can complement or outperform traditional approaches by extracting more comprehensive data, building better predictive models, and providing actionable implications for planners. The analysis is separated into two main themes: data acquisition and analytical inference. The data acquisition theme proposes methods for transforming overlooked or missing data into valuable input for transport models. Overall, this thesis makes four main contributions to the literature: (1. Identifying vehicle-mounted cameras and incomplete surveys as valid, high-resolution sources of data when combined with DL pipelines; (2. Extending traffic state estimation to use partial trajectories extracted from video sequences to extract useful traffic states by means of GA-calibrated CTMs; (3. Improving synthetic population generation, providing evidence that GANs can satisfy future marginal constraints and learn from sparse, masked data; and (4. Generalising deep choice modelling to thousand-alternative examples, showing that DNNs can equal or exceed traditional discrete-choice models in accuracy and behavioural realism.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752087</guid>
    </item>
    <item>
      <title>Automatic design of safe, high performance and compact deep learning models for autonomous vehicles, AutoDeep</title>
      <link>https://trid.trb.org/View/2752080</link>
      <description><![CDATA[Deep Neural Networks (DNN) are increasingly being used to support decision-making in autonomous vehicles. While DNN holds the promise of delivering valuable results in safety-critical applications, broad adoption of DNN systems will rely heavily on how the computation intensive DNN could be customized and deployed on the resource-limited vehicle embedded hardware platform and also how much to trust their outputs. High DNN accuracy comes at high computations, storage, and memory bandwidth requirements, which makes their deployment particularly challenging, especially for vehicle embedded computing platforms. Therefore, an efficient method is required to optimize the network complexity by efficiently exploring the design space. In addition, to trust a decision made by a DNN in an autonomous vehicle, we need assurance that it is robust in its computations and predictions and will cause no harm in real-world unintended data perturbations. There have been a lot of research on improving the accuracy of DNNs. However, nowadays, building just for accuracy is not sufficient as the DNN's main design concern. We must know how to design a compact and accurate DNN, how to optimize the computation-intensive DNN efficiently, and also how to model and evaluate noise and improve the DNN robustness for safety-critical autonomous vehicle applications. In this project we develop an automatic framework called AutoDeep to achieve performance, compactness, and robustness in design and customization of DNN for safety-critical applications such as intention detection and behavior prediction for road and construction autonomous vehicles.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752080</guid>
    </item>
    <item>
      <title>iDecide</title>
      <link>https://trid.trb.org/View/2752078</link>
      <description><![CDATA[To safely maneuver a heavy vehicle in a complex traffic situation is a non-trivial task, where the driver must be able to detect, track and predict the motion of multiple surrounding road-users, and based on that uncertain information decide the future motion of its own vehicle. This is true for both a human driver and an autonomous system. For an autonomous vehicle to be able to master complex traffic situations potentially involving multiple road-users, e.g., roundabouts, intersections, crossings, lane-changes, in a safe and efficient way, the vehicle needs to first be able to interpret the current traffic scene. Then, based on that interpretation and its associated uncertainty, it has to make rational and safe decisions both of discrete nature as well as of continuous nature which together form the future motion of the vehicle. The focus in this project has been on the latter of these challenges. The overall objective of this project has been to develop a joint decision-making and motion-planning mechanism that enables an autonomous heavy vehicle (heavy-duty truck or bus) to operate in urban and suburban traffic in a safe, trustworthy and efficient way while not exceeding the computational budget. The system should take uncertainties into account in the predictions of other road users' future behavior, multiple road-users (including vulnerable), and traffic rules.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752078</guid>
    </item>
    <item>
      <title>Predictive monitoring for autonomous trucks, PreMAT : concluding report</title>
      <link>https://trid.trb.org/View/2752077</link>
      <description><![CDATA[The project objective was to investigate how the total risk level during testing of automated vehicles under development can be reduced, without affecting the vehicle's own system. Key research questions: - How can we guarantee that an autonomous vehicle never exits a predefined physical space? - How can we prevent that a pedestrian is hit by a test vehicle in the test area? - What are the requirements on a surveillance system to reach an adequate safety level? A successful outcome of the research would be an outline for an automated surveillance system that enables testing of automated vehicles in early development phases as well as high testing uptime being less dependent on human supervision. Three surveillance functions were investigated: (i) Dynamic geofence; (ii) Pedestrian detection; (iii) Anomaly detection The first two were proven as concepts, including a safety requirement analysis. Within the Dynamic geofence concept, two research papers were published. The Anomaly detection did not reach a proof of concept but indicated a good potential to do so.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752077</guid>
