<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <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>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>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>
    </item>
    <item>
      <title>Modeling and analysis of longitudinal vehicle dynamics near standstill with brake friction</title>
      <link>https://trid.trb.org/View/2752004</link>
      <description><![CDATA[Longitudinal vehicle dynamics play a critical role in ride comfort at low speeds, particularly during frequent start-and-stop manoeuvres in everyday driving. Under such conditions, friction-induced dynamics and abrupt changes in acceleration can lead to significant jerk and passenger discomfort. This thesis investigates low-speed longitudinal dynamics with a focus on friction effects, non-smooth behavior, and near-standstill jerk.A minimal vehicle model is developed to capture the essential longitudinal dynamics associated with propulsion, braking, and friction. To accurately represent transitions between static and dynamic friction, an event-driven numerical framework based on a state-machine formulation is introduced, enabling reliable simulation of stick-slip behavior and zero-velocity crossings. The non-smooth nature of the system is further analyzed using a Filippov framework, allowing phase-space investigation of motion near switching boundaries and providing insight into stability, trapping regions, and oscillatory behavior. The modeling approach is supported by experimental measurements from a real vehicle test conducted on an inclined road using high-resolution IMU sensors. Experimental data are used for parameter estimation, torque reconstruction, and state estimation through a Kalman filter, enabling phase-space analysis of the relative motion between the vehicle body and wheel. Taken together, the numerical, analytical, and experimental results provide a coherent description of longitudinal vehicle dynamics at low-speed.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752004</guid>
    </item>
    <item>
      <title>Microscopic simulation of bicycle traffic : analysis and modeling of heterogeneity and free riding on bicycle paths</title>
      <link>https://trid.trb.org/View/2752003</link>
      <description><![CDATA[As bicycling becomes an integral part of sustainable mobility, reliable planning tools are essential to ensure bicycling as an efficient mode of transport. The growing bicycle demand requires not only expanding infrastructure, but also ensuring that such infrastructure supports well-functioning traffic under high demands. Given the high heterogeneity in bicyclist characteristics, the use of microscopic traffic simulation, which explicitly considers individual properties and preferences, becomes particularly useful for evaluating bicycle traffic performance. While traffic simulation has been extensively utilized for traffic planning of various modes of transport, this type of modeling support is largely lacking in the planning of bicycle traffic. Although most commercial simulators allow multi-modal traffic analysis, bicycle traffic is often modeled by adjusting parameters in models originally designed for other modes, even though bicyclists may exhibit distinct characteristics and behaviors. Consequently, the proper inclusion of bicyclists into various traffic simulation analyses is difficult, and often inaccurate. The objective of this thesis is to develop and evaluate mathematical models for accurate microscopic simulation of bicycle traffic, with a focus on developing empirically well-founded models that capture the heterogeneity in bicyclists' characteristics and preferences, as well as their interactions with the built environment and with each other. The thesis delivers an empirical characterization of bicycle traffic in diverse contexts, describing the heterogeneity in characteristics and preferences of bicyclists - including disaggregated analyses by bicycle type - that potentially influence traffic performance. Methods for processing and validating bicycling data are developed to support this characterization. Furthermore, the thesis demonstrates that bicyclist speeds are highly context-dependent and proposes simulation models for context-related features of bicycling trips, such as topography, curvature, and wind, that integrate heterogeneous and adaptive free riding behavior to improve the accuracy of simulated speeds and the reliability of bicycle traffic simulations. This thesis advances the accuracy and applicability of microscopic simulation of bicycle traffic for its use in the planning of well-functioning bicycle traffic.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752003</guid>
    </item>
    <item>
      <title>Estimating land transport costs in Sweden's bio-based industries</title>
      <link>https://trid.trb.org/View/2751957</link>
