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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>Virtual Load Sensor Methodology for Structural Verification</title>
      <link>https://trid.trb.org/View/2624029</link>
      <description><![CDATA[Large off-highway vehicles, such as combine harvesters, corn heads, and hinged drapers, are complex machines comprised of multiple interacting subsystems. Consequently, capturing the load path through full vehicle finite element modeling poses significant challenges and can be computationally intensive during the design development process. We primarily employ two structural analysis approaches based on the availability of load inputs: (1) Full Frame Finite Element Model Setup; (2) Subsystem Finite Element Model Setup. Just like virtual verification, physical verification can also be performed at both the full vehicle and subsystem levels. The most critical input for both physical and virtual structural verification is load data. Traditionally, we acquire structural loads induced by ground excitations using wheel force transducers. For subsystem finite element models, interface loads are essential, which often necessitate custom load transducers during data collection. However, instrumenting every interface of the machine for load measurement is neither practical nor cost-effective. To overcome this challenge, authors propose a novel approach called the Virtual Load Sensor methodology. This technique extracts subsystem interface loads from the full vehicle finite element model, enabling subsystem-level structural analysis and fatigue verification, leading to faster and more concurrent design development. Additionally, these loads can be utilized to drive physical rig tests for subsystems, thereby avoiding the need for costly full vehicle rig tests. In this paper, we present an innovative and standardized Virtual Load Sensor methodology, detailing how this method facilitates the extraction of subsystem interface load time histories through superposition from the virtual finite element model. The methodology has been proved by demonstrating load and strain time history correlation.]]></description>
      <pubDate>Tue, 30 Dec 2025 08:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624029</guid>
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    <item>
      <title>NCHRP Research Report 1019: How Do Farm Vehicles Affect Pavements?</title>
      <link>https://trid.trb.org/View/2554117</link>
      <description><![CDATA["Implements of husbandry (IoH)” refer to vehicles that are exclusively used for agricultural purposes, such as planting; seeding; cultivating; harvesting; applying nutrients, fertilizers, or chemicals; or transporting agricultural products and supplies to and from farms. These vehicles include tractors, attached equipment (i.e., tankers, manure spreaders, plows, cultivators, and combines), and trucks. While primarily used on farms, IoH frequently travel on roads and bridges. National Cooperative Highway Research Program (NCHRP) Project 01-58 developed models for estimating the effects of IoH on pavement performance and these procedures and a software tool for estimating impacts are presented in NCHRP Research Report 1019: Quantifying the Effects of Implements of Husbandry on Pavement. This article presents highlights from Research Report 1019 including: IoH axle weight, tire footprint, and axle spacing as compared to typical trucks; current State practices for evaluating pavement damage from overweight loads; predicting IoH damage on flexible and rigid pavements; the need for calibration of damage models for IoH vehicles; and evaluating the effect of thawing on base shear strength. Incorporating the developed procedures into the AASHTOWare Pavement ME Design software could provide a means for better estimation of the effects of IoH on pavement performance.]]></description>
      <pubDate>Wed, 16 Jul 2025 08:47:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554117</guid>
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    <item>
      <title>Disturbance-Observer-Based Barrier Function Adaptive Sliding Mode Control for Path Tracking of Autonomous Agricultural Vehicles With Matched-Mismatched Disturbances</title>
      <link>https://trid.trb.org/View/2511870</link>
      <description><![CDATA[In this article, path-tracking control strategies are proposed for autonomous agricultural vehicles (AAVs) with unknown matched–mismatched disturbances. First, a second-order disturbance observer (DOB) is designed to estimate the matched and mismatched disturbances to mitigate their negative effects. Second, by introducing a modified sliding mode surface, a DOB-based first-order sliding mode (FOSM) control scheme is proposed to effectively deal with the system lumped disturbance. To completely eliminate the chattering problem existing in the designed DOB-based FOSM controller, a DOB-based barrier function adaptive sliding mode (BFASM) control strategy is further proposed. The distinguishing feature of the developed BFASM control strategy is that the designed sliding variable can be finite time stabilized to a predefined neighborhood around the origin, and the upper bound of lumped disturbance does not require to be known in advance. The practical stability of the overall path-tracking system is demonstrated by using the rigorous Lyapunov theory analysis. Finally, some comparative simulations and experiments are conducted to highlight the strong robustness, adaptability, and excellent tracking performance of the developed barrier function-based adaptive path-tracking control strategy.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511870</guid>
    </item>
    <item>
      <title>Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet)</title>
