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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Seabird responses to altered marine vessel activity during the COVID-19 anthropause: insights from citizen science</title>
      <link>https://trid.trb.org/View/2661976</link>
      <description><![CDATA[Human activities exert multifaceted pressures on marine ecosystems, yet the degree to which seabirds respond to changes in vessel activity remains poorly quantified. The COVID-19 “anthropause” provided an unprecedented natural experiment to assess these responses across spatial and temporal scales. We combined Automatic Identification System data describing marine vessel activity with citizen-science observations from eBird to examine how 35 coastal seabird species in British Columbia, Canada, responded to pandemic-related changes in vessel traffic. Vessel activity was modelled using Generalized Additive Models to identify sites where small (personal), mid-sized (fishing and tug), and large (cargo, tanker and passenger) vessel traffic increased or decreased relative to pre-pandemic years (2018–2019). Seabird abundance was then modelled using negative binomial Generalized Additive Mixed Models to test for species-level changes between pre- and post-COVID periods, using the same sites identified in the previous step as showing significant increases or decreases in vessel activity. This paired-model approach (one for increase sites and one for decrease sites) allowed us to isolate seabird responses to directional changes in vessel activity, increasing confidence that observed patterns reflected vessel effects rather than unrelated environmental variation. Results revealed strong spatial heterogeneity in vessel reductions, with large vessels showing widespread declines, while small and mid-sized vessels displayed mixed patterns. Seabird responses were closely linked to these patterns: abundance generally increased where small-vessel traffic declined and decreased where it increased, indicating a negative association with small boats, while the opposite was true for mid-sized vessels, with abundance tending to rise where traffic increased and fall where it declined, reflecting a positive association. Large vessels produced few, mostly positive, significant associations, suggesting weaker or more distant effects. Collectively, these results demonstrate that vessel type, predictability, and proximity influence seabird responses to maritime disturbance. Our findings underscore the importance of considering vessel class and species-specific behaviour in marine spatial planning and mitigation of human impacts on seabird communities.]]></description>
      <pubDate>Wed, 22 Apr 2026 14:04:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661976</guid>
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    <item>
      <title>Risk-Based Rapid Visual Screening of Bridges</title>
      <link>https://trid.trb.org/View/2263873</link>
      <description><![CDATA[Seismic resiliency of new bridges has improved over the years due to improved seismic codes and design practices. However, the vulnerability of seismically deficient bridges, coupled with aging and deterioration, poses a significant threat to life safety and integrity of lifeline systems. It is economically not feasible to retrofit the entire seismically deficient bridges. Therefore, there is need for a comprehensive plan to identify critical bridges and prioritize their retrofit and upgrade requirements. A risk-based seismic evaluation technique is proposed in this paper to develop a ranking scheme for bridges. The complex interaction between seismic hazard, bridge vulnerability and consequence of failure is handled in a hierarchical manner. Some of the input risk parameters, expressed as qualitative and quantitative quantifiers, are transformed into commensurable values. A fuzzy-logic based modelling technique is used to aggregate through the hierarchy and obtain final risk index for prioritization. The efficacy of the proposed method is illustrated with bridges in the British Columbia, Canada.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:39:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2263873</guid>
    </item>
    <item>
      <title>Optimizing evacuation routes for human mobility during wildfires: A case study of the 2023 McDougall Creek Wildfire</title>
      <link>https://trid.trb.org/View/2656107</link>
      <description><![CDATA[Wildfires present significant challenges to evacuation planning due to their dynamic nature, rapid spread, and the limitations of static routing methods, which often lead to congestion and increased safety risks. This study addresses these issues by analyzing the 2023 McDougall Creek Wildfire (Kelowna, BC) using GPS data, identifying congestion bottlenecks, and developing a dynamic Dijkstra-A* algorithm with a multi-criteria cost function that integrates distance, congestion, and fire risk. Validated through SUMO simulations across two scenario groups with fire origins in the northwest and southwest, we tested four evacuation strategies: simultaneous departure, temporally staggered departure, region-based evacuation with uniform response times, and region-based evacuation with realistic response time variability. Our results demonstrate that region-based, staggered evacuations with realistic response times effectively reduce peak congestion and improve safety compared to simultaneous approaches. This research highlights the potential of GPS-informed behavior and hazard-aware routing to improve adaptive evacuation strategies for climate-driven wildfire events.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656107</guid>