      <description><![CDATA[The transition to a circular and biobased economy necessitates efficient and cost-effective transportation of geographically dispersed biomass resources. This PM presents the development of a transport cost model tailored to the specific needs of the Swedish biogas sector, with a focus on bulk transport of low-grade biomass and digestate. Building upon the Swedish national freight transport model SAMGODS and its associated cost calculations and incorporating updated assumptions for 2024, the model distinguishes between fixed and variable costs and introduces parameters for distance- and time-dependent cost estimation. The model is integrated with a spatial origin-destination (OD) matrix, enabling route-specific cost calculations based on road network data. A sensitivity analysis identifies key cost drivers, including vehicle utilization, backhauling rates, and payload capacity. The model is demonstrated through illustrative scenarios for both liquid and solid biomass transport, highlighting its applicability for simulation and planning purposes. While not intended for market pricing, the model provides a transparent and adaptable framework for evaluating Swedish transport logistics in biobased value chains.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:34:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2751957</guid>
    </item>
    <item>
      <title>Ground borne noise model and methodology description</title>
      <link>https://trid.trb.org/View/2751942</link>
      <description><![CDATA[The following report presents a model and methodology suggested to be used for ground-borne noise predictions in projects of the Swedish Transport Administration. The model is part of the project Development of methodology for ground-borne noise in road and railway projects carried out at the Division of Applied Acoustics, Chalmers, and financed by the Swedish Transport Administration. The project aims at developing and describing a methodology and model for ground-borne noise prediction to be used in the projects of the Swedish Transport Administration. The report contains a further development from the first draft presented in November 2020. The suggested model is based on a literature review that has been carried out, questions asked to consultancy companies about their models used today, and by asking the members of UIC (International union of railways) for information about models used in their countries. Information was achieved from Italy, Denmark, Switzerland, Germany, Portugal, and the United Kingdom (the HS2 model). The model has been adapted to Swedish bedrock conditions by measurements carried out in the Gårda tunnel in Gothenburg and by using existing data and results from measurements in the Åsa tunnel close to Varberg and the Håknäs tunnel along the Bothnia Line. The existing data and reports have been provided by the Swedish Transport Administration. The suggested prediction model for ground-borne noise is based on a structure with a source term and correction terms that take into account various aspects such as train speed, distance attenuation, ground-to-building coupling, the relation between vibration levels on floor/walls, sound pressure levels in the room, and various treatments in the track. The terms are assumed to be independent and may therefore be handled separately. The model and methodology are based on a concept of different levels of information available and different model precision using stages of: location, planning and construction.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:34:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2751942</guid>
    </item>
    <item>
      <title>A Simulation-Based Methodology for Road Freight Network Abstraction: Case Study in the Canadian Prairie Region</title>
      <link>https://trid.trb.org/View/2658093</link>
      <description><![CDATA[Efficient planning and design of road freight networks require an understanding of their spatial characteristics and demand patterns. However, the large scale and dense interconnections of these networks pose challenges in accurately representing and analyzing freight movement while ensuring computational efficiency. Public agencies also face difficulties in traffic monitoring, safety analysis, and asset management on a broader scale. This study focuses on the Prairie region of Canada and leverages data from the Canadian Freight Analysis Framework (CFAF) to propose a methodology for network extraction. Using a multi-model traffic assignment approach, the method extracts a sub-network from the original while preserving essential functional features. Results indicate that the proposed approach significantly simplifies the network without compromising structural integrity, offering a robust foundation for practical applications.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658093</guid>
    </item>
    <item>
      <title>Monitoring tire-road friction using connected vehicle data</title>
      <link>https://trid.trb.org/View/2666552</link>
      <description><![CDATA[One in five serious or fatal road traffic accidents occur under severe weather conditions. Despite notable improvements in traffic safety, the Vision Zero approach of shared responsibility for eliminating fatalities and serious injuries remains a global challenge. As the vehicle fleet becomes increasingly connected and automated, vast amounts of data are generated for every kilometer traveled, data that can be used to enhance road safety. One promising application is the monitoring of tire-road friction to improve understanding of road surface conditions and the interaction between tire and road. Since 2018, the Swedish Transport Administration has obtained connected vehicle data, sometimes referred to as floating car data (FCD) or probed vehicle data, to follow up on tire-road friction. The focus of the administration has been on how connected vehicle data can be applied to support and improve winter road maintenance on Sweden's public road network. Within the Digital Vinter project, connected vehicle data have been validated and analyzed alongside conventional tire-road friction estimation methods and in relation to Road Weather Information Systems (RWIS) and Mobile Reporting of Ploughing (MIP). Some of the results from the Digital Vinter project are presented within this thesis.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666552</guid>
