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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Integrating Towable Mobile Charging Hubs into Electric Vehicle Route Planning with Time Window Constraints and Partial Recharging</title>
      <link>https://trid.trb.org/View/2735018</link>
      <description><![CDATA[As electrification technology for medium- to heavy-duty trucks becomes more mature, an increasing number of logistics companies are integrating electric trucks into their fleets. However, limitations such as limited driving range and increased labor costs associated with long charging durations still need to be addressed. To tackle these challenges, towable mobile charging hubs (MCHs) can be adopted as an alternative charging solution for electric delivery fleets. This article integrates towable MCHs into the traditional electric vehicle routing problem with time window constraints (EVRPTW) and nonlinear partial recharging. A hybrid ant colony system (HACS)-based algorithm is introduced to co-optimize decisions between intraroute charging using fixed charging stations (FCSs) and interroute charging using MCHs, routing for delivery vehicles (DVs) and MCHs, and charging schedules for DVs. Case study results demonstrate that the proposed algorithm can balance the benefits between intraroute charging using FCSs and interroute charging using MCHs, achieving up to a 12.16% reduction in operational costs on Solomon instances and 11.27% on a real-world dataset.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735018</guid>
    </item>
    <item>
      <title>Quantification of Truck Electrification Damage to Flexible Pavements</title>
      <link>https://trid.trb.org/View/2767461</link>
      <description><![CDATA[The transition to electrified freight transport is poised to introduce heavier axle loads because of the placement of battery packs and higher torque that is instantaneously available from the electric motor design. The current road infrastructure may not be equipped to meet such technologically driven demands, further straining the already deteriorating road network. This study quantifies the impact of heavy-duty electric vehicles (HDEVs) on the structural and near-surface behavior of flexible pavements. Three-dimensional finite element models of a truck tire, loaded on typical interstate, arterial, rural, and state pavement sections in Illinois, U.S., were analyzed. The resulting strains served as inputs into mechanistic-empirical transfer functions, and a cumulative damage framework is proposed. A parametric assessment of the combined impact of battery placement configuration and slip ratio (acceleration) evidenced two clear trends: 1) axle load redistribution because of battery-placement-influenced tensile, shear and compressive strains throughout the depth, accelerating bottom-up and top-down cracking, and rutting, and 2) higher acceleration-affected near-surface shear strains, driving shoving and shear-driven top-down cracking as the critical distresses. The Electric Truck Adjustment (ETA) index is introduced to evaluate relative pavement damage induced by different HDEV battery placement scenarios and acceleration with respect to internal combustion engine trucks. A step-by-step implementation scheme to embed the ETA framework into pavement design guidelines is detailed. Using the ETA parameter, optimum HDEV configurations reduced predicted service life by up to 30% for a typical interstate, rural, and state road, and up to 47% for arterial roads.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767461</guid>
    </item>
    <item>
      <title>Optimization of Transportation Routes for Electric Container Trucks under Different Charging Modes</title>
      <link>https://trid.trb.org/View/2767454</link>
      <description><![CDATA[With the advancement of energy conservation and emission reduction initiatives at ports, electric container trucks are being increasingly adopted in port collection and distribution logistics. The charging strategy for electric container trucks directly affects their transportation routing. This paper considers three different charging strategies for electric container trucks and develops the optimization models for their transportation routes. An improved genetic algorithm is designed to solve the models. Based on real-world data from Logistics Company Q, an empirical analysis is conducted. The comparison analysis results show that the lowest transportation cost occurs under the rapid battery-swapping mode, followed by the partial charging strategy, while the full charging strategy incurs the highest transportation cost. The sensitivity analysis results show that, under the full charging mode, total distribution costs increase significantly as the delivery time window penalty coefficient rises. Under the partial charging mode, when the charging amount is between 60% and 80% of the battery capacity, the total distribution cost decreases with the increase of the charging amount. However, when the charging amount is between 80% and 100% of the battery capacity, the total distribution cost also exhibits increasing trend. Under the battery-swapping mode, as the rated battery capacity gradually increases from its original level, the total transportation cost gradually decreases. However, it introduces operational challenges, especially time conflicts, which may have a negative impact on the service quality of the transportation company.]]></description>
      <pubDate>Thu, 27 Aug 2026 14:26:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767454</guid>
    </item>
    <item>
      <title>Single-Charge Feasibility Evaluation of Synthetic Electric New York City Truck Tours</title>
      <link>https://trid.trb.org/View/2581450</link>
