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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>Electric Vehicle Adoption and Use in Rural and Urban Utah</title>
      <link>https://trid.trb.org/View/2714188</link>
      <description><![CDATA[This research project sought to better understand factors affecting electric vehicle (EV) adoption, use, and charging behaviors in Utah, and any differences between urban and rural areas. To accomplish this goal, three key datasets were used: Utah’s 2023 household travel survey, 2015–2024 Utah vehicle registrations by county, and a 2021–2023 survey of “gig” drivers (who work for ridehailing and delivery companies) about EVs. Several key outcomes were studied: EV registrations, household vehicle ownership and EV adoption, daily travel behaviors, charging behaviors, and gig driver EV perceptions and preferences. Following literature reviews and statistical analyses, the research team identified explanatory factors and differences in EV adoption, use, and charging behavior between urban and rural parts of Utah. EV adoption in Utah is increasing among registered vehicles, especially in rural areas and for light trucks. Utah travel survey data analysis indicated relatively few  differences in the factors influencing the travel behaviors of EV-owning and non-EV households. Instead, EV- specific travel and charging behavior results suggest that rural EV users (residents and visitors) are more strategic  about their use and charging of EVs, whereas urban EV users can be more opportunistic. These findings suggest that public EV charging is important, especially in rural areas. Analysis of gig driver surveys found Salt Lake City to have lower EV adoption than other cities in the western US, suggesting needs around EV leasing options, targeted marketing, and public EV charging in urban areas, to help facilitate electrification of the gig driving sector.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:11:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714188</guid>
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    <item>
      <title>Modeling the market penetration of automated vehicles with an integrated choice-based diffusion approach</title>
      <link>https://trid.trb.org/View/2704530</link>
      <description><![CDATA[In the past decade, transportation researchers have predicted Automated Vehicle (AV) preferences using discrete choice models, including logit and probit models. These studies rely on stated preferences on AV adoption which only provides an adoption scenario at a single point in time. This limitation can be overcome by following a diffusion-based approach known as the Bass Model using sales data on the product. In this study, the authors use existing sales data on Level 2 AVs also known as advanced driver assistance systems (ADAS) for prediction of mass adoption of Level 3 AVs. This study is the first to model AV mode choice combining discrete choice theory with a diffusion model. Stated and revealed preference data from Puget Sound Travel Survey, and a Nested Logit Model are used to obtain the market penetration of AV travel modes. The market shares are then fed into the diffusion model for the prediction of the number of adopters for automated transit, automated taxi and privately owned autonomous cars. The findings reveal that it will take 20 years to reach full market potential for Level 3 automated cars and buses, when there is a 0.53 percent annual decrease in the price of the ADAS technology. By quantifying the timing and mode-specific dynamics of AV adoption under different cost scenarios, this study may help shape policies in the future related to the penetration of AVs, specifically pricing policies, and regulatory strategies for varying AV modes.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704530</guid>
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    <item>
      <title>Dynamic Modeling and Optimal Planning for Integrating Electric Vehicles and Renewable Energy Into Coupled Traffic-Power Systems</title>
      <link>https://trid.trb.org/View/2658800</link>
      <description><![CDATA[The large-scale adoption of electric vehicles (EVs) is regarded as an effective strategy for reducing greenhouse gas emissions from the transportation sector. Although EVs do not produce tailpipe emissions, their widespread adoption may impose significant pressure on the decarbonization of the power system—a critical aspect often overlooked in planning and policy-making. Moreover, the actual environmental benefits of EVs depend on the source of electricity used for charging. Considering the power demand induced by EV charging, we develop a continuous-time dynamic model for the optimal planning of simultaneous EV adoption and the integration of renewable energy sources into the power system. The interactions between competing entities, such as EVs versus legacy vehicles, and renewable versus conventional energy sources, are incorporated into our mathematical model based on the Lotka-Volterra equations. We formulate an optimal control problem to determine planning policies for EV subsidies, infrastructure investment, and the rates of renewable integration and fossil fuel retirement. The goal is to achieve a desired EV market penetration rate (MPR) while enabling the smooth integration of renewables. The nonlinear optimal control problem is solved using the Pontryagin minimum principle. Comprehensive numerical results are presented to demonstrate the effectiveness of the proposed approach, including sensitivity analyses on MPR targets, planning horizons, and cost-benefit considerations. The results offer useful insights for policymakers in designing long-term strategies for EV adoption within a coupled traffic-power system.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658800</guid>