    </item>
    <item>
      <title>Capacity modeling and shift optimization for train dispatchers (CAPMO-Train)</title>
      <link>https://trid.trb.org/View/2666521</link>
      <description><![CDATA[In the CAPMO-Train project, we aimed to illuminated the possibilities of automated, optimized train-dispatcher shifts that take all legal and operational restrictions into account and integrate train-dispatcher workload. To this end, we developed an optimization frame-work for shift scheduling. We exemplified our framework with results for Malmo¨ dispatching center, but the framework itself is flexible and can be applied for other dispatching centers. We derived the number of train movements in a dispatching area during a time period as an approximation for the objective task load (which is correlated to the subjective dispatcher workload) and inferred an upper bound for this approximation based on discussion with operational experts. Together with legal and operational requirements for train-dispatcher shifts, this task-load measure build the basis for the optimization framework.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666521</guid>
    </item>
    <item>
      <title>Safety performance functions in a road environment with automated vehicles</title>
      <link>https://trid.trb.org/View/2640566</link>
      <description><![CDATA[The reduction of road fatalities can be achieved by intervening in various aspects, including infrastructure, transportation policy, vehicles, and driver behavior. One of the most promising solutions to solve this issue is to rely on Automated Vehicles (AVs), which can prevent human errors, which account for most crashes. However, the impact of AVs on road safety is still unquantifiable. The reason resides in a lack of observed data, as well as in the uncertainty about AV introduction on roads and their interaction with other vehicles and users. In this paper, a methodology to predict the impact of AVs is proposed, relying on Safety Performance Functions (SPFs). An ad hoc SPF for AVs has been developed just for multivehicle crashes, based on a set of market penetration rates, to propose a mathematical model that can include recent technological innovations in road traffic and be adapted to other contexts. Considering the area of the Province of Bari and three different time horizons, crashes were simulated with the presence of AVs in different traffic scenarios. The proposed scenarios were taken from extensive literature studies about the deployment of AVs. The SPF for the predicted crashes was developed by adding one coefficient that considers the presence of AVs to the baseline equation, controlling for the road geometry. The fitted models show a satisfactory goodness-of-fit, based on different metrics, including CuRe (Cumulative Residuals) plots.]]></description>
      <pubDate>Fri, 19 Dec 2025 10:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640566</guid>
    </item>
    <item>
      <title>Railway bridge dynamic amplification factors : investigation of effects from track irregularities</title>
      <link>https://trid.trb.org/View/2598650</link>
      <description><![CDATA[This work investigates the dynamic effects on short-span railway bridges, with a particular focus on the impact of track irregularities and the resulting dynamic amplification factor, notated as φ &#8242;&#8242;. The objective of this study is to investigate whether the current design formula, which is based on older investigations, is un necessary conservative and could be refined to increase the allowable axle loads and enhance the effectiveness of the railway transport system. The study is focused on short-span bridges, with a span length between 4-20 m, given that they are more susceptible to dynamic effects. The research involves experimental testing on two concrete bridges located on the southern main line near Katrineholm, Sweden. The objective is to validate the finite element models used for a larger number of simulations. The principal model employed for simulations is a two-dimensional model that incorporates train-track bridge interaction. The impact of track irregularities is incorporated into the model to calculate their isolated effect. The track irregularities used are derived from measurements on track sections in Sweden. The results show that the current formula overestimates the dynamic amplification factor for a significant portion of the studied interval compared to the formula given in Eurocode, with the upper limit for eigenfrequency and for the studied spans of 4-20 m and train speeds of up to 120 km/h, particularly for lower speeds. Based on the simulation results, a new formula for φ &#8242;&#8242; is proposed. A big difference between the formulas, is that the Eurocode formula is no longer affected by speed after 80 km/h, which was not in line with the simulations. The magnitude of difference also depends on with what kind of track quality is being compared against, the new formula for φ &#8242;&#8242;, proposed in this study, uses a scaling factor depending on standard deviation σ, instead of only using "good track" or not.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598650</guid>
