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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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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>Effectiveness of Inductive Vehicle Charging to Alleviate EV Range Anxiety: Data Management Plan</title>
      <link>https://trid.trb.org/View/2709411</link>
      <description><![CDATA[This project, titled "Effectiveness of Inductive Vehicle Charging to Alleviate EV Range Anxiety", will evaluate the efficacy of inductive vehicle charging (IVC) in overcoming range anxiety for different electric vehicle (EV) users. It systematically categorizes different passenger and freight transportation user groups and investigates their use cases where these various users can reap benefits from IVC implementation. Considering different EV user groups, this proposal provides proof of concept for locations or scenarios in which IVC technology effectively removes range anxiety for light to heavy-duty vehicles. In addition, the proposal investigates the current IVC technology characteristics to assess the cost of IVC implementation and identify installation and maintenance requirements. The data collected during this project is from the literature, meeting with IVC technology manufacturer(s), and gathering information from the IVC pilots. This data management plan describes the data that will be collected and how it will be stored, accessed, and archived. All input data for the models will be stored in ASCII text files.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709411</guid>
    </item>
    <item>
      <title>Effectiveness of Inductive Vehicle Charging to Alleviate EV Range Anxiety</title>
      <link>https://trid.trb.org/View/2709410</link>
      <description><![CDATA[This study evaluates the efficacy, optimal placement, and economic viability of Inductive Vehicle Charging (IVC) technology as a strategic solution to facilitate electrification of the Michigan transportation sector. Motivated by the need to mitigate range anxiety and support high-utilization electric fleets, the research employs a multi-methodological approach combining economic modeling, transit network analysis, and macroscopic highway optimization. The findings demonstrate that while stationary IVC chargers are sufficient for smaller transit agencies, in-motion IVC is critical for larger, high-frequency transit systems to maintain operational continuity without fleet expansion. For intercity travel, optimization models reveal that high-power dynamic charging on key arterial corridors specifically in Southeast Michigan maximizes social welfare by complementing the existing DC fast-charging on key network. The study concludes that IVC offers a distinct economic advantage in scenarios characterized by high traffic density and fleet utilization, recommending targeted investment in shared infrastructure for transit and freight corridors.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709410</guid>
    </item>
    <item>
      <title>An Online Tool for Guiding Bus Fleet Decarbonisation Through Green Hydrogen and Electrification</title>
      <link>https://trid.trb.org/View/2581586</link>
      <description><![CDATA[The transition to zero emission bus (ZEB) fleets is accelerating. Two prevalent ZEB options that are often compared to each other are battery electric buses (BEBs) and fuel cell electric buses (FCEBs) fueled by green hydrogen. Hydrogen is labelled as green when it is produced by electrolysis powered by renewable electricity. From the perspective of a bus fleet operator or regional authority interested in replacing a conventional diesel bus fleet with one of these new technologies, it can be unclear which combinations of BEBs and FCEBs are most suitable in terms of cost, emission reduction, and capability to maintain regular operation of the bus fleet. This work develops the Enabling Support Tool (EST), an easy-to-use model that can assess the trade-offs between BEBs and FCEBs in terms of their technical performance, required infrastructure, cost, and emissions reduction potential. Using a novel input process that does not require complex drive-cycle data, the EST allows the user to quickly investigate the feasibility of a mixed fleet of BEBs and FCEBs, considering the effects of local climate conditions, road gradient, and varying bus payload on the daily range of BEBs. This enables users to explore the feasibility of different combinations of BEBs and FCEBs and thus guide cost-effective full fleet decarbonisation.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581586</guid>
    </item>
    <item>
      <title>Long-Distance Route Planning for Electric Vehicles Considering Range Anxiety and Fatigue Constraints</title>
      <link>https://trid.trb.org/View/2709165</link>