    </item>
    <item>
      <title>Spatial dynamics of crash hotspots under autonomous vehicle adoption scenarios</title>
      <link>https://trid.trb.org/View/2698625</link>
      <description><![CDATA[In road environments with large Autonomous Vehicle (AV) fleets and higher SAE automation levels, reliable crash data are often unavailable, making direct safety assessment infeasible. In such cases, traffic simulation offers a valuable alternative for evaluating safety. This study conducts a spatial modelling analysis to predict crash hotspot occurrences under different AV deployment scenarios. The study combines microsimulation-derived conflict data, a quantitative crash-risk formulation, validated using field crash data, based on Time-To-Collision (TTC) thresholds, and spatial statistical analysis using the Getis-Ord Gi* statistic to detect statistically significant hotspots of elevated crash risk. The resulting hotspots were further analyzed using a binomial Generalized Additive Model (GAM) to quantify the impact of automation, roadway and spatial factors on the probability that a conflict event occurs within a hotspot area. Results show that automation significantly alters the spatial distribution of crash risk, leading to a gradual reduction and spatial diffusion of hotspots as AV penetration increases. However, a temporary rise in the probability that conflict events occur within hotspot areas occurs under moderate automation shares, highlighting the transitional instability of mixed-traffic conditions. Intersections and other high-interaction areas remained the most critical locations, while congested segments were associated with a higher probability that conflict events occur within hotspot areas. The proposed framework supports data-informed planning and policy decisions during the transition toward automated urban mobility.]]></description>
      <pubDate>Wed, 27 May 2026 13:05:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698625</guid>
    </item>
    <item>
      <title>Performance and Safety Assessment of Dedicated Lane for Connected Automated Vehicles</title>
      <link>https://trid.trb.org/View/2662675</link>
      <description><![CDATA[This study aims to evaluate the effects of different dedicated lane (DL) strategies on the performance and safety of highways in mixed traffic conditions where manually driven vehicles (MDVs) coexist with connected autonomous vehicles (CAVs). Using simulations, various DL strategies are examined in conjunction with market penetration rates (MPRs) and flow/capacity (v/c) ratios. Our findings indicate that an increase in the number of DLs reduces conflicts between CAV-MDV and CAV-CAV but increases conflicts among MDVs. Furthermore, it is noted that high v/c ratios worsen conflicts and diminish the positive effects of DLs. The effectiveness of the DL strategy becomes more prominent, particularly at high MPRs in highway segments outside of entrance and exit regions, while its impact is less in those areas. This study demonstrates that the DL strategy enhances safety by reducing collision risks between CAVs and MDVs, but its effectiveness is linked to MPR. Given its regional performance and safety analysis compared to existing studies on DL strategies, this study is anticipated to provide a significant contribution academically and practically.]]></description>
      <pubDate>Fri, 01 May 2026 14:33:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662675</guid>
    </item>
    <item>
      <title>Quantifying emission reductions from new energy vehicle adoption via integrated macro-micro data analysis</title>
      <link>https://trid.trb.org/View/2665218</link>
      <description><![CDATA[The transportation sector is a significant source of global carbon emissions, and widespread adoption of New Energy Vehicles (NEVs) is critical for mitigation. Existing studies, however, rely predominantly on macro-level scenario analyses and life cycle assessments (LCA), often lacking integration with real-world vehicle operation data to quantify the actual emission reduction impacts of growing NEV fleets. This study analyzes over one million traffic monitoring records from Chongqing (2019-2024) to characterize spatiotemporal evolution of urban road carbon emissions. We extract micro-scale travel characteristics based on travel distance and develop a K-ME-XGBoost-LSTM hybrid model to illuminate nonlinear relationships between NEV growth and traffic emissions. The results indicate that road network mileage, NEV penetration rate, travel distance, and travel speed are the primary determinants of emission levels. A critical finding is that significant emission reductions materialize only after NEV penetration exceeds a threshold of approximately 10%. Moreover, the mitigating effect on NOₓ emissions becomes progressively stronger than that on CO as the NEV share increases. Furthermore, within urban core areas, the adoption of NEVs effectively reduces the demand for long-distance travel using fuel-powered vehicles, thereby driving system-wide declines in emissions. Projections for the next five years (2025-2029) indicate that this positive emission reduction trajectory is likely to continue across multiple districts.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665218</guid>
