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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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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      <title>Trade-offs in aviation impacts on climate favour non-CO₂ mitigation</title>
      <link>https://trid.trb.org/View/2587106</link>
      <description><![CDATA[Climate assessments of civil aviation have consistently quantified the dominant climate-forcing components: (1) CO₂ emissions, (2) NOx (NO + NO₂) emissions and (3) persistent contrails. All three components exert a positive radiative forcing (RF) and lead to climate warming of similar magnitudes. The aviation community is actively seeking to reduce its climate footprint through advanced engine technologies, more sustainable aviation fuel and optimal routing plans. These approaches usually involve a trade-off of CO₂ against NOx or contrails (non-CO₂), such as burning 1% more fuel to decrease contrail RF by 4%. Here, we show that a climate-trade-off risk curve derived from uncertainties in the RF components can give the probability that a specified trade-off ratio will produce a climate benefit. For each component, we calculate the integrated effective RF resulting from 1 year of flights: global warming per activity (GWA). The complementary cumulative probability distribution of the GWA(non-CO₂) to GWA(CO₂) ratio results in a climate-trade-off risk curve giving the likelihood of a positive climate outcome as a function of the trade-off-CO₂ to trade-off-non-CO₂ ratio, because the product, GWA x trade-off, should be the same for both. We find a likely (67%) chance of climate mitigation on a 100-year time horizon for the above suggested ratio of 1:4, favouring proposed non-CO₂ mitigation efforts with ratios smaller than this.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587106</guid>
    </item>
    <item>
      <title>The Potential Scale-Up of Sustainable Aviation Fuels Production Capacity to Meet Global and EU Policy Targets</title>
      <link>https://trid.trb.org/View/2685584</link>
      <description><![CDATA[Sustainable aviation fuels (SAF) can reduce aviation greenhouse gas emissions, yet their production scale-up to meet policy goals remains unexplored. Here, we describe the Global SAF Capacity Database to quantify global and European Union (EU) SAF capacity, comparing it to production capacity announcements. Despite announcements of 9.1 Mt year−1 (2.2 Mt year−1 in the EU) by 2024 and 38.9 Mt year−1 (9.3 Mt year−1 in the EU) by 2030, only 24% (26% in the EU) of the announced capacity was realized on time by 2024. Over 40% of the announced capacity for 2030 risks delays or cancellations. Using a diffusion model parametrized by announced capacity, realization rates, expected demand, and historical growth analogs, we calculate SAF potential scale-up to meet net-zero carbon emissions by 2050. Even if SAF follows the rapid scale-up of solar and wind energy, the global and EU capacity will fall short of respective targets by 42% and 18% in 2030, and 7% and 5% in 2050.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685584</guid>
    </item>
    <item>
      <title>The Link Between Low-Stress Bicycle Facilities and Bicycle Commuting</title>
      <link>https://trid.trb.org/View/2569549</link>
      <description><![CDATA[Existing research links standard bicycle lanes with increased levels of bicyclist commuting. Here the authors question how newer facility types fare relative to standard bicycle lanes. Using 6 years of longitudinal data across 14,011 block groups in 28 US cities, the authors find that block groups that installed protected bicycle lanes experienced bicycle commuter increases 1.8 times larger than standard bicycle lane block groups, 1.6 times larger than shared-lane marking block groups and 4.3 times larger than block groups that did not install bicycle facilities. Focusing on mileage, protected bicycle lane mileage installed was significantly associated with bicycle commuter increases 52.5% stronger than standard bicycle lane mileage and 281.2% stronger than shared-lane marking mileage. The results suggest that lower-stress bicycle facilities—such as protected bicycle lanes—are significantly associated with larger increases in ridership at the block-group level compared with higher-stress facilities such as standard bicycle lanes and shared-lane markings.]]></description>
      <pubDate>Tue, 08 Jul 2025 13:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569549</guid>
    </item>
    <item>
      <title>The closing longevity gap between battery electric vehicles and internal combustion vehicles in Great Britain</title>
      <link>https://trid.trb.org/View/2521638</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 11 Mar 2025 13:45:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521638</guid>
    </item>
    <item>
      <title>Dynamic cycling enhances battery lifetime</title>
      <link>https://trid.trb.org/View/2521637</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 11 Mar 2025 13:45:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521637</guid>
    </item>
    <item>
      <title>Effects of electric vehicle charging stations on the economic vitality of local businesses</title>
      <link>https://trid.trb.org/View/2437984</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 09 Oct 2024 14:19:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2437984</guid>
    </item>
    <item>
      <title>A matched case-control analysis of autonomous vs human-driven vehicle accidents</title>