      <description><![CDATA[For urban freight delivery trucks to switch to electric batteries, they would need to either need to operate at a short enough range that does not require recharging in the middle of a “single-charge shift”, or to deviate from their current tours to recharge en-route, which can be costly. However, limitations in data prevent policymakers from identifying the fraction of the market feasible for single charge shifts as the likely adopters. We propose using synthetic tours of trucks to identify this portion of the fleet for electrification. The framework is applied in a case study to a synthetic aggregate urban truck tour population in New York City to assess what portion of total freight routes can be delivered with electric trucks. Results suggest 64.6% percent of tours are electrifiable with the average tour consuming 45.2 kWh on a single charge. This scenario would lead to consumption of an extra 6.4 GWh/day of energy or a 4.5% increase for New York City. The most and least electrifiable industries are reported along with industries consuming the most kWh. Feasible operational cost conditions are established based on price per kWh versus price per gallon, added road damage due to the battery weight is assessed, and emission investment breakpoints are calculated based on CO2 emissions reductions from operations versus increases from battery construction.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581450</guid>
    </item>
    <item>
      <title>International Standard for Electric Road System</title>
      <link>https://trid.trb.org/View/2581448</link>
      <description><![CDATA[This paper describes the international standard for the electric road system for the sustainable mobility and transportation for smart city and communities. The international standard is being created by ISO/TC268/SC2/WG2. The electric road system is particularly needed for long-haul transport battery powered electric vehicles. The framework, concept of operations (How roadside feeding electric road system can be configured and integrated in a sustainable mobility and transportation), and system components are explained.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581448</guid>
    </item>
    <item>
      <title>Evaluation Framework of Next Generation Electric Trucks</title>
      <link>https://trid.trb.org/View/2581444</link>
      <description><![CDATA[To measure the success of a research and development project and assess its impact, an evaluation methodology has to be established. The proposed methodology in the NextETRUCK project follows best practices in validation and draws on insights from previous support projects such as CONVERGE and FESTA. It outlines a set of research hypotheses and associated goals for different innovation aspects of the project, forming the basis for evaluation. Key elements of this evaluation plan include defining Key Performance Indicators (KPIs). A total of 28 KPIs have been identified, covering areas such as vehicle and charging performance, digital tools, driver/fleet operator experiences, and market/total cost of ownership considerations. These KPIs can be measured during the demonstration and digital twin operations, using both quantitative and qualitative measures. Objective data, subjective evaluations, and inputs such as vehicle data, questionnaires, and driver interviews can all contribute to the assessment. The evaluation plan takes a structured approach, detailing each KPI’s description, assessment methods, parameters, and necessary information to address potential risks and challenges during the evaluation phase.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581444</guid>
    </item>
    <item>
      <title>Active Thermal Control for Thermally Safe Operation of Electric Truck Inverters during Kerbstone Climbing</title>
      <link>https://trid.trb.org/View/2732054</link>
      <description><![CDATA[Electric trucks are exposed to short-duration high-current events, such as kerbstone climbing, where the traction inverter operates at high current and zero or very low output frequency. Under these conditions, the junction temperature of the power semiconductors can rapidly approach critical limits. Conventional mitigation strategies rely on torque limitation or hardware oversizing, which either degrade vehicle performance or increase system cost. This article investigates the application of Active Thermal Control to enhance dynamic current capability and lifetime utilisation. Three strategies are analysed, namely gate voltage increase, switching frequency reduction, and discontinuous pulse width modulation, together with selected hybrid combinations. A simulation framework integrating loss estimation, state space thermal modelling, and cycle-counting based lifetime evaluation is developed and experimentally validated using a dedicated back-to-back test bench with direct junction temperature measurement. Results show that the combined gate voltage and switching frequency strategy reduces the junction-to-ambient temperature rise by approximately 42% and increases the allowable number of climbing events by up to ten times for a peak current of 135 A (0.9 pu). For a fixed thermal constraint, the same strategy enables about 35% higher current. The study also analyses the trade-offs associated with each ATC strategy, highlighting both their thermal and lifetime benefits and their potential impact on current quality and implementation complexity, among other aspects. These findings support the integration of ATC in electric vehicle traction systems for lifetime-aware and performance-oriented operation.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732054</guid>
    </item>
    <item>
      <title>Collaborative planning framework for heavy-duty truck electrification: corridor-port integrated energy systems</title>
      <link>https://trid.trb.org/View/2737142</link>