      <link>https://trid.trb.org/View/2540251</link>
      <description><![CDATA[Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment vehicles to examine the factors influencing crash severity, with a particular focus on comparing incidents on county roads to those on non-county roads. The dataset included key variables such as road geometry, lighting conditions, and traffic interactions, with preprocessing techniques like Synthetic Minority Over-sampling Technique (SMOTE) applied to address class imbalance. The TabNet model, a tabular deep learning model, was employed to analyze crash dynamics, offering both predictive accuracy and interpretability through feature importance and SHapley Additive exPlanations (SHAP) plots. Findings revealed that crash severity on county roads is primarily influenced by crash speed limit, first harmful event, traffic control, and person age, reflecting the role of road geometry and demographic risk in rural settings. In contrast, non-county roads were more affected by lighting conditions, intersection-related features, and population group, emphasizing the impact of visibility and traffic complexity in urban areas. Speed limit consistently emerged as a critical factor across all road types and severity levels. The study emphasized the need for targeted safety interventions, including visibility enhancements, speed management, and enhanced education campaigns for county and non-county areas.]]></description>
      <pubDate>Fri, 16 May 2025 09:33:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540251</guid>
    </item>
    <item>
      <title>Accuracy Verification of Component-TPA on the Finished Off-Road Vehicle</title>
      <link>https://trid.trb.org/View/2505961</link>
      <description><![CDATA[As the diversity of prime movers continues to grow, the applying the component-TPA to the finished off-road vehicles such as construction machinery and agricultural machines is ongoing to realize the modular design. In this study, the effects of the nonlinearity of accelerance in the forced vibration system excited by the prime mover and the condition number when identifying the blocked force using the inverse matrix method have been investigated.]]></description>
      <pubDate>Tue, 25 Mar 2025 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2505961</guid>
    </item>
    <item>
      <title>Performance-optimisation-based repetitive trajectory tracking control for autonomous farming vehicle</title>
      <link>https://trid.trb.org/View/2507265</link>
      <description><![CDATA[The high precision trajectory tracking for the autonomous farming vehicle (AFV) is closely related to work quality and crop yield. Farmland operations are usually repetitive tillages, and the farmland soil is relatively soft, slippery, and uneven, which can easily lead to uncertain problems such as slippage, parameter perturbation, and external disturbance. This paper proposes a finite-time repetitive trajectory tracking control strategy integrated with particle swarm optimisation (PSO) and equivalent input disturbance (EID) for the AFV to achieve performance optimisation. The repetitive control framework with finite-time convergence technique can effectively realise the iterative convergence performance of the repetitive trajectory tracking. The PSO algorithm is integrated into determining the control gains to optimise the dynamic and steady-state performances of the trajectory tracking control system. The EID method enhances the tracking precision and robustness to internal and external disturbances. Under MATLAB/Simulink and CarSim co-simulation environment, the effectiveness and advantages of the designed control strategy are illustrated by comparing it with the traditional repetitive control, the EID-based PI control, the active-disturbance-rejection-control-based nonsingular terminal sliding mode control, as well as the sliding-mode-observer-based integral sliding mode control strategies for a farming vehicle.]]></description>
      <pubDate>Tue, 25 Mar 2025 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2507265</guid>
    </item>
    <item>
      <title>Study on Technical Requirements for the Road Approval of Non-Road Mobile Machinery</title>
      <link>https://trid.trb.org/View/2458960</link>
      <description><![CDATA[Up to now, unlike other types of vehicles like agricultural tractors, cars, trucks, buses or motorcycles, non-road mobile machinery (NRMM) has not had an EU wide type approval system. Instead, NRMMs were approved by individual member states using their own national regulations. This lack of harmonisation of technical requirements for NRMM in the European market has had a negative impact on the NRMM sector, leading to increased costs and design complexities associated with the need to conform to the different sets of requirements of the 27 Member States. Harmonisation of the type-approval system across the EU will remove significant market friction and improve the functioning of the market for these machines.]]></description>
      <pubDate>Mon, 13 Jan 2025 10:24:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2458960</guid>
    </item>
    <item>
      <title>Sustainable Production Strategies in Agricultural Transport: Balancing Economic Efficiency and Environmental Impact</title>
      <link>https://trid.trb.org/View/2408240</link>