      <description><![CDATA[This research proposes to tackle two important issues in long-distance travel for electric vehicles (EVs): range anxiety and driver fatigue. A new route planning approach integrating the perception of psychological and physiological states is presented. This model quantifies range anxiety by integrating conditions such as battery depletion, charging station coverage, and traffic congestion and explicitly considers the evolution of driver fatigue influenced by continuous driving behavior and circadian rhythms, as well as a time-overlapping strategy for simultaneously arranging charging and rest tasks. To efficiently solve the route planning problem for large-scale road networks, this research develops an optimization approach using an A*-guided adaptive genetic algorithm (A*-AGA), which integrates heuristic search and evolutionary optimization. Simulation experiments on typical long-distance routes demonstrate that the approach is highly effective in reducing driver anxiety and fatigue, optimizing the total travel time, ensuring the route's feasibility, and greatly improving the long-distance EV driving experience.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709165</guid>
    </item>
    <item>
      <title>Electric Aircraft: FAA Is Evaluating Designs for Certification and Considering Long-Term Regulatory Approaches</title>
      <link>https://trid.trb.org/View/2706365</link>
      <description><![CDATA[Electric propulsion aircraft have the potential to lower operating costs, increase access to air service for regional airports, and reduce environmental impacts and noise from aviation. However, the Federal Aviation Administration (FAA) has not yet issued a type certification for a manned electric aircraft as of March 2026, and when such aircraft will be able to commercially operate is not clear. Section 1012 of the FAA Reauthorization Act of 2024 includes a provision for the U.S. Government Accountability Office (GAO) to assess the safe and scalable operation and integration of electric aircraft into the National Airspace System. This report describes (1) the types and uses of electric aircraft in development; (2) the extent of infrastructure deployed at U.S. airports to support electric aircraft, and any challenges airports face in deploying infrastructure; and (3) FAA’s approach to certificating the airworthiness of electric aircraft designs, and related challenges identified by aviation industry stakeholders. GAO analyzed literature on electric aircraft published between 2019 and 2024 and used information from these studies to supplement testimonial evidence from interviews with aviation industry stakeholders and federal officials. GAO also analyzed public information on government and industry efforts to develop electric aircraft. GAO interviewed officials from FAA, the National Aeronautics and Space Administration, the National Laboratory of the Rockies, and a nongeneralizable selection of 30 aviation industry stakeholders, including aircraft and engine manufacturers, airports, fixed-base operators, state departments of transportation, and a flight training school. Eight interviews were conducted as part of site visits to Washington State and Ohio.]]></description>
      <pubDate>Fri, 29 May 2026 15:36:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706365</guid>
    </item>
    <item>
      <title>Identifying Patterns of Real-World Charging Frequency for a Sample of Plug-In Hybrid Electric Vehicles in North America</title>
      <link>https://trid.trb.org/View/2692175</link>
      <description><![CDATA[Plug-in Hybrid Electric vehicles (PHEVs) have the capability to effectively utilize electricity from the grid as an energy source for powering an appreciable portion of the total vehicle miles travelled (VMT), thereby reducing greenhouse gas (GHG) emissions, since the Carbon Intensity (CI) of electricity is often less than that of liquid fuels in many parts of the world. Several real-world usage factors can affect the fraction of VMT electrified, with the frequency of charging being one of the most influential factors. Studies in recent years have attempted to characterize the real-world performance of PHEVs based on long-term average fuel consumption and/or other data flags in the readout from vehicle On-Board Diagnostics (OBD), but such approaches are unable to infer accurate estimates for the occurrence of charging events. This paper adopts an approach that relies on analysis of highly granular (trip by trip) information obtained from vehicles equipped with a data communication module (DCM) to infer the occurrence of charging events from change in the battery state of charge (SoC) between trips. Analysis of data obtained from a large sample of PHEVs (one full calendar year for hundreds of vehicles) in the US and Canada reveals three distinct patterns: i) vehicles that are consistently charged, ii) vehicles that are consistently not charged, and iii) vehicles with temporally varying frequency of charging. Unlike some other studies about PHEVs in other parts of the world, results of our sample for PHEVs in North America show that the majority are consistently charged, but with various frequency levels that are regionally dependent.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692175</guid>
    </item>
    <item>
      <title>Temperature Effects on Electric and Hybrid Vehicle Efficiency</title>
      <link>https://trid.trb.org/View/2701102</link>