    </item>
    <item>
      <title>Contributions to branch-and-price methods for electric vehicle routing problems</title>
      <link>https://trid.trb.org/View/2752064</link>
      <description><![CDATA[The Vehicle Routing Problem (VRP) is a fundamental combinatorial optimization problem concerned with determining cost-efficient routes for a fleet of vehicles serving a set of customers under operational constraints. In recent years, the electrification of transportation has led to the emergence of the Electric Vehicle Routing Problem (EVRP), where routing decisions must account for battery charging requirements and energy-related constraints. These additional considerations significantly increase the complexity of the problem. This thesis studies exact solution approaches for the EVRP based on branch-and-price algorithms, the state-of-the-art methodology for solving large-scale vehicle routing problems to optimality. Branch-and-price combines branch-and-bound with column generation, where the master problem is solved using linear programming relaxation and new columns (routes) are generated dynamically by solving a pricing problem. For the EVRP, the pricing problem takes the form of an elementary shortest path problem with resource constraints, which is typically solved using labeling algorithms.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752064</guid>
    </item>
    <item>
      <title>Stochastic and learning-based control strategies for electric autonomous mobility systems</title>
      <link>https://trid.trb.org/View/2752048</link>
      <description><![CDATA[Electric Autonomous Mobility-on-Demand (E-AMoD) systems offer a path toward sustainable urban transportation through the coordinated operation of shared, zero-emission autonomous vehicles. Yet their deployment poses difficult operational challenges: fleet rebalancing, vehicle routing, and charging must be managed jointly, under uncertainty, and within tight computational budgets. A central argument of this thesis is that no single decision-making paradigm suffices. Optimization-based methods are well suited for strategic fleet control where uncertainty guarantees and feedback are essential, while learning-based methods become necessary at finer operational scales where real-time optimization is computationally prohibitive.Three contributions are presented, each targeting a different operational level. The first introduces a chance-constrained model predictive control (MPC) framework for station-level fleet rebalancing, combining Gaussian Process Regression for probabilistic demand forecasting with a hierarchical architecture that separates strategic rebalancing from tactical matching. The second extends this framework to electric fleets operating under multiple interacting uncertainties, employing a tailored Nested Benders Decomposition to maintain metropolitan-scale tractability without sacrificing MPC's receding-horizon feedback. The third contribution shifts to node-level electric dial-a-ride routing, including pickup-delivery sequencing, time windows, and ride-time constraints, and proposes a deep reinforcement learning approach built on a Graph Edge Attention Network capable of handling hundreds of requests with second inference times. Taken together, the three contributions show that optimization and learning serve complementary roles in E-AMoD operations, with the appropriate paradigm determined by the granularity and real-time demands of the problem at hand.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752048</guid>
    </item>
    <item>
      <title>Ship surrogate modelling and voyage optimisation for short-sea shipping fuel efficiency</title>
      <link>https://trid.trb.org/View/2752047</link>
      <description><![CDATA[Short-sea shipping (SSS), essential for European transport logistics, is increasingly challenged by strict environmental regulations such as the EU Emissions Trading System, fuel price volatility, and the need to maintain tight schedules on short voyages. This thesis reframes SSS operations as a data-driven voyage-optimisation problem, developing an integrated framework that combines operational ship data, metocean data, machine learning (ML) models, and advanced optimisation algorithms to min imise voyage fuel consumption and emissions while meeting estimated time of arrival targets. The framework was developed through the two interconnected fields of modelling and optimisation. (1. Modelling established surrogate models for ship performance: multiple ML regression algorithms were benchmarked for fuel consumption predic tion, with XGBoost identified as the most stable and reliable. Independently, Gaus sian process regression was employed to estimate added resistance in head waves for model-scale ships. When integrated into a grey-box neural network fuel model, it reduced prediction errors by a factor of 3 relative to semi-empirical methods. (2. Op timisation involved introducing a methodology to optimise the total fuel consumption of a voyage, validated across two case-study ships. For a double-ended ferry, a complete decision-support system using Bayesian optimisation (BO) was implemented to determine the optimal power profile across an entire voyage. This achieved simulated fuel savings of up to 43%, with an 18% reduction confirmed during full-scale sea trials. For longer SSS voyages, the framework was extended to a chemical tanker case study. A metocean-aware segmentation algorithm, MS-PELT, was developed to divide routes into operationally meaningful legs, outperforming state-of-the-art methods whilst enabling near-real-time application. Voyage optimisation was subsequently performed using parallel coupled dynamic programming (PCDP), achieving up to 14.8% fuel savings. A final refinement step combining PCDP and BO achieved a potential fuel saving of 9.3% relative to measured voyage fuel consumption.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752047</guid>