      <description><![CDATA[Purpose. This paper aims to present a sustainable production strategy for manufacturing parts and components for agricultural transport means, focusing on minimising production costs and post-production waste generation. Methodology. The research employs a case study approach, using participant observation and analysis of a manufacturing company's specific production process for an agricultural trailer floor panel. The authors propose a model for optimising raw material selection and utilising post-production waste to achieve economic and environmental savings. Results. The results demonstrate that by selecting the appropriate raw material dimensions and implementing a product diversification strategy to utilise post-production waste, the manufacturer can reduce material costs and environmental losses while generating additional revenue streams. Theoretical contribution. The study contributes to sustainable production management by proposing a practical strategy model that balances economic efficiency and environmental concerns in manufacturing agricultural transport components. The model emphasises the importance of raw material selection, waste minimisation, and product diversification to achieve sustainability goals. Practical implications. The findings provide valuable insights for manufacturers in the agricultural transport sector, highlighting the potential benefits of adopting sustainable production strategies. The proposed model can be implemented in practice to optimise resource utilisation, reduce waste generation, and improve overall economic and environmental performance.]]></description>
      <pubDate>Sat, 31 Aug 2024 21:06:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2408240</guid>
    </item>
    <item>
      <title>Unsupervised Learning-Based Classification of Driver Following Behavior in Agricultural Traffic</title>
      <link>https://trid.trb.org/View/2335326</link>
      <description><![CDATA[Use of public roadways by farm vehicles is essential for accomplishing routine farming activities such as moving from field to field. Crashes involving farm vehicles are more likely to result in injuries than crashes that do not involve farm vehicles. One challenge of studying farm vehicle crashes is that very little is known about the exposure of farm vehicles to other vehicles on the roadway. This paper presents a methodology for classifying the behavior of a passenger vehicle with respect to a farm vehicle on the roadway using video image processing and an unsupervised classification technique. A custom data collection device recorded video of vehicles as they approached, followed, and overtook the same-direction farm vehicle. Video image processing, object detection based on deep learning, and manual annotation from video reviewers resulted in a trajectory (i.e., vehicle's estimated distance from farm vehicle over time) for a primary subject vehicle. An unsupervised Gaussian mixture model (GMM), previously fitted using video and GPS data from instrumented vehicles on a test track to identify three distinct phases of behavior (i.e., approaching, following, and backing off), was applied to the trajectories. A sample of predictions from the GMM were evaluated by domain expert to calculate the efficacy and performance of the model. The overall accuracy of the GMM was 80%, but its performance varied widely across the different driver behavior classes. The results of this study will enable researchers to better quantify safe and potentially unsafe driving behaviors near farm equipment.]]></description>
      <pubDate>Thu, 15 Feb 2024 15:25:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2335326</guid>
    </item>
    <item>
      <title>Matheuristic for synchronized vehicle routing problem with multiple constraints and variable service time: Managing a fleet of sprayers and a tender tanker</title>
      <link>https://trid.trb.org/View/2277130</link>
      <description><![CDATA[This paper considers an extension of the vehicle routing problem with synchronization constraints and introduces the vehicle routing problem with multiple synchronization constraints and variable service time. This important problem is motivated by a real-world problem faced by one of the largest agricultural companies in the world providing precision agriculture services to their clients who are farmers and growers. The solution to this problem impacts the performance of farm spraying operations and can help design policies to improve spraying operations in large-scale farming. The authors propose a Mixed Integer Programming (MIP) model for this challenging problem, along with problem-specific valid inequalities. A three-phase powerful matheuristic is proposed to solve large instances enhanced with a novel local search method. The authors conduct extensive numerical analysis using realistic data. Results show that the authors’ matheuristic is fast and efficient in terms of solution quality and computational time compared to the state-of-the-art MIP solver. Using real-world data, the authors demonstrate the importance of considering an optimization approach to solve the problem, showing that the policy implemented in practice overestimates the costs by 15%–20%. Finally, the authors compare and contrast the impact of various decision-maker preferences on several key performance metrics by comparing different mathematical models.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:10:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2277130</guid>
    </item>
    <item>
      <title>Application of V2X Technology in Communication Between Vehicles and Infrastructure in Chosen Area</title>
      <link>https://trid.trb.org/View/1972708</link>
      <description><![CDATA[The Precision Agriculture (PA) concept is becoming more and more important in the modern world. Although it faces numerous hardships with technology the biggest problem is the lack of pure standard definition. Solution may be to get inspired from Vehicle to Everything (V2X) model. It is a widely spoken conception of the road system, where vehicle, infrastructure and other traffic elements would be able to communicate with others. However, it is not in the common use, it is a developed technology with tested and established assumptions for various of aspects like transmitting medium. Because such work has been done in that field, it should be considered to implement solutions from V2X to PA, due to the similarities between them.]]></description>