      <link>https://trid.trb.org/View/2417113</link>
      <description><![CDATA[]]></description>
      <pubDate>Mon, 12 Aug 2024 16:50:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417113</guid>
    </item>
    <item>
      <title>Low-latency automotive vision with event cameras</title>
      <link>https://trid.trb.org/View/2387419</link>
      <description><![CDATA[The computer vision algorithms used currently in advanced driver assistance systems rely on image-based RGB cameras, leading to a critical bandwidth-latency trade-off for delivering safe driving experiences. To address this, event cameras have emerged as alternative vision sensors. Event cameras measure the changes in intensity asynchronously, offering high temporal resolution and sparsity, markedly reducing bandwidth and latency requirements. Despite these advantages, event-camera-based algorithms are either highly efficient but lag behind image-based ones in terms of accuracy or sacrifice the sparsity and efficiency of events to achieve comparable results. To overcome this, here the authors propose a hybrid event- and frame-based object detector that preserves the advantages of each modality and thus does not suffer from this trade-off. The method exploits the high temporal resolution and sparsity of events and the rich but low temporal resolution information in standard images to generate efficient, high-rate object detections, reducing perceptual and computational latency. The authors show that the use of a 20 frames per second (fps) RGB camera plus an event camera can achieve the same latency as a 5,000-fps camera with the bandwidth of a 45-fps camera without compromising accuracy. The approach paves the way for efficient and robust perception in edge-case scenarios by uncovering the potential of event cameras.]]></description>
      <pubDate>Mon, 10 Jun 2024 15:12:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2387419</guid>
    </item>
    <item>
      <title>Last-mile delivery increases vaccine uptake in Sierra Leone</title>
      <link>https://trid.trb.org/View/2370842</link>
      <description><![CDATA[Less than 30% of people in Africa received a dose of the COVID-19 vaccine even 18 months after vaccine development. Here, motivated by the observation that residents of remote, rural areas of Sierra Leone faced severe access difficulties, the authors conducted an intervention with last-mile delivery of doses and health professionals to the most inaccessible areas, along with community mobilization. A cluster randomized controlled trial in 150 communities showed that this intervention with mobile vaccination teams increased the immunization rate by about 26 percentage points within 48-72 h. Moreover, auxiliary populations visited community vaccination points, which more than doubled the number of inoculations administered. The additional people vaccinated per intervention site translated to an implementation cost of US $33 per person vaccinated. Transportation to reach remote villages accounted for a large share of total intervention costs. Therefore, bundling multiple maternal and child health interventions in the same visit would further reduce costs per person treated. Current research on vaccine delivery maintains a large focus on individual behavioral issues such as hesitancy. The study demonstrates that prioritizing mobile services to overcome access difficulties faced by remote populations in developing countries can generate increased returns in terms of uptake of health services.]]></description>
      <pubDate>Wed, 15 May 2024 15:27:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2370842</guid>
    </item>
    <item>
      <title>Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment</title>
      <link>https://trid.trb.org/View/2343255</link>
      <description><![CDATA[Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. The authors discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. The authors demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.]]></description>
      <pubDate>Fri, 19 Apr 2024 09:38:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2343255</guid>
    </item>
    <item>
      <title>Learning Naturalistic Driving Environment With Statistical Realism</title>
      <link>https://trid.trb.org/View/2348169</link>
      <description><![CDATA[For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, the authors develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of the authors' knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.]]></description>
      <pubDate>Fri, 15 Mar 2024 16:35:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348169</guid>
    </item>
    <item>
      <title>Fast charging of energy-dense lithium-ion batteries</title>
      <link>https://trid.trb.org/View/2062753</link>
