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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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    <item>
      <title>Trajectory-integrated accessibility analysis of public electric vehicle charging stations</title>
      <link>https://trid.trb.org/View/2704496</link>
      <description><![CDATA[Electric vehicle (EV) charging infrastructure is crucial for advancing EV adoption, managing charging loads, and ensuring equitable transportation electrification. However, there remains a notable gap in comprehensive accessibility metrics that integrate the mobility of the users. This study introduces a novel accessibility metric, termed Trajectory-Integrated Public EVCS Accessibility (TI-acs), and uses it to assess public electric vehicle charging station (EVCS) accessibility for approximately 6 million residents in the San Francisco Bay Area based on detailed individual trajectory data in one week. Unlike conventional home-based metrics, TI-acs incorporates the accessibility of EVCS along individuals’ travel trajectories, bringing insights on more public charging contexts, including public charging near workplaces and charging during grid off-peak periods. As of June 2024, given the current public EVCS network, Bay Area residents have, on average, 7.5 h and 5.2 h of access per day during which their stay locations are within 1 km (i.e. 10–12 min walking) of a public L2 and DCFC charging port, respectively. Over the past decade, TI-acs has steadily increased from the rapid expansion of the EV market and charging infrastructure. However, spatial disparities remain significant, as reflected in Gini indices of 0.38 (L2) and 0.44 (DCFC) across census tracts. Additionally, our analysis reveals racial disparities in TI-acs, driven not only by variations in charging infrastructure near residential areas but also by differences in their mobility patterns.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704496</guid>
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    <item>
      <title>Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications</title>
      <link>https://trid.trb.org/View/2724741</link>
      <description><![CDATA[Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, generally provide high prediction accuracy. However, their predefined functional structure and large number of coupled parameters are designed for broad applicability across many tire types rather than for specific racing tires. This often results in limited interpretability, nontrivial parameter identification, and unnecessary model complexity for specialized applications such as Formula Student.This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify models that optimally balance prediction accuracy and structural simplicity. In contrast to black-box machine learning approaches, the resulting models consist of explicit mathematical expressions that enable physical interpretation and efficient evaluation.The methodology is applied to experimental tire test bench data, focusing on the lateral force – slip angle relationship at constant vertical load. In a first step, the symbolic regression algorithm is utilized to derive a set of candidate mathematical expressions. These models are subsequently benchmarked against 200 independent data sets comprising various tire types and vertical loads. The evaluation reveals that the identified models approximate the measured tire behavior with accuracy comparable to, and in many cases exceeding, the Magic Formula, while exhibiting lower model complexity.The results demonstrate that symbolic regression can uncover alternative tire models that better represent the characteristics of Formula Student racing tires than conventional approaches. Owing to their compact structure and physical consistency, the derived models are particularly well suited for real-time vehicle simulations, parameter studies, and control-oriented applications in Formula Student vehicle development.]]></description>
      <pubDate>Tue, 21 Jul 2026 11:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724741</guid>
    </item>
    <item>
      <title>Who gets to schools? Education cumulative inequalities in Brazilian cities</title>
      <link>https://trid.trb.org/View/2692320</link>
      <description><![CDATA[Access to quality basic education is crucial for social equity. In Global South cities, where socioeconomic, racial, and infrastructural inequalities are extensive and cumulative, a multidimensional understanding of school access is especially necessary. Traditional accessibility analyses often overlook critical dimensions such as school quality and heterogeneous demand. This paper introduces an innovative framework to examine how these inequalities shape public elementary school access, integrating school capacity, differentiated demand, and service quality. Leveraging a spatial optimization algorithm combined with explainable AI (Shapley values), the analysis presented in this article reveals income as the most decisive factor in school choice in São Paulo and Belo Horizonte, in Brazil. Racial disparities and inadequate pedestrian infrastructure (sidewalk coverage) significantly amplify access barriers. Considering school quality reveals more pronounced inequalities and a funneling effect that disproportionately benefits higher-income students. This study offers a diagnosis of spatial injustice, underscoring the urgent need for integrated education and urban mobility policies to effectively promote equity in education.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692320</guid>