    </item>
    <item>
      <title>Efficient tactical decision making for trucks in highway traffic with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2752045</link>
      <description><![CDATA[This thesis investigates tactical decision making for autonomous heavy-duty trucks in highway traffic using deep reinforcement learning, with a particular emphasis on optimizing safety, efficiency and costs. The key aspects of decision making include Adaptive Cruise Control (ACC) and lane changes, which strongly influence energy consumption, travel time, and traffic interactions. To support a systematic study of this problem, we develop a scalable traffic model on a simulation platform, providing a controlled and extensible environment for autonomous truck driving in multi-lane highways. We propose a hierarchical control architecture in which reinforcement learn ing is used for high-level tactical decision making, while low-level tactical actions are handled by physics-based controllers. This separation is found to improve the performance by reducing safety risks and facilitates the integra tion of learning-based decision making with established control methods. A realistic reward function is designed to jointly capture safety, efficiency, and operational costs, and advanced training strategies such as curriculum learning are investigated to handle conflicting objectives within a scalarized framework. We further explore a multi-objective reinforcement learning formulation to explicitly represent trade-offs between competing objectives, enabling the learning of interpretable Pareto frontiers. The results demonstrate that learning based tactical decision making policies can achieve meaningful trade-offs between safety and various operational costs in abstracted highway scenarios, and that multi-objective formulations provide valuable insight into the structure of these trade-offs. Overall, this work contributes to methodological foundations and evaluation tools for economically meaningful and extensible learning-based tactical decision making for heavy-duty trucks]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752045</guid>
    </item>
    <item>
      <title>Optimal control methods in charge- and trip-planning for electric vehicles</title>
      <link>https://trid.trb.org/View/2752044</link>
      <description><![CDATA[The transport sector is a major contributor to global greenhouse gas emissions, prompting increasingly stringent regulations and accelerating the transition toward electric mobility. Although electric vehicles (EVs) offer significant potential for emission reduction, their large-scale adoption is still hindered by range anxiety, i.e. the fear of the battery running out before a charging station is reached. Intelligent charge- and trip-planning (ICTP) is a way to address range anxiety by optimizing the charging station selection, the vehicle's energy consumption, the battery thermal management, and the charging process. However, the resulting problems are typically large-scale, nonlinear, and mixed-integer, which makes them computationally challenging to solve. This thesis develops optimal control methods to solve the ICTP problem in a computationally efficient way, to allow real-time onboard implementation. First, the computational tractability of the ICTP problem is improved through tailored warm-start strategies and the relaxation of binary decision variables, enabling the use of faster continuous solvers and achieving substan tial reductions in computation time. Second, a semi-analytical optimal control solver based on Pontryagin's Maximum Principle is developed for EV charging optimization. The solver yields explicit control laws and its low computation time allows for real-time embedded implementation. Finally, a nonlinear optimal control framework for mission planning of long-range solar-powered EVs is proposed, enabling the joint optimization of trip time and energy management under spatio-temporal constraints. The method was tested on a solar-powered vehicle racing across the Australian Outback.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752044</guid>
    </item>
    <item>
      <title>Modelling, control, and optimization of fuel cell hybrid trucks</title>
      <link>https://trid.trb.org/View/2752030</link>