      <description><![CDATA[This research evaluates the powertrain efficiency of select battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) tested at ambient temperatures of 20°F, 75°F, and 95°F using an AVL emissions test cell chassis dynamometer. BEV test vehicles include the Chevrolet Equinox EV, Ford Mustang Mach-E, and Tesla Model Y. HEV test vehicles include the Toyota Prius, Honda CR-V Hybrid, and Hyundai Tucson Hybrid. The objective of this study is to quantify temperature-related efficiency changes and assess whether hybrid powertrains mitigate efficiency losses more effectively compared to fully electric vehicles under hot and cold environmental conditions. The results indicate that hot and cold ambient temperatures—most notably cold conditions—substantially increase energy demand due to reduced battery discharge efficiency and elevated thermal management and cabin conditioning loads. The findings underscore the importance of incorporating seasonal and Heating, Ventilation, and Air Conditioning (HVAC)-related energy impacts into range planning and performance assessments, as real-world operating range can deviate significantly from Environmental Protection Agency (EPA)-rated values under non-ideal environmental conditions. The results of this study are intended to provide consumers, policymakers, and automotive stakeholders with objective data regarding electrified vehicle performance in cold and hot weather operation. The cost comparison between operating a BEV and an HEV provides a quantitative basis for informing consumers about which powertrain configuration may be most appropriate for their specific usage patterns and operating conditions.]]></description>
      <pubDate>Thu, 28 May 2026 16:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701102</guid>
    </item>
    <item>
      <title>Fuel Cell vs. Battery Electric Buses: Environmental, Economic and Operational Performance</title>
      <link>https://trid.trb.org/View/2580056</link>
      <description><![CDATA[Hydrogen fuel cell buses (FCBs) and battery electric buses (BEBs) represent two types of zero-emission drivetrains for air pollution reduction and decarbonization of public transport. In this work, the two bus technologies are compared in terms of their environmental, economic and operational performance. Real-world data from two European sites serve as basis for a carbon footprint (CF) calculation, a total cost of ownership (TCO) analysis and a performance assessment. The results indicate an advantageousness of BEBs compared to FCBs regarding greenhouse gas emissions and costs. The main reason for the higher climate impact of FCBs is the additional energy required to produce hydrogen, compared to the direct use of electricity in BEBs. The BEBs’ advantage in terms of TCO is mainly due to lower vehicle and energy costs. However, the results highly depend on the specific local conditions and assumptions. Furthermore, operation conditions such as range as well as flexibility play a decisive role in bus operators’ decision making. Here, FCBs show considerable advantages, making them favorable for long and/or demanding routes.]]></description>
      <pubDate>Tue, 26 May 2026 09:42:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580056</guid>
    </item>
    <item>
      <title>Understanding the charging behavior of electric vehicle drivers on long-distance trips – The roles of range regulation and human-automation cooperation</title>
      <link>https://trid.trb.org/View/2697880</link>
      <description><![CDATA[Advances in battery technology and charging infrastructure have improved long-distance electric vehicle (EV) travel. However, effective trip planning can be challenging. EV trip planners (EVTs) can support drivers in range regulation, yet their effectiveness and acceptance depend on how drivers experience the interaction. In this context, the authors introduce Driver Electric Vehicle Trip Planner Interaction Style (DEVTIS), which captures individual differences in reliance on or modification of system-generated recommendations. DEVTIS is assumed to be shaped by driver characteristics, experience, and perceived system cooperativity. The objective of the present study was to examine EV drivers' interaction with EVTs for range regulation on long distances and its underlying psychological dynamics. Through a correlational online study with 133 EV drivers, the authors assessed affinity for technology interaction and subjective range competence as personal characteristics variables, perceived cooperativity and DEVTIS as core interaction-related variables, trust and system acceptance as evaluative user experience measures and range stress and range utilization as EV range-regulation-related variables. Findings revealed that perceived cooperativity negatively predicted DEVTIS, indicating that higher perceived cooperativity was associated with less frequent modification of system recommendations. Moreover, perceived cooperativity positively predicted trust and system acceptance, while DEVTIS negatively predicted both. Subjective range competence showed a slight negative association with range stress. These findings underscore the importance of understanding human-automation cooperation in EVT usage. Considering perceived cooperativity and individual interaction tendencies supports the design of adaptive, driver-centered EVTs, facilitating a seamless transition to EV-based mobility. Promoting perceived cooperativity may enhance trust and system acceptance, while excessive modification of EVT recommendations could undermine these factors.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697880</guid>