    </item>
    <item>
      <title>Lessons from High-Speed Level-5 AV Racing: Data Management Plan</title>
      <link>https://trid.trb.org/View/2716614</link>
      <description><![CDATA[This Center for Connected and Automated Transportation (CCAT) Data Management Plan (DMP) provides a framework for managing the data that are expected to be generated from the research project titled “Lessons from High-speed Level-5 AV Racing” that was awarded through the center. For this project, data will be collected on high-speed racing vehicle performance in a field environment and human subject attributes in a driving simulation environment. This document discusses the key elements of Purdue CCAT’s Data Management Plan for this project, namely, data description, data format and metadata standards, access policies, policies for re-use, redistribution, derivatives, and plans for archiving and preservation.]]></description>
      <pubDate>Wed, 24 Jun 2026 17:03:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716614</guid>
    </item>
    <item>
      <title>High-Speed Driverless Racing: Lessons for Autonomous Mobility</title>
      <link>https://trid.trb.org/View/2716612</link>
      <description><![CDATA[Driverless racecar competitions are often perceived as recreational. However, their academic, industry, and research benefits are potentially tremendous, as several significant lessons can be learned from these competitions to advance autonomous mobility. This study was motivated (and its conduction facilitated) by the active involvement of the authors in several autonomous racing initiatives in the United States. The study first synthesized and analyzed information from existing literature related to the key modules of autonomous vehicle (AV) operations as part of AV racing competitions and identified some lessons that could be learned in the context of each module – perception and sensing, data fusion, path planning and decision making, vehicle dynamics and control, and hardware and software safety. After synthesizing such information, the study establishes that high-speed AV tests and competitions where multiple teams push the limits of sensing, decision-making, and control serve as testbeds for creating, developing, and refining AV technologies. This is because the AV racing events have used controlled environment platforms to demonstrate what autonomous systems can achieve under extreme conditions, and allowing testing of edge-case scenarios and performance extremes that would be too risky or impractical on public roads. Further, by demonstrating what autonomous systems can achieve under extreme conditions and edge cases, the AV racing events have shown that they foster innovation in a safe and controlled environment. The identified lessons from the racing competitions can serve as a knowledge base to facilitate safe and efficient AV operations on high-speed road transportation corridors such as freeways. The discussion includes caveats regarding the dichotomies between AV operations guideway (racetrack vs. roadway) and AV vehicle designs (race car designs vs. standard automobile and truck design), and how these differences temper the translation of lessons learned from AV racing to real-world (roadway) AV operations. In sum, the study outcomes support the notion that high-speed AV events can continue to serve as proving grounds for testing edge cases of autonomous operations to facilitate safety and movement efficiency in autonomous driving in the real world. In the prospective era of AV operations on public roads, the lessons learned can help enhance AV design and sensing/communication capabilities for roadway operations specifically. The lessons can also promote transportation mobility, safety, reliability, and economic productivity associated with autonomous mobility at high-speed road corridors, particularly freeways.]]></description>
      <pubDate>Wed, 24 Jun 2026 17:03:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716612</guid>
    </item>
    <item>
      <title>2025 AASHTO State DOT HR Metrics Report</title>
      <link>https://trid.trb.org/View/2696142</link>
      <description><![CDATA[This report provides key human resources (HR) metrics from state departments of transportation (state DOTs) that responded to a survey developed by the American Association of State Highway and Transportation Officials (AASHTO) Committee on Human Resources. In the survey, state DOTs were asked to provide information on employee counts and tenure, turnover and retirement, telework, and diversity data, both for the entire agency and broken down by EEO position categories. The collection and reporting of this data is designed to create a uniform set of metrics to inform nationwide policies, processes, and best practices. This report updates data collected for the 2023 Annual AASHTO State DOT HR Metrics Report, published in 2024.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696142</guid>