      <pubDate>Thu, 16 Nov 2023 14:45:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972708</guid>
    </item>
    <item>
      <title>Predictors of rural driver self-reported passing behaviors when interacting with farm equipment on the roadway</title>
      <link>https://trid.trb.org/View/2264475</link>
      <description><![CDATA[Crashes involving farm equipment (FE) are a major safety concern for farmers as well as all other users of the public road system in both rural and urban areas. These crashes often involve passenger vehicle drivers striking the farm equipment from behind or attempting to pass, but little is known about drivers’ perceived norms and self-reported passing behaviors. The objective of this study is to examine factors influencing drivers' farm equipment passing frequencies and their perceptions about the passing behaviors of other drivers. Data were collected via intercept surveys with adult drivers at local gas stations in two small rural towns in Iowa. The survey asked drivers about their demographic information, frequency of passing farm equipment, and perceptions of other drivers' passing behavior in their community and state when approaching farm equipment (proximal and distal descriptive norms). A multinomial logistic regression model was used to estimate the relationship between descriptive norms and self-reported passing behavior. Survey data from 201 adult drivers showed that only 10% of respondents considered farm equipment crashes to be a top road safety concern. Respondents who perceived others passing farm equipment frequently in their community were more likely to report that they also frequently pass farm equipment. The results also showed interactions between gender and experience operating farm equipment in terms of self-reported passing behavior. Results from this study suggest local and state-level norms and perceptions of those norms may be important targets for intervention to improve individual driving behaviors around farm equipment.]]></description>
      <pubDate>Mon, 23 Oct 2023 15:08:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264475</guid>
    </item>
    <item>
      <title>Experimental assessment of a PID control solution for braking safety of transportation by agricultural tractor trailer combinations</title>
      <link>https://trid.trb.org/View/2215654</link>
      <description><![CDATA[Huge numbers of agricultural tractor trailer combinations are used for transportation. Many of the combinations rely on over-run braking on trailers. On-road transportation by the combination is being increased by increasing speed and mass capacities due to market pressure. A significant braking safety issue during on-road transportation is dealt with in this work. Proportional integral derivative (PID) control is proposed as a transitional solution towards domination of new tech equipment. Conventional and proposed PID brake controls were compared experimentally by a loaded real world scale agricultural tractor trailer combination. A double axle (front and rear) trailer with 8 tons load was used for dry asphalt road conditions and 0.35 seconds lag time detected between manual and PID controlled braking. Loss of driving stability was reduced by 50% and deceleration increased 21% with PID. Jack-knifing phenomenon is also evaluated. Proposed solution covers an important safety issue and improves braking performance.]]></description>
      <pubDate>Mon, 21 Aug 2023 09:08:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2215654</guid>
    </item>
    <item>
      <title>Characteristic analysis with new liquid solid coupling dynamics model of high-clearance sprayer during liquid variation</title>
      <link>https://trid.trb.org/View/2215652</link>
      <description><![CDATA[High clearance sprayer (HCS) plays an important role in agricultural production. Considering HCS's higher utilization and work environment, the characteristic analysis of HCS is studied. Therefore, a new whole simulation model of liquid solid coupling dynamics of HCS is built. In the detail, the mathematical models of kinetic energy, potential energy and dissipated potential energy are built with new whole vehicle mechanical model. Secondly, the differential equations of HCS nodes are set up. Then the nodes response curves of HCS total system are simulated. Finally, the influence of liquid variation on HCS nodes is analyzed. With simulation results, the liquid fluctuation of liquid storage device is influenced by primary motion of HCS. The influence of flow rate on the stability of HCS nodes is significantly different. The stability of HCS is the problem to be settled urgently when the velocity and liquid flow are all changed and synchronized.]]></description>
      <pubDate>Mon, 21 Aug 2023 09:08:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2215652</guid>
    </item>
    <item>
      <title>Using Motor Vehicle Crash Records for Injury Surveillance and Research in Agriculture and Forestry</title>
      <link>https://trid.trb.org/View/2196910</link>
      <description><![CDATA[Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events. The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives. The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives. Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety’s evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health. Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.]]></description>
      <pubDate>Fri, 21 Jul 2023 09:18:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2196910</guid>
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