      <description><![CDATA[The possibility of building a 90 kWh electric vehicle (EV) pack with a 300-mile cruise range has been made manifest with advances in lithium-ion batteries - specific energies of 250-300 Wh kg-¹ have been reached for these batteries with nickel-rich layered oxide cathodes and graphic anodes. However, a drawback to the use of such massive batteries to mitigate range anxiety is that due to the limited raw resource supply and excessive high cost, they are ineffective for mainstream EV adoption. The downsizing of EV batteries can be enabled by ten-minute fast charging, resulting in both sustainability and affordability without inducing range anxiety. Unfortunately, for energy-dense batteries of more than 250 Wh kg-¹ or higher than 4 mAh cm-², fast charging continues to be a significant challenge. The authors in this case use a material-agnostic approach based on asymmetric temperature modulation combined with a thermally stable dual-salt electrolyte in order to attain charging of a 265 Wh kg-¹ battery to 75% (or 70%) state of charge in 12 (or 11) minutes for in excess of 900 (or 2,000) cycles. This equates to a 500,000 mile range where every charge is a fast charge. In addition, the authors develop a digital twin of this type of battery pack in order to evaluate its cooling and safety, as well as demonstrate that thermally modulated 4C charging requires only air convection. An inherently safe and compact route to cell-to-pack development is presented. As a result of this rapid thermal modulation method to yield highly active electrochemical interfaces only during fast charging, there is the significant possibility of achieving both fast charging and stability of next-generation materials, including anodes such as lithium metal and silicon.]]></description>
      <pubDate>Tue, 13 Dec 2022 10:04:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2062753</guid>
    </item>
    <item>
      <title>Invisible ship tracks show large cloud sensitivity to aerosol</title>
      <link>https://trid.trb.org/View/2038828</link>
      <description><![CDATA[Cloud reflectivity is sensitive to atmospheric aerosol concentrations because aerosols provide the condensation nuclei on which water condenses1. Increased aerosol concentrations due to human activity affect droplet number concentration, liquid water and cloud fraction2, but these changes are subject to large uncertainties3. Ship tracks, long lines of polluted clouds that are visible in satellite images, are one of the main tools for quantifying aerosol–cloud interactions4. However, only a small fraction of the clouds polluted by shipping show ship tracks5,6. Here the authors show that even when no ship tracks are visible in satellite images, aerosol emissions change cloud properties substantially. The authors develop a new method to quantify the effect of shipping on all clouds, showing a cloud droplet number increase and a more positive liquid water response when there are no visible tracks. The authors directly detect shipping-induced cloud property changes in the trade cumulus regions of the Atlantic, which are known to display almost no visible tracks. The authors' results indicate that previous studies of ship tracks were suffering from selection biases by focusing only on visible tracks from satellite imagery. The strong liquid water path response the authors find translates to a larger aerosol cooling effect on the climate, potentially masking a higher climate sensitivity than observed temperature trends would otherwise suggest.]]></description>
      <pubDate>Wed, 16 Nov 2022 11:36:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2038828</guid>
    </item>
    <item>
      <title>Global food-miles account for nearly 20% of total food-systems emissions</title>
      <link>https://trid.trb.org/View/1991171</link>
      <description><![CDATA[The purpose of this study was to estimate global carbon dioxide emissions related to food transport expressed in food-miles, as a percentage of total food-systems emissions. This was calculated using a multi-region input-output model that incorporated physical transportation distance, mass, modes and emissions coefficients into global supply chains. The results showed that food-miles emissions is responsible for 19% of total food-system emissions, which consists of food-miles, food production, and land-use change emissions. In particular, vegetables and fruits comprise 36% of global food-miles emissions, with transport emissions almost double production-related emissions for these items. To reduce the environmental impact of food transport, the authors recommend that animal product consumption be decreased and plant-based food consumption be increased, along with increased local food production.]]></description>
      <pubDate>Tue, 01 Nov 2022 09:21:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1991171</guid>
    </item>
    <item>
      <title>Forecasting the evolution of fast-changing transportation networks using machine learning</title>
      <link>https://trid.trb.org/View/2006294</link>
      <description><![CDATA[Transportation networks play a critical role in human mobility and the exchange of goods, but they are also the primary vehicles for the worldwide spread of infections, and account for a significant fraction of CO₂ emissions. The authors investigate the edge removal dynamics of two mature but fast-changing transportation networks: the Brazilian domestic bus transportation network and the U.S. domestic air transportation network. The authors use machine learning approaches to predict edge removal on a monthly time scale and find that models trained on data for a given month predict edge removals for the same month with high accuracy. For the air transportation network, the authors also find that models trained for a given month are still accurate for other months even in the presence of external shocks. The authors take advantage of this approach to forecast the impact of a hypothetical dramatic reduction in the scale of the U.S. air transportation network as a result of policies to reduce CO₂ emissions. The authors' forecasting approach could be helpful in building scenarios for planning future infrastructure.]]></description>
      <pubDate>Tue, 11 Oct 2022 16:58:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2006294</guid>
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