    </item>
    <item>
      <title>Predicting the Impact of FIA 2026 Power Unit Regulations on the Performance of Formula 1 Car</title>
      <link>https://trid.trb.org/View/2691995</link>
      <description><![CDATA[This work investigates the impact of the forthcoming 2026 FIA Formula 1 power unit regulations on vehicle track performance. This new regulation introduces a rebalanced power distribution between the internal combustion engine and the Motor Generator Unit-Kinetic (MGU-K), with each unit contributing up to 350 kW. This transition nearly triples the previous 120 kW output of the MGU-K while constraining the internal combustion engine through newly imposed fuel energy limits. A full vehicle powertrain model was developed in GT-Suite following the 2026 FIA technical directives. Particular attention was given to Articles 5.4.7 to 5.4.10, which define key constraints on hybrid operation: a maximum variation of 4 MJ in battery state of charge, up to 9 MJ of recoverable energy per lap, and a peak MGU-K electrical power output of 350 kW. The model includes updated architecture specifications, active aerodynamic modules, energy deployment logic, and component-level constraints. Telemetry data from the 2024-2025 qualifying sessions was employed for model development, validation, and benchmarking, enabling performance comparisons under realistic track conditions. Analysis results reveal notable deviations in powertrain response and vehicle performance across a range of circuits, establishing a correlation between the powertrain performance and track layouts. The model enables the decomposition of overall system effects into discrete contributions from specific regulatory requirements and individual component limitations. This paper will propose a justification and scheme for developing circuit-specific strategies based on maximum energy constraint and vehicle performance.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691995</guid>
    </item>
    <item>
      <title>A Comparative Assessment of Fuel Cell and Battery Powertrains for
                    Formula Student Electric Applications</title>
      <link>https://trid.trb.org/View/2696794</link>
      <description><![CDATA[The organizers of the most prominent Formula Student competitions have recently                     initiated a preliminary feasibility study on the application of hydrogen-based                     propulsion technologies in future single-seater race vehicles. These include                     electric powertrains with electrochemically converted hydrogen in fuel                     cell–powered vehicles, competing within the electric championship league. Based                     on the initial set of regulations, this study presents a model-based comparison                     between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs)                     for Formula Student. The analysis is conducted using energy, power, and                     efficiency metrics from four candidate models of propulsion systems, implemented                     in an open and publicly available MATLAB script: two BEVs with varying battery                     capacities, and two FCVs employing different hybridization strategies. The aim                     of this study is to pinpoint and quantify the advantages and disadvantages of                     each technology for the Formula Student use case, and to identify the optimal                     solution combining the different requirements of maximum acceleration and                     endurance race.]]></description>
      <pubDate>Mon, 27 Apr 2026 16:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696794</guid>
    </item>
    <item>
      <title>Simulation Study on a Neural Tire Model Based on Grouped Feature Learning</title>
      <link>https://trid.trb.org/View/2691934</link>
      <description><![CDATA[The tire model is a crucial component in the design of the K-characteristic of FSAE racing car suspensions, and directly influences the achievement of maximum cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure, and camber angle as input features, while the second uses tire temperature, ambient temperature, and ground temperature. The first network model simulates the magic formula model of the tire, and the second fine-tunes the lateral force, aligning moment, and overturning moment based on temperature characteristics. It prevents secondary input features (such as temperature) from being completely dominated by primary input features, facilitating the explanation of the influence of the two feature groups on tire characteristics. The accuracy and robustness of the model are suitable for the engineering requirements of FSAE. During the Formula Student China competition, based on on-track measured data, the tire model was co-simulated with VI-CarRealTime to quickly calculate the tire pressure required to achieve maximum lateral force. This effectively saved practice time before the race and helped the team achieve a third-place finish.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691934</guid>