    </item>
    <item>
      <title>Behavioral adaptation in mixed traffic: The roles of AV penetration rate and human driving style</title>
      <link>https://trid.trb.org/View/2686992</link>
      <description><![CDATA[Automated vehicles (AVs) are designed to improve traffic safety, mobility, and driver comfort; however, their benefits depend on the widespread adoption of AVs. As full market penetration remains unlikely in the near term, AVs will continue to operate alongside human-driven vehicles (HDVs), making it essential to understand their interactions and the implications for traffic safety. This study investigated how different AV penetration rates (0%, 25%, 50%, 75%) influence HDV drivers’ behavioral adaptation at the tactical level and subjective evaluations, while considering drivers’ individual driving styles (aggressive, moderate, and defensive). Thirty-six drivers participated in a driving simulator experiment involving two driving scenarios (left-turn and lane-change scenarios) under varying AV penetration rates. Drivers’ adaptive decision-making in response to AVs’ defensive driving, as the AV penetration rate changed, was measured by the frequency of left turns executed without yielding and the distance maintained from surrounding vehicles during lane changes. Subjective evaluations were assessed through perceived safety and anxiety ratings collected after each trial. Results indicated that the influence of AV penetration rate was moderated by driving style. In the lane-change scenario, increased AV penetration rate resulted in more adaptive decision-making among aggressive and moderate drivers, whereas in the left-turn scenario, this effect emerged only for aggressive drivers. In contrast, AV penetration had no significant effect on defensive drivers’ behavior in either scenario. These findings suggest that higher AV penetration may compromise safety in mixed traffic by provoking more aggressive decision-making and aggressive behavior among certain driver types, highlighting the need to account for driver adaptation patterns in AV deployment strategies.]]></description>
      <pubDate>Mon, 27 Apr 2026 17:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686992</guid>
    </item>
    <item>
      <title>Charging while driving lanes: A boon to electric vehicle owners or a disruption to traffic flow</title>
      <link>https://trid.trb.org/View/2645481</link>
      <description><![CDATA[Large-scale adoption of commercial and personal Electric Vehicles (EVs) is expected to significantly affect traffic flow dynamics, emissions, and energy consumption in the transportation sector. Range anxiety and challenges associated with charging EVs are among the key issues that reduce the adoption rate of EVs and, in turn, limit their system-level impacts. A promising solution to address these challenges is the introduction of charging while driving (CWD) lanes, either by appropriating an existing lane or augmenting an EV reserved lane to the highway segment. Although technological advancements have made it possible to charge vehicles wirelessly while driving, introducing such lanes to the traffic stream can potentially disturb traffic flow and result in new congestion patterns. This study puts forward a microscopic simulation framework to investigate the effects of CWD lanes on traffic flow dynamics at the segment level. It takes into account different market penetration rates (MPRs) of both personal and commercial EVs in the forms of Automated Vehicles (AVs) and Electric drayage Trucks (ETs), respectively. Different policies have been investigated to suggest the best design for CWD lanes. Results indicate that introducing CWD lanes can decrease overall traffic throughput and increase congestion due to additional lane-changing maneuvers by electric vehicles aiming to utilize the CWD lane. Although higher MPRs of EVs help stabilize traffic flow and reduce the number of shockwaves, speed disruption tends to increase in the CWD lane and propagate to adjacent lanes. Emission analyses show significant reductions (up to 63 %) in pollution levels with increasing MPRs of personal and commercial EVs. Our analysis shows that while CWD lanes can facilitate the adoption of EVs, they can deteriorate traffic efficiency, emphasizing the importance of careful design and policy considerations.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645481</guid>
    </item>
    <item>
      <title>A dynamic analysis of autonomous vehicle diffusion and the impact of pricing strategies</title>
      <link>https://trid.trb.org/View/2643247</link>