      <description><![CDATA[The heavy-duty freight sector finds itself in a state of change. Legislation and customer demand pushes the industry towards electrification, where the pure battery and fuel cell electric hybrid have emerged as the top contending technologies to replace the conventional diesel powertrain, and although pure battery powertrains dominate the light-duty sector, projections indicate that fuel cell hybrids will show superior performance in long-range missions with heavy cargo. Particularly so in the context of future autonomous vehicles, considering the potential for continuous, non-stop driving. Regardless, several techno-economic challenges remain before widespread adoption of either pure battery powertrains or fuel cell hybrids in the heavy-duty sector. These challenges include but are not limited to the weight of lithium cells, elevated hydrogen prices, insufficient recharging/refuelling infrastructure, durability, as well as thermal management related issues. A model-based approach can be used to target these challenges, which motivated the development of the Electrochemical Commercial Vehicle (ECCV) platform; a model library tailored for controls algorithm development and rapid virtual prototyping of electrified trucks. While auto-manufacturers use in-house and proprietary software to solve similar tasks, the open-source nature of the ECCV-platform allows for collaboration within academia and industry without issues relating to intellectual property rights, a type of collaboration which was demonstrated in a benchmark competition held at the IFAC World Congress 2023, where six teams from universities all over the world contributed their solutions to the fuel cell hybrid energy management problem.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752030</guid>
    </item>
    <item>
      <title>Travel demand estimation and network supply calibration for large-scale urban networks</title>
      <link>https://trid.trb.org/View/2752029</link>
      <description><![CDATA[Estimation of origin-destination (OD) vehicle flows and link capacity calibration are fundamental processes in transportation science, especially in the context of transport modelling and simulation. They ensure that transportation models accurately reflect real-world travel behaviour and network conditions. This thesis develops a simulation-based optimization algorithm for network-wide link capacity calibration. To address the high dimensionality of large-scale networks, the algorithm is integrated with partial least squares (PLS) regression, which reduces the number of variables and enhances computational efficiency. The algorithm is evaluated on an urban road network in Stockholm, Sweden, where it demonstrates feasibility and higher efficiency compared to the simultaneous perturbation stochastic approximation (SPSA) method. For large-scale OD estimation, this thesis advances the field in two main directions. First, it develops a data fusion framework that integrates multiple heterogeneous data sources, including mobile network data, link count observations, and turning proportion data. Second, it proposes several methods to enhance the computational efficiency of OD estimation in large urban networks. These include: (i) implementing data-driven network assignment (DDNA) using GPS data to construct a fixed OD-to-link mapping, thereby eliminating the need for iterative assignment within a bi-level optimization structure; (ii) applying non-negative matrix factorization (NNMF) for dimensionality reduction, which simplifies the optimization by reducing the number of variables; and (iii) developing a numerical solver based on an interior-point method that exploits structural properties of the assignment matrix, such as sparsity and linearity, to enhance computational performance. The proposed OD estimation methods are evaluated on real-world networks in central Stockholm and Norrköping, Sweden, demonstrating accurate and stable OD and link flow estimates with substantial gains in computational efficiency compared to solving the OD estimation problem without dimensionality reduction techniques or numerical solver improvement.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752029</guid>
    </item>
    <item>
      <title>Road vehicle energy demand predictions under uncertain operating conditions</title>
      <link>https://trid.trb.org/View/2752007</link>
      <description><![CDATA[While the literature on routing algorithms is extensive, the focus has merely been on defining the optimization problem and algorithm, often using simple energy consumption models. In contrast, research in range es timation relies on rather complicated energy consumption models, which are often derived from vehicle data. These models do, unfortunately, have poor transfer ability between different drivers, environmental conditions, and vehicles. A great effort has thus been undertaken to model these effects in isolation, for instance, the study of rolling resistance and air drag. Building on models like those, numerous complex complete vehicle simulation models have been developed with excellent accuracy in controlled environments, but at the cost of being too computationally expensive for in-vehicle use. Additionally, these models seldom quantify uncer tainty, a crucial parameter for preventing battery depletion. To this day, the uncertainty of a range estimate is most commonly inferred from data, sensitivity analyses, or empirical model parameters. Methods relying on data or sensitivity analyses generally impose a constant uncertainty, owing to the estimation methods adopted. In contrast, using a model-based approach, for instance, derived from empirical model parameters, has the advantage of cap turing dynamic characteristics that vary between transport missions. Notably, these parameters may not necessarily convey any physical meaning, but instead exist solely as internal elements of a black-box model. In contrast, by adopting a physical model-based approach, variations in energy demand can be derived from exogenous parameters like those obtained from weather, traffic, mission, and road information. This approach aligns precisely with that adopted in this thesis.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752007</guid>
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