    </item>
    <item>
      <title>Charging ahead unfairly: An examination of temporal shifts in electric vehicle supply equipment accessibility across California’s communities</title>
      <link>https://trid.trb.org/View/2694626</link>
      <description><![CDATA[With the expansion of electric vehicle (EV) use, it has become increasingly important to ensure that public electric vehicle supply equipment (EVSE) meets growing demand and is accessible to everyone in need. Yet a meaningful assessment of how communities have benefited from EVSE expansion requires a lens that goes beyond static snapshots of EVSE accessibility. In this study, the authors conduct a longitudinal analysis over the past decade (2014–2024) to uncover how public charging infrastructure has evolved and how accessibility has shifted over time in the State of California. The authors examine EVSE accessibility across communities using a mixture of descriptive and statistical methods. A core feature of the analysis is that the authors examine changes in accessibility over time using a generalized additive model (GAM) with time-varying effects. The findings suggest that accessibility patterns across California’s communities have varied according to three distinct stages in EVSE deployment: early deployment and planning, accelerated expansion, and coordinated growth. The GAM analysis identifies a clear temporal shift in relative EVSE accessibility levels across income groups: lower-income locations exhibited relative advantages prior to 2017, but higher-income locations became increasingly advantaged during the accelerated expansion phase in later years. Majority Black locations maintained above-average accessibility but experiencing a gradual decline over time, while majority Hispanic locations remained consistently below average throughout the study period. These time-varying findings enhance understanding of how disparities unfold across deployment stages and motivate more adaptive, stage-aware EVSE policy design.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694626</guid>
    </item>
    <item>
      <title>Assessment of an Integrated Cooling/HVAC Circuit for Electric Heavy Quadricycles</title>
      <link>https://trid.trb.org/View/2581595</link>
      <description><![CDATA[Within the last decades, an always increasing attention has been addressed to the development and market diffusion of alternative powertrains, either hybrid or fully electric. Especially for electric powertrains some open points are nowadays still present with respect to thermal management and cabin comfort, which are intended to be addressed in the present study. This is the reason why the European Commission is striving the research towards the development of innovative and efficient electric powertrains. Within this framework, the REFLECTIVE project aims at developing an electric heavy quadricycle equipped with a HVAC (heating ventilation air conditioning) module integrated with the powertrain/charging cooling system, with the aim of reusing part of the heat generated at the powertrain during driving conditions to heat up the cabin, with the consequence of reducing the thermal power requested at the electric cabin heater to fulfil this task. Although an additional heat exchanger is required, it is possible to guarantee a certain amount of heat preventing the use of the battery for the electrical heater activation. Moreover, the battery thermal management as well can be done also using hot/fresh air generated by the HVAC sub-system. By this way, several thermal loads can be managed through the same integrated circuit with apparent benefit in terms of energetic efficiency, despite some complexity is introduced. The aim of this paper is to assess the effectiveness of this solution on the vehicle range and battery state of charge, through the integration of a 1-D model of the cooling circuit with a 0-D model of the entire vehicle. Different driving conditions, namely summer and winter scenarios and different speed profiles, will be considered. Results show that the HVAC and cooling systems have a huge effect on range reduction with respect to the range estimated, but at the same time, but the benefit of the recuperator can be also assessed.]]></description>
      <pubDate>Mon, 18 May 2026 11:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581595</guid>
    </item>
    <item>
      <title>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://trid.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>A Comprehensive Leakage-Free Forecasting Pipeline for Segmented Time Series: Application to Cross-Trip State-of-Charge Prediction in Automated Electric Vehicles</title>
      <link>https://trid.trb.org/View/2659151</link>