      <description><![CDATA[Safety regulation and short-sighted consumer behaviour during the initial phases of autonomous vehicles (AVs) deployment can contribute to the ‘dark age' of AVs, characterised by slow market growth. This paper examines the diffusion of AVs through encapsulating the bottleneck model and Bass diffusion model within a dynamic framework. Based upon that, we derive the market penetration of AVs and their diffusion rate. Furthermore, we propose and compare the short-term and long-term pricing strategies. Numerical results indicate that, in contrast to the short-term pricing strategy, the diffusion of AVs is significantly faster under the long-term pricing strategy. The long-term pricing strategy can mitigate the negative externalities associated with safety settings by lowering the initial price, thereby balancing safety concerns with the diffusion of AVs.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643247</guid>
    </item>
    <item>
      <title>Consumer Behaviour and Infrastructure Challenges in the Adoption of Battery Electric Vehicles in Slovakia</title>
      <link>https://trid.trb.org/View/2666135</link>
      <description><![CDATA[Electromobility plays a crucial role in the EU’s decarbonisation strategy, aiming to reduce greenhouse gas emissions in the transport sector. Despite these objectives, the adoption of battery electric vehicles (BEVs) in Slovakia remains significantly below the EU average, highlighting economic, infrastructural, and behavioural barriers. This study examines the key challenges limiting BEV adoption, including high acquisition costs, insufficient charging infrastructure, and limited consumer awareness, using a mixed-method approach supported by the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM). While recent investments in charging networks and financial incentives signal progress, affordability and accessibility concerns persist among consumers. The findings underscore the need for strategic policy interventions, enhanced financial incentives, and public awareness campaigns to accelerate BEV adoption and align Slovakia with EU sustainability goals while supporting its transition towards a low-emission transport system.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666135</guid>
    </item>
    <item>
      <title>Get the biggest fish to fry: Assessing commercial operators’ barriers towards electric vehicle adoption in urban and rural sub-saharan Africa</title>
      <link>https://trid.trb.org/View/2632871</link>
      <description><![CDATA[Electric vehicles (EVs) have a high potential of accelerating sustainable development in sub-saharan Africa (SSA), a quickly emerging region with little EV penetration so far. However, directing limited policy and investment efforts for maximum effect requires identification and prioritization of barriers to EV adoption. Literature highlights limited transferability of such analyses between geographic contexts due to distinct mobility demand patterns and operational models. While numerous investigations exist for other regions, few studies focus on SSA and none of them considers the wide gap in transport accessibility between urban and rural settings. We fill this gap using a bayesian multi criteria decision making approach to weigh barriers identified from literature through a two-stage online expert study for both settings with stochastic credal analysis for robustness. High purchase cost and multiple infrastructural barriers (mostly related to charging) are identified as the most significant barriers in both contexts, with a focus on the former in urban and the latter in rural settings. Especially the broad set of issues holding back electrification and mobilization uncovered in rural areas requires multifaceted approaches. Besides general EV friendly and mindful policy, legislators should therefore prioritize methods to overcome CAPEX hurdles, and incentivize investment in rural electrical infrastructure to unlock EVs’ full potential.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632871</guid>
    </item>
    <item>
      <title>An activity-based model for district-level modal share analysis with electric vehicles</title>
      <link>https://trid.trb.org/View/2669958</link>
      <description><![CDATA[This study introduces an activity-based modeling approach designed to support decision-makers in understanding the dynamics of private car trips and their potential transition to electric mobility. Despite the growing emphasis on sustainable mobility, there remains a gap in the analyses of electric vehicle (EV) penetration into district-level modal share, especially with respect to urban spatial and infrastructural heterogeneity. By leveraging daily activity-travel patterns data from different urban districts in the city of Budapest, Hungary, this research evaluates a range of scenarios across varying levels of EV penetration. The elaborated approach is used to evaluate modal share change objectives by linking individual trip characteristics, such as distance and CO2 emissions, with district-level attributes, such as availability of charging infrastructure, average node degree, and average shortest path length. The results show that increased EV penetration in the modal share reduces CO2 emissions across all districts, by up to 23% in some cases, while often increasing travel distances, particularly in regions with lower network density and charger availability. This study aims to provide valuable insights by offering a practical framework that integrates optimization and operation research techniques, incorporates empirical data from surveys and various policy documents, as well as embeds perspectives from transportation geography. Furthermore, the research is further strengthened by sensitivity analyses in the attempt to capture social and spatial heterogeneity in urban mobility electrification.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669958</guid>