    </item>
    <item>
      <title>Study of the Vortex Flow over a Generic Open-Wheel Race-Car</title>
      <link>https://trid.trb.org/View/2691875</link>
      <description><![CDATA[Open wheel race cars present a challenge to the aerodynamic designer because of the numerous wakes and vortices created by the various body components. The present study follows the development of a high-downforce race car and investigates possible vortex manipulations to increase its aerodynamic efficiency. The tools used for this study involved computational fluid dynamics and small-scale wind tunnel testing. Once the basic geometry of the racecar was finalized, cost effective measures were tested to improve its downforce to drag ratio. As an example, by fine tuning the position of different body components, such as the rear wing location relative to the underfloor diffuser exit, vehicle’s aerodynamic performance can be modified. The results of both the wind tunnel and the computational investigations indicated that such simple modifications can positively improve the race-car downforce to drag ratio. Also, once the baseline vehicle’s geometry was frozen and observing that the largest aerodynamic surface on the car is its underfloor, different vortex generators attached below the underfloor were tested to increase the vehicle’s downforce. The above modifications between the baseline racecar, and the car with the underfloor vortex generators resulted in a gain of 8.12% in downforce and 8.24% in lift to drag ratio.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691875</guid>
    </item>
    <item>
      <title>Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network</title>
      <link>https://trid.trb.org/View/2691857</link>
      <description><![CDATA[The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the control value for rear-wheel steering. Rear-wheel steering can reduce the turning radius during low-speed driving and enhance the racing car’s stability during high-speed cornering. This method was validated on a typical race track via CarSim-MATLAB co-simulation, resulting in reduced lap time. To meet the real-time computing requirements of FSAE, MATLAB was used to simulate the discretization results of vehicle parameters such as vehicle speed and front-wheel steering angle, generating a Look-Up Table for rear-wheel steering angles, which provides a feasible solution for real-vehicle tests. The racing car is equipped with a manual switch for the driver to operate. The driver can manually turn the rear-wheel steering function on or off when cornering or whenever they deem it necessary.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691857</guid>
    </item>
    <item>
      <title>How racial segregation contributes to disparities in pedestrian safety</title>
      <link>https://trid.trb.org/View/2663733</link>
      <description><![CDATA[Everyone deserves the right to walk safely in their neighborhoods. However, racial segregation can disproportionately escalate the risk of pedestrian crashes, exposing certain racial communities to higher risk levels. This study investigates racial disparities in the distribution of severe pedestrian crashes (fatal and injury-related) in terms of residential segregation in the City of Chicago, an urban area with pronounced racial segregation. The authors constructed a detailed Census Block Group–level dataset by integrating multiple data sources, including socio-demographic variables, built environment characteristics, crime rates, and crash records. The analysis reveals that although Black-majority block groups represent only 24% of Chicago’s population, they account for 37% of all severe pedestrian crashes, indicating significant racial disparities in pedestrian safety. The authors employed multiple indices to characterize racial segregation and classify neighborhoods into four areas: Black, Hispanic, White-majority, and Mixed communities. The authors developed Spatial Lag Negative Binomial (SLNB) model to examine the main drivers contributing to severe pedestrian crashes across these communities. Five major categories of variables were identified as significant predictors: exposure factors, socio-demographic characteristics, indicators of social exclusion, spatial and temporal conditions, and transit accessibility. The results indicate that while crash risk in White-majority areas is more associated with socio-demographic characteristics, Black and Hispanic-majority areas are disproportionately affected by infrastructure deficits, such as poor transit accessibility and inadequate nighttime lighting. Distinct patterns associated with segregated communities were highlighted, shedding light on unique challenges faced by these groups. The findings from this study provide valuable insights for city authorities focused on two critical goals: enhancing pedestrian safety and dismantling barriers to equitable access to safe walking environments for all communities.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:43:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663733</guid>