    </item>
    <item>
      <title>Evaluating regional variation in neighbourhood socioeconomic inequalities in motor vehicle injury collisions</title>
      <link>https://trid.trb.org/View/2640743</link>
      <description><![CDATA[Disadvantaged communities have higher rates of traffic injury. The goal was to measure regional variation in the association between small-area socioeconomic deprivation and motor vehicle crashes resulting in injury within British Columbia, Canada. The authors analyzed road traffic injury crashes from universal auto insurance claims (2019–2023) across 16 urban regions of British Columbia, aggregated by census dissemination areas (DAs). The authors measured socioeconomic deprivation using the Vancouver Area Neighborhood Deprivation Index, a normalized score combining seven health-related sociodemographic variables applied to the 2021 Canadian census. The authors examined associations between deprivation and three injury crash types (motor vehicle, bicycle-motor vehicle, and pedestrian-motor vehicle) using Bayesian spatial regression models. Increased socioeconomic deprivation was consistently associated with higher injury crash incidence across all crash types, with regional variation in the strength of the relationship. Among the most populous regions, a one standard deviation increase in deprivation was associated with injury crash increases from 17% (95% Credible Interval [CI]: 12%–21%) in Vancouver-Fraser Valley to 51% (95% CI: 36%–68%) in the Okanagan region. Similar patterns were observed for cyclist injuries, from 10 % (95 % CI: 4%–16%) in Vancouver-Fraser Valley to 64 % (95 % CI: 38%–93%) in Okanagan, and for pedestrian injuries, from 17 % (95 % CI: 11%–24%) in Vancouver-Fraser Valley to 77% (95% CI: 48%–110%) in Okanagan. Smaller regions had wide credible intervals and more uncertainty in associations. To address spatial inequalities, prioritization of the placement of road safety interventions should incorporate equity considerations, as well as area-level interventions that address fundamental risk factors of traffic volume and speed.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640743</guid>
    </item>
    <item>
      <title>A joint model of residential mobility and location choice decisions</title>
      <link>https://trid.trb.org/View/2596563</link>
      <description><![CDATA[Residential relocation is a process involving the interdependent decisions of mobility (i.e. the decision to move) and location choice. Location choice is a discrete choice decision, whereas mobility has a continuous time dimension. Since households relocate along their life course for different reasons such as the birth of a child, these reasons could influence the duration of stay as competing events. This paper develops a joint model for mobility and location choice decisions. Data come from a retrospective survey conducted in the Central Okanagan of British Columbia, Canada. A reason-based competing hazard model is developed for the mobility component of the joint model. For the location choice component, a latent segmentation-based logit model is developed to capture unobserved heterogeneity. The influence of location utility on mobility is addressed through log sum parameters. The model confirms the effects of life-cycle events, parcel, socio-demographic, land use, transportation, neighbourhood and accessibility characteristics. The findings reveal that households relocating for the occurrence of life-cycle events are active in the housing market following the birth of a child, marriage and vehicle purchase. Relocation to live in a desirable neighbourhood is found to delay the move for the reason of living closer to activity points. Results also indicate that duration is likely to be shorter with the increase in expected utility from the following location. Regarding the location choice, preferences for dwellings closer to schools are evident. In the case of heterogeneity, it is observed that urban dwellers are more likely to prefer locations closer to the workplace with the addition of jobs in the household. In contrast, suburban dwellers are more likely to prefer residing far from the workplace. The developed joint model has been included in an integrated urban model, currently under development at the University of British Columbia.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596563</guid>
    </item>
    <item>
      <title>Wildfire Evacuation Choice-Making among Underserved Groups in Alberta and British Columbia</title>
      <link>https://trid.trb.org/View/2628306</link>