      <description><![CDATA[Deep decarbonisation of heavy-duty truck freight is essential for global carbon neutrality. As vital intermodal hubs, ports can support the development of green freight corridors. However, existing research on port integrated energy systems primarily focuses on internal logistics and vessels, neglecting corridor- port energy-demand coupling. To bridge this gap, we propose a “Corridor-Port Integrated Energy System” (C-PIES) framework and develop a two-stage distributionally robust optimisation model to address uncertainties in renewable energy generation and freight traffic flow. Using high-frequency GPS trajectory data, ambiguity sets are constructed to support robust scheduling of port renewable energy for corridor traffic-driven loads. A case study of Quanzhou Port shows that C-PIES enables source-load-storage coordination through battery swapping stations reducing system cost by 42.89% and carbon emissions by 50.2% compared with the isolated mode. The results demonstrate the potential of ports as regional energy hubs for low-carbon freight corridors.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737142</guid>
    </item>
    <item>
      <title>Electrifying Saudi Road Freight: A Total-Cost-of-Ownership based Optimization Analysis</title>
      <link>https://trid.trb.org/View/2714093</link>
      <description><![CDATA[This study assesses when and where battery electric trucks can economically replace diesel trucks in Saudi Arabia’s road freight sector under Vision 2030. A Saudi specific total cost of ownership model is developed using local fuel prices, electricity tariffs, duty cycles, and grid carbon intensity, and extended with a cost minimization prototype that internalizes emissions through an internal carbon price. The analysis covers medium duty urban and regional fleets and heavy duty long haul operations, with robustness tested through Monte Carlo simulations that vary energy prices, grid emissions, and policy shocks. Results show that medium duty battery electric trucks operating in depot based fleets are already cost competitive under current industrial electricity tariffs, with managed charging strengthening this advantage. Heavy duty long haul trucks remain cost positive but approach parity when the diesel to electricity price ratio widens or standardized corridor charging is available. Across scenarios, relative energy prices dominate cost outcomes, while carbon pricing and grid decarbonization primarily affect emissions. The findings support a phased electrification strategy prioritizing medium duty fleets and targeted heavy duty pilots.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714093</guid>
    </item>
    <item>
      <title>A Lane-Change Trajectory Planning Strategy Based on Dynamic Safety Boundaries</title>
      <link>https://trid.trb.org/View/2727931</link>
      <description><![CDATA[Lane-change trajectory planning is a key technology for ensuring the driving safety of autonomous electric trucks. However, under extreme driving conditions such as high speeds or low tire-road friction coefficient, ensuring both the safety of trajectory planning and the stability of vehicle motion remains a major challenge. This paper proposes a lane-change trajectory planning strategy based on dynamic safety boundaries (LCTP-DSB), aiming to achieve coordinated optimization among trajectory smoothness, collision avoidance, and vehicle stability. First, differential manifold theory is introduced to characterize the evolution of safety boundaries in nonlinear systems. The impacts of external disturbances and control inputs on vehicle stability are analyzed, and dynamic constraints for lane-change trajectory planning in autonomous electric trucks are established. Second, to efficiently solve the dynamic constraints, a sequential region filtering and progressive optimization (SRFPO) method is proposed. By partitioning the safety boundary, this method enables rapid screening of feasible solutions and global optimal solution search. In the trajectory planning process, the transfer position is dynamically selected in response to the current dynamic state of the vehicle. The trajectory is divided into a lane-change phase and lane-alignment phase, and the final optimal lane-change trajectory is obtained through trajectory stitching and optimization. Finally, the proposed method is validated via simulation and HIL testing, confirming its effectiveness in exploiting vehicle stability limits during trajectory planning.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727931</guid>
    </item>
    <item>
      <title>Multi Speed – Multi Motor Torque Vectoring Control for a Commercial Electric Truck to Enhance Drivability and Performance</title>
      <link>https://trid.trb.org/View/2724705</link>
      <description><![CDATA[This paper explores the requirement of multi speed – multi motor torque vectoring in a battery electric commercial truck. The area of focus was to compare the vehicle performance and range of a BEV truck with conventional central drive single motor configuration with the same vehicle consisting of a multi speed – multi motor torque vectoring control strategy. Through this exercise, we have analysed the motor power and torque requirements to meet the vehicle performance along with the required reduction ratios. A MATLAB based vehicle model is used to simulate the effect of multi motor operation on the vehicle range. Also simulated the effect of torque vectoring control algorithm on the vehicle performance like steady state cornering(SSC), Double Lane change (DLC), Off road dive Cycle, vehicle stability and turning circle diameter(TCD).]]></description>
      <pubDate>Mon, 27 Jul 2026 09:06:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724705</guid>
    </item>
    <item>