    </item>
    <item>
      <title>From experimentation to learning and routinization: A long-term trial framework for cargo bike and light electric vehicle integration in Germany</title>
      <link>https://trid.trb.org/View/2669953</link>
      <description><![CDATA[In light of rising sustainability demands in commercial transport, cargo bikes and light electric vehicles (LEV) offer promising alternatives to conventional fleets. However, despite increasing vehicle diversity and supportive policies, adoption remains limited due to organizational inertia, sunk costs, and resistance to change. Short-term pilot projects, while useful for testing feasibility, often fail to capture the evolving and context-specific dynamics of fleet integration. To address this gap, the authors introduce and evaluate a novel long-term trial framework implemented across multiple German regions over a 12-month period. The framework embeds vehicle trials into daily operations and combines tailored onboarding, sustained engagement, and structured reflection phases. This article makes three core contributions. First, it advances both academic and practical understanding by introducing a reliable and transferable framework for long-term fleet transformation that functions as an organizational learning device. Second, it uncovers context-specific and time-sensitive challenges of integrating LEVs and cargo bikes, as shown through five stylized cases across logistics, manufacturing, services, and craft sectors. Third, it conceptualizes three learning-based pathways of organizational commitment to fleet transformation: fleet expansion, vehicle substitution, and purchase prevention, through which long-term trials can support learning-based fleet reconfiguration, including normative learning reflected in the strategic reframing of alternative vehicles. The findings highlight the value of long-term trials in supporting adaptive and real-world transitions that extend beyond the scope of short-term studies. By fostering deeper organizational learning and aligning vehicle solutions with operational realities, the framework establishes critical conditions for sustained adoption and contributes to the broader transition toward more sustainable transport practices.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669953</guid>
    </item>
    <item>
      <title>Access to markets and technology adoption in the agricultural sector: Evidence from Brazil</title>
      <link>https://trid.trb.org/View/2632510</link>
      <description><![CDATA[This paper studies how improved market access through infrastructure influences agricultural modernization. We focus on Brazil’s federal highway expansion from 1950 to 2000 and its impact on the adoption of new agricultural technologies. To address endogeneity, we exploit the construction of Brasília and the plan to connect it with all state capitals as a natural experiment. Using least-cost paths between Brasília and each capital, we build a predicted highway network to instrument for market access. The results show that increased market access led to greater use of modern inputs-such as fertilizers and pesticides-and higher agricultural productivity and wages. However, these gains are uneven: municipalities in the Northeast benefited less, and in some cases, not at all. To explain this heterogeneity, we develop a stylized model in which high input costs in certain regions dampen the incentives to upgrade technology, even with better market access. Historical evidence from Brazil’s import-substitution industrialization period supports this mechanism. These findings suggest that while infrastructure can boost development, its effectiveness depends on complementary regional conditions.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632510</guid>
    </item>
    <item>
      <title>A Comprehensive Review of Traffic State Estimation Methods under Low Penetration Rate of Connected Vehicles</title>
      <link>https://trid.trb.org/View/2612977</link>
      <description><![CDATA[Accurate traffic state estimation (TSE) is a critical step for proactive traffic management and control. With the rapid development of mobile sensing and communication techniques in recent years, TSE, making use of traffic information collected through Connected Vehicles (CVs), has become an important trend. As to the situation that the penetration rate of CVs in real-world settings is still limited, how to develop effective TSE methods that are suitable for such a situation is of great significance. The present work therefore aims to provide a comprehensive review of TSE methods under the low penetration rate (PR) of CVs. Specifically, a comprehensive introduction to the existing TSE methods is first provided. Then, TSE methods that could be applicable for sparse data situations are further analyzed and discussed. The outcomes of the present work could provide important reference for effective TSE and proactive traffic management with sparse data situations.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612977</guid>
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