      <description><![CDATA[Wildfires continue to threaten multiple regions in Canada, and evacuations are often the primary means of ensuring life safety. Understanding how people make decisions before and during wildfire evacuations is thus important in informing preparedness and planning. This research collected survey data between May and July 2023, from residents living in high to moderate fire-risk areas of Alberta and British Columbia (N?=?2,868) to understand their intended evacuation behavior and choice-making during a future wildfire event. Our analysis focuses on underserved groups (people with disabilities, older adults, lower-income households, visible minorities, and carless residents), often neglected in evacuation planning processes. We contribute to the literature by uniquely focusing on decision-making within distinct underserved groups, rather than simply using these identities as variables within a broader model. Estimated logit models offer insight into factors affecting evacuation departure timing, destination and route choices, mode choices, and preferred shelter types. Results suggested that factors such as perceived risk, previous evacuation experiences, and intersecting vulnerabilities have a significant influence on group choices. For example, whereas risk perception significantly influenced evacuation timing among people with disabilities, sociodemographic characteristics were significant in determining shelter choices among older adults. These findings have important implications for enhancing equitable wildfire evacuations, pointing to the need for tailored strategies that consider the needs, barriers, and decision-making patterns of underserved groups. We provide several policy recommendations for local agencies, including ensuring multimodal evacuation plans with transit and shared mobility considerations and providing targeted support for those with intersecting vulnerabilities.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:07:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628306</guid>
    </item>
    <item>
      <title>Multijurisdictional Status Review of Low Carbon Fuel Standards, 2010–2020 Q2 California, Oregon, and British Columbia</title>
      <link>https://trid.trb.org/View/2627331</link>
      <description><![CDATA[A fuel carbon intensity (CI) standard, such as California’s Low Carbon Fuel Standard (LCFS), Oregon’s Clean Fuels Program (CFP) or British Columbia’s Renewable & Low Carbon Fuel Requirements (RLCFR, also known as an LCFS), aims to reduce transportation sector greenhouse gas (GHG) emissions by incentivizing innovation, technological development, and deployment of low-emission alternative fuels and vehicles. These programs set a performance standard that considers the full lifecycle impacts of fuel production and use while treating all transportation fuels similarly, allowing consumers and markets to determine the path to compliance. This LCFS status report is the latest in a series of updates based on program metrics. It focuses on the programs in the Pacific Coast Collaborative (PCC) jurisdictions of California, Oregon, and BC, and covers 2010 through 2020 Q2 (the most recent data available at the time of writing). This report is structured as follows: Section 1 introduces and provides an overview of the policies in the three jurisdictions, their reported CI trends, and program credit/deficit balance. Section 2 describes the sources of alternative fuel energy use and crediting in each jurisdiction over time under the policies. Section 3 outlines trends in the reported CIs of alternative fuels over time. Section 4 describes the state of markets for program credits in each jurisdiction. Section 5 discusses trends in primary program credit generators – the major transportation fuels, i.e., ethanol, biomass-based diesel, natural gas, and electricity, emphasizing the role of feedstocks, as well as sources of credit generation beyond fuel use. Section 6 explores potential interactions among LCFS jurisdictions and relationships between LCFS credit markets and fuel markets. Section 7 offers concluding remarks and highlights potential avenues for future research.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627331</guid>
    </item>
    <item>
      <title>Decision support framework for screening deficient bridges based on statistically-identified key indicators and Monte Carlo simulation</title>
      <link>https://trid.trb.org/View/2617343</link>
      <description><![CDATA[The urgent need to prioritize deficient bridges in hazard-prone regions highlights the importance of strategic decision-making under financial constraints. This paper develops a comprehensive screening framework that integrates economic and social impacts alongside structural condition indices. First, key performance indicators (KPIs) are statistically identified using the Taguchi design of experiments, providing a data-driven foundation for the model. Next, the Fuzzy Analytic Hierarchy Process (AHP) assigns dynamic weights to the KPIs, emphasizing critical factors such as structural condition and bridge importance. Subsequently, a Bridge Screening Index (BSI) is introduced and applied to a case study of 15 bridges in British Columbia, Canada. Finally, a Python-based algorithm was implemented to conduct Monte Carlo simulations, evaluating the model's sensitivity to variations in input parameters. By building on the simulation outcomes, a refined BSI formulation is suggested. This simplified approach is practical for data-limited scenarios, offering optimal results with a 95 % confidence level.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617343</guid>
    </item>
    <item>
      <title>Modeling bicyclists' destination location choice: Spatial-temporal constraint for choice set generation</title>
      <link>https://trid.trb.org/View/2594403</link>