      <description><![CDATA[The rapid adoption of Electric Vehicles (EVs) in the global pursuit of energy efficiency and carbon neutrality necessitates effective strategies to mitigate their carbon footprint and enhance operational stability. Similarly, in order to achieve Sustainability Development Goals, a promising solution toward green mobility, which is gaining ground nowadays, constitutes Automated Vehicles (AVs), which are EVs having the capability to move autonomously, without the need for a driver. One of the most critical factors regarding energy efficiency is the optimal management of energy consumption of AVs. This research study explores the application of machine learning (ML) models for State-of-Charge (SoC) forecasting in AVs, crucial for addressing challenges such as range anxiety and grid overloading. Leveraging real-life EV data from automated minibuses in Gothenburg, Sweeden, a comprehensive pipeline is proposed for data pre-processing, feature selection, and model training. With a focus on predicting SoC several minutes ahead, various ML techniques, including linear regression, ridge regression, lasso regression, and elastic-net regression are embedded in a pipeline specifically developed to overcome the challenge of training time-series models on discontinuous data segments, corresponding to discharge cycles. This pipeline is called Cross-Segment-Leakage-Free (CSLF). The results demonstrate the efficacy of CSLF, with the best-performing model achieving a Mean Absolute Error (MAE) of 0.92 in a forecasting horizon of 30 minutes, representing a significant improvement over baseline models. The study underscores the importance of meaningful pre-processing and model selection in SoC consumption forecasting for AVs, offering insights into future research directions and deployment strategies for enhancing EV efficiency and grid stability.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659151</guid>
    </item>
    <item>
      <title>Assessment of the factors affecting electric vehicle charging stations demand prediction</title>
      <link>https://trid.trb.org/View/2682141</link>
      <description><![CDATA[Electric vehicles (EVs) represent a pivotal sustainable mobility solution for urban decarbonization. This work investigates how proximity to main urban attraction points, such as restaurants, supermarkets, tourist sites, schools, and hospitals, influences EV charging demand within a fixed service range. Given the critical role of charging accessibility in mitigating EV range limitations, the study proposes a stepwise linear regression to quantify the relationship between charging station utilization and driving distances to nearby points of interest (POIs). Our analysis identifies statistically significant demand predictors, providing practical insights into strategic infrastructure planning. Applied to the Lombardy region, Italy, findings can support data-driven optimization of charging station placement, balancing urban accessibility with equitable spatial distribution. The results can contribute to sustainable urban mobility frameworks by integrating POI-based demand modelling into EV infrastructure expansion strategies.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682141</guid>
    </item>
    <item>
      <title>The flight range estimation of commercial aircraft – Boeing 737 max 800 considering the transition from internal combustion to SOFC (solid oxide fuel cell) engines</title>
      <link>https://trid.trb.org/View/2625517</link>
      <description><![CDATA[The range of the aircraft is the primary concern when operating commercial airliners. In transitioning from conventional-engine commercial airliners to carbonless ones, the flight range has been investigated in this paper to provide design modification metrics for future electrified aircraft designs and processes. The total weight of both cases is fixed to compare the performance of two different engines directly. Due to the use of ammonia for fuel cell systems, such as SOFC with a turbogenerator (TG), the engines are heavier and have a lower fuel capacity than jet engines. The lower range associated with SOFC-TG propulsion highlights the need to significantly enhance aerodynamic efficiency to achieve parity or improve operational capabilities. Given the pivotal role of aerodynamic design in mitigating drag forces and optimizing fuel efficiency, innovative strategies and advancements in airframe design have become imperative. By prioritizing aerodynamic enhancements tailored to the unique requirements of SOFC-TG propulsion systems, such as optimizing weight distribution and minimizing drag coefficients, it is conceivable to bridge the range disparity between conventional and alternative fuel-powered aircraft variants. This paper presents a critical parameterization analysis for considering a conversion of an existing commercial aircraft. With fixed operational conditions and weight and volumetric specifications, a crucial aerodynamic performance, flight range, is revealed as a function of the adopted propulsion system. This effort will extend to any “conversion” or design and operation modification studies for future aircraft.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625517</guid>
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