    </item>
    <item>
      <title>Planning and operation optimization of mobility-on-demand services in the multimodal mobility system</title>
      <link>https://trid.trb.org/View/2598649</link>
      <description><![CDATA[Multimodal mobility systems provide seamless service by integrating various travel modes like driving, cycling, Mobility-on-Demand (MoD) services, and Public Transit (PT) services. With the advancement in autonomous driving and electric vehicles, MoD services show their significant potential in coordinating with other travel modes, especially for PT services. To make the best use of its potential, it is essential to investigate the planning and operations of MoD and PT services in the multimodal mobility system. In the multimodal mobility system, service operations on the supply side should focus on intermodal coordination. On the demand side, customers decide on routes and modes according to service levels such as travel time and price. However, research gaps exist in the planning and operations of integrated MoD and PT services. First, existing literature lacks in optimizing service operations that conform to customer behavior for multimodal mobility systems. Second, existing methods are not applicable to solve such an optimization problem with consistent 'expected' (from service operations) and 'actual' customer behavior. Third, there is a lack of operational optimization models with temporal dynamics for electric MoD vehicles integrated with PT service. To address the above issues, the included papers propose (1. service operation planning in multimodal mobility systems, (2. a generic mathematical solution algorithm for the choice-based optimization problem, and (3. electric MoD operation in multimodal mobility systems.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598649</guid>
    </item>
    <item>
      <title>Railway rescheduling under near-operational disruptions</title>
      <link>https://trid.trb.org/View/2598588</link>
      <description><![CDATA[Railway is an environmentally sustainable mode of transportation, which offers convenience for passengers and provides a cost-effective and efficient solution for the movement of goods. However, railway transportation also has drawbacks, especially disruptions due to some incidents, which are difficult to accurately predict and prevent. Infrastructure failure, extreme weather, human error, and lack of staff are typical examples of such incidents. The disruptions cause different levels of train delays and even cancellations. Frequent delays and cancellations make the railway less competitive with other modes of transport. As a result, it is crucial to investigate the possible strategies for rapidly restoring railway traffic after disruptions. In this thesis, we focus on railway rescheduling after near-operational disruptions. We aim to achieve acceptable results within a short time and a small computational effort. This thesis introduces some fundamental concepts related to railway rescheduling, outlines the motivation and research questions of this thesis, discusses further concepts of railway rescheduling including timetable, rolling stock, and crew rescheduling, and displays the relevant methods used for rescheduling. In this thesis, we propose approaches for near-operational rescheduling of the timetable and crew schedules.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598588</guid>
    </item>
    <item>
      <title>Incremental stability of traffic reaction models</title>
      <link>https://trid.trb.org/View/2598580</link>
      <description><![CDATA[Incremental asymptotic stability is assessed for cooperative systems of ordinary differential equations (ODEs). Such systems of ODEs arise in macroscopic traffic flow modeling which is emphasized in the present thesis. If a system of ODEs is incrementally asymptotically stable then there exists a set of initial conditions from which all solutions converge to each other asymptotically and this can be exploited in a state estimation context. It is shown that if the state space of a cooperative system of ODEs is a Cartesian product of intervals, then this system is incrementally asymptotically stable if and only if all solutions that are initially ordered, converge to each other in an appropriate sense. This fact is used to establish incremental asymptotic stability for a class Traffic Reaction Models. The Traffic Reaction Model form a family of numerical schemes to solve scalar conservation laws, governed by partial differential equations (PDEs). For one conservation law there are several numerical schemes and if the scheme is semi-discrete it gives rise to a system of ODEs. Suitable conditions on the conservation law are provided such that a particular semi-discrete scheme gives rise to an incrementally exponentially stable system of ODEs.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598580</guid>
    </item>
  </channel>
</rss>