      <description><![CDATA[With growing environmental concerns and increased awareness about physical fitness, many people are shifting their travel mode to bike for various activities. Hence, understanding bike users' destination choice behavior is crucial for developing policies and making infrastructure investment decisions. This study develops a mixed logit model with panel effect to analyze destination location choice for bike user’ home-based tours. The study adopts time-space prism concept to generate activity-specific choice sets, accommodating spatial, temporal, and physical strength constraints. The model is estimated using travel survey data from the Okanagan region of British Columbia. It has been tested for different sizes of choice sets, and goodness-of-fit measures have been compared to identify the model that best fits the data. The results suggest that tour-related attributes, individual characteristics, work profiles, built environment characteristics, and transportation infrastructure-related variables significantly influence destination choices. For example, bike users prefer destinations closer to home. However, variation exists based on tour types. For simple tours with one destination, bike user may travel longer distances, whereas for complex tours with multiple destinations, they may prefer shorter distances for each leg of the tour. Telecommuters may travel longer distances, while commuters show significant variability in their destination choices – some prefer traveling shorter distances while others travel farther. Important policy variables include a higher density of activity destinations, bike lane to road length ratio, and bike index, which may attract more biker users. The findings of this study will facilitate in developing travel demand models sensitive to bike users' travel behavior.]]></description>
      <pubDate>Fri, 14 Nov 2025 08:46:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594403</guid>
    </item>
    <item>
      <title>Bayesian hierarchical non-stationary hybrid modeling for threshold estimation in peak over threshold approach</title>
      <link>https://trid.trb.org/View/2604825</link>
      <description><![CDATA[The peak over threshold (POT) approach in extreme value theory is widely used for crash risk estimation, but the reliability is often undermined by the subjective and arbitrary selection of the conflict threshold, which can lead to biased outcomes. This study advances the hybrid modeling method for objective threshold determination by developing a non-stationary framework and comprehensively comparing five distinct model structures. The framework allows the threshold to vary with real-time traffic covariates, while the comparison identifies the optimal distribution for general conflicts. The Bayesian hierarchical structure is used to combine traffic conflicts from different sites, incorporating covariates and site-specific unobserved heterogeneity. Five non-stationary BHHM models, including Normal-GPD, Cauchy-GPD, Logistic-GPD, Gamma-GPD, and Lognormal-GPD models, were developed and compared. Traditional graphical diagnostic and quantile regression approaches were also used for comparison. Traffic conflicts collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The Bayesian approach is employed to estimate the threshold and other parameters in the non-stationary BHHM models. The results show that the proposed BHHM approach could estimate the threshold parameter objectively. The non-stationary BHHM models capture how the threshold varies dynamically across signal cycles in response to changing traffic status. The Lognormal-GPD model is superior to the other four BHHM models in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the BHHM outperform those estimated based on the graphical diagnostic and quantile regression approaches, indicating the superiority of the proposed threshold determination approach. The findings of this study contribute to enhancing the existing EVT methods for providing a threshold determination approach as well as producing reliable crash estimations.]]></description>
      <pubDate>Mon, 27 Oct 2025 09:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604825</guid>
    </item>
    <item>
      <title>A comparison of the prevalence of cannabis and alcohol use among drivers and passengers in British Columbia and Ontario, Canada</title>
      <link>https://trid.trb.org/View/2598944</link>
      <description><![CDATA[Similar to drink driving, the prevalence of driving under the influence of cannabis (DUIC) is expected to depend on the availability and cost of cannabis which would impact cannabis use in both drivers and passengers, and factors that specifically target cannabis use in drivers such as the deterrent effect of traffic laws and driver’s opinion about the risks and acceptability of DUIC. To disentangle these effects, the authors aimed to compare the prevalence of alcohol and tetrahydrocannabinol (THC) detection 1) in drivers vs. passengers involved in motor vehicle accidents and 2) in drivers and passengers from BC vs. Ontario. Chart review and toxicology data from an ongoing prospective study of moderately injured motor vehicle occupants were analyzed. Log-binomial regression models were used to obtain prevalence ratios (PRs). This manuscript reports on data from 3004 drivers and 941 passengers. Approximately half (55.1%) were male, and the mean (SD) age was 43.8 (19.1) years. Alcohol and THC detection prevalence was 14.2% and 12.4%, respectively. Passengers had higher prevalence of alcohol than drivers (aPR [95% CI]: 1.22 [1.06, 1.40]). No difference in THC prevalence was observed between drivers and passengers. Ontario drivers had higher prevalence of alcohol detection than BC drivers (aPR [95% CI]: 1.33 [1.13, 1.58]) but lower prevalence of THC detection (aPR [95% CI]: 0.80 [0.64, 0.99]). Among passengers, no significant interprovincial differences were observed for alcohol or THC detection. These findings may be partially explained by differences in provincial traffic laws, public opinion, and overall consumption rates.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598944</guid>