      <title>Projected cost competitiveness of zero-emission trucks in Australia</title>
      <link>https://trid.trb.org/View/2686797</link>
      <description><![CDATA[This study compares the Total Cost of Ownership (TCO) of battery-electric trucks (BETs), fuel cell trucks (FCTs), and diesel trucks (DTs) under Australian conditions, using a bottom-up simulation model based on real-world data on fuel consumption, payload, dwell-time, and costs, excluding government incentives.TCO projections, aligned with Australia's decarbonization goals to 2050, indicate that zero-emission trucks (Gross Weight Mass 15.5 t, 22.5 t and 42.5 t) are expected to reach cost competitiveness with diesel by 2050. BETs currently exhibit lower TCO for back-to-base operations, whereas FCTs are more competitive for long-haul applications. By 2050, FCTs are projected to outperform both BETs and DTs across all freight tasks, driven by lower CAPEX and payload capacity gains of up to 1 tonne compared to DTs.Driver wages constitute the largest cost component (30 – 70%), followed by CAPEX in back-to-base and fuel in long-haul operations. Fuel consumption is analyzed across 50 – 100% payload cases. For similar trucks, literature values generally cluster closer to the 50% load case in Australia, suggesting lower payload utilization than is typical in Australia.These findings highlight the need for pilot projects, infrastructure planning, and policy support tailored to Australia's unique freight conditions to accelerate the adoption of zero-emission trucks.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686797</guid>
    </item>
    <item>
      <title>Dynamic charging of electric trucks – a necessary addition to charging stations</title>
      <link>https://trid.trb.org/View/2703950</link>
      <description><![CDATA[Electrification of heavy-duty road freight transport is an important step to reduce the carbon emissions of the transport sector. Major investments are being considered for the transitioning of truck fleets with heavy batteries as well as new charging infrastructure. Until recently the emphasis for infrastructure has been on charging stations, while dynamic charging is an important alternative to consider. This paper summarizes recent research results about dynamic charging and argues for integrative network design, where stationary and dynamic technologies work together. It concludes with main research & development challenges.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703950</guid>
    </item>
    <item>
      <title>The charger location problem with routing and driver working hours for long-haul electric heavy-duty trucks</title>
      <link>https://trid.trb.org/View/2681790</link>
      <description><![CDATA[The limited network of chargers for electric heavy-duty trucks (eHDTs) hinders a widespread adoption due to their shorter operating range compared to diesel engines. The charger location problem seeks to determine the optimal number and location of chargers in a transportation network to support efficient eHDT logistics operations. Moreover, commercial drivers’ hours of service (HOS) are regulated by law, requiring careful planning of driving, breaks, and rest periods. In this context, effective vehicle scheduling and routing are crucial to increasing punctuality and safety in road freight transport. Neglecting these operational aspects in the charger location problem can lead to suboptimal or even infeasible decisions. The aim of this paper is to develop, model and solve the charger location problem with routing and driver’s working hours. The decisions include the long-haul electric vehicle routing, and the scheduling of drivers respecting HOS requirements by determining the locations, types and number of chargers. The problem is modeled by transforming the road network into a communication-time-expanded network through a temporal discretization process. We also present two mechanisms to reduce the model size. We conduct extensive numerical experiments in order to demonstrate the efficiency of the proposed optimization methods and to evaluate the impact of several features on the routing and scheduling of the eHDTs.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:12:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681790</guid>
    </item>
    <item>
      <title>Physics-informed attention on temporal fusion transformer for multivariate truck range forecasting</title>
      <link>https://trid.trb.org/View/2711552</link>
      <description><![CDATA[Accurate real-time forecasting of remaining vehicle range remains a challenge, particularly under varying payloads, road gradients, and environmental conditions. Conventional Temporal Fusion Transformers (TFTs) utilize attention mechanisms to dynamically weight historical inputs but may fail to capture explicit physical relationships that are critical for accurate predictions in heavy-duty electric trucks. This paper introduces a novel approach to integrating physical vehicle dynamics directly into the attention mechanism of TFTs. Our method, Physics-Informed Attention for TFT (PIA-TFT), modifies attention calculation by injecting physics-based relevance scores derived from vehicle speed, payload, road gradients, and other physical parameters, improving interpretability and model accuracy under operational conditions. Empirical evaluations conducted with real-world data from electric trucks demonstrate that the PIA-TFT reduces prediction errors compared to standard TFTs by up to 18%. Our approach is a step towards more physically consistent and explainable deep learning architectures for automotive forecasting tasks.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:51:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711552</guid>
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