    </item>
    <item>
      <title>Transport equity in South Africa - How much progress was made over the last two decades?</title>
      <link>https://trid.trb.org/View/2632872</link>
      <description><![CDATA[Transport equity (justice) is a field of research that has been gaining momentum. As South Africa is the least equitable country in the world, according to the World Bank, the authors felt compelled to assess if this injustice is also present in the transport system. Availability, access, safety, security, affordability, inclusion, sustainability, human rights and equity are fundamental concepts of socially just transport. Consecutive South African Household Travel Surveys (2003, 2013 and 2020) were used to establish trends in travel times, mode use, travel purpose, as well as perceptions of road safety and personal security. Gender and race differences were analysed to investigate the (in)justice argument. Over the past two decades, there has been an increase in average travel time with evidence of racial disparity related to the travel time burden. The nation walks more, however, this is out of economic necessity, not environmental consciousness. While travel patterns between males and females only differ marginally in the South African context, in contrast with common literature, racial differences are significant. The Black community travels longer distances, mostly by walking and the use of (in)formal public transport. They are also more exposed to road safety and personal security risks. The analysis revealed that more metrics deteriorated over the past two decades than improved, leading to the conclusion that transport justice has not significantly improved, despite major investments in the public transport system, in the form of bus rapid transit systems, amongst others.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632872</guid>
    </item>
    <item>
      <title>Determination and description of a vehicle's maximum jerk capacity</title>
      <link>https://trid.trb.org/View/2646996</link>
      <description><![CDATA[Both Transient Optimal and Quasi-Steady-State vehicle modelling are frequently used for minimum time manoeuvre and minimum lap-time simulations for motorsport/vehicle development purposes. Quasi-Steady-State methods have been shown to perform such tasks with low computational cost but produce results with measurable deviation from equivalent Transient Optimal studies. It is hypothesised that bounding point-mass models with jerk limits will improve alignment with Transient Optimal simulations through the consideration of transient behaviours such as control application rates and inertia. This work presents a new method for the determination of a vehicle's maximum jerk capacity from a seven degree-of-freedom vehicle model. A range of results from this proposed technique are presented and show the sensitivity of jerk capacity to changes in control application rate limits and yaw inertia, as well as current vehicle velocity, acceleration, and jerk. It is proposed that further exploration of the validity of the jerk limit calculation method take place through characterisation of said limits and application to a point-mass model, after which comparison to Transient Optimal and Quasi-Steady-State equivalents can occur.]]></description>
      <pubDate>Mon, 23 Mar 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646996</guid>
    </item>
    <item>
      <title>Schwarz decomposition for parallel minimum lap-time problems: evaluating against ADMM</title>
      <link>https://trid.trb.org/View/2646994</link>
      <description><![CDATA[The Minimum Lap Time Problem (MLTP) remains a significant area of research, particularly in the motorsport context. This form of Optimal Control Problem (OCP) aims to minimise lap times on a specific track with a given vehicle. Various complexities in both vehicle and track models are employed across the literature to address optimal trajectory planning. While previous works have tackled MLTP as a singular task using a serial approach, the increasing model complexity and horizon length demands the utilisation of parallelisation techniques. This paper introduces a novel application of the Overlapping Schwarz Decomposition algorithm to address the MLTP. The algorithm divides the problem into smaller sub-problems based on different sectors of a track, distributing them among multiple processors. We validate and compare the Schwarz approach against a serial approach and the Alternating Direction Method of Multipliers (ADMM) in solving MLTP with over 2.5 million variables. Despite the general efficiency improvement of parallelisation compared to the serial approach, the Schwarz algorithm demonstrates superior speed, accuracy and robustness compared to ADMM. As a result of our findings, it emerges as the preferred choice when large-scale MLTPs need to be solved.]]></description>
      <pubDate>Mon, 23 Mar 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646994</guid>
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