    </item>
    <item>
      <title>Addressing the electric vehicle adoption gap for small fleets: A case study of local energy transitions in British Columbia</title>
      <link>https://trid.trb.org/View/2599147</link>
      <description><![CDATA[In the transition to replacing internal combustion engine vehicles with electric vehicles (EV), there remains a gap in adoption by small fleets. Researchers and practitioners have posited that this gap may exist for a range of reasons, including: that the fleet electrification is not economically rational, that the needs of fleet operators are too diverse for current market offerings, or that targeted government interventions for this segment are lacking. The authors conducted a survey (n = 68) of small fleet operators in British Columbia, Canada and categorized the responses into barriers related to cost, incompatibility (real or perceived) and availability. Current EVs are incompatible with the operational needs of some respondents but the results show that, in many cases, the incompatibility is perceived and EVs could meet the stated requirements of such small fleets. The authors also observed that common customizations to (or “upfitting” of) fleet vehicles can be readily applied to EVs, but specialized use cases must be produced by the manufacturer—which may be a supply-related barrier. The authors also used a total cost of ownership (TCO) to demonstrate that while economic rationality is generally stronger for lighter duty class vehicles, small fleets that drive longer distances have a greater advantage in electrification. The findings suggest that government intervention targeted at small fleets, such as bulk purchasing programs, could increase the adoption of EVs in this segment when coupled with purchase incentives. This gap could potentially be filled by local agencies, which can play a critical role in brokering trust between parties involved by being the middle actor at the boundary of government, suppliers, and customers. Lastly, the authors observe that small fleet operators display some understanding of the TCO of EVs. Incorporating an educational component into a bulk purchase program, as observed in other successful procurement arrangements that the authors review, could enhance the confidence of fleet operators and ultimately, lead to further adoption.]]></description>
      <pubDate>Thu, 09 Oct 2025 16:39:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2599147</guid>
    </item>
    <item>
      <title>Developing and microsimulating demographic dynamics for an integrated urban model: a comparison between logistic regression and machine learning techniques</title>
      <link>https://trid.trb.org/View/2561625</link>
      <description><![CDATA[Studies have shown that sociodemographic attributes significantly influence individuals' transportation choices. However, not all travel demand models do not account for this effect when predicting future travel scenarios. On the other hand, current integrated urban models (IUMs) that incorporate demographic dynamics mostly rely on conventional logit models and rule-based models. These models may not be optimal for complex modeling since they do not fully capture the non-linear relationship between inputs and output. In this research, we explore the feasibility of utilizing machine learning (ML) models to enhance the prediction of demographic dynamics within our proposed IUM—known as ‘STELARS’, in conjunction with conventional logit models. To address the challenge of the black-box nature of ML, we employ an explainable AI technique (xAI) to gain insights into the influence of the factors and compare them with the interpretation revealed by the logit models. Three demographic components are considered: marriage/common-law formation, separation and divorce, and childbirth events, while other components were developed using rate-based models. The results (on the testing dataset) indicate that ML models outperform conventional logit models in terms of overall accuracy by a margin of up-to 3%. However, when considering the true positive accuracy (correctly predicting the event of interest), a significant improvement of 30–48% is observed. Additionally, the xAI analysis reveals consistent interpretation with the logit model. Subsequently, we implemented our demographic dynamics module within our integrated urban modeling system to predict population changes in the Okanagan region of Canada. The multi-year validation of the simulation results against Census data suggests a reasonably close prediction of the observed population. We also optimize the runtime of the demographic dynamics module using vectorization, reducing the simulation time for the demographic changes in our study area (comprising approximately 200,000 individuals living in 85,000 households) to just about 100 s for the total 10 years of simulation. The development and implementation of this advanced demographic dynamics module to accurately predict the life events of individuals adds a fundamental capacity to the STELARS to be built as an event-based microsimulation model.]]></description>
      <pubDate>Wed, 17 Sep 2025 09:01:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561625</guid>
    </item>
    <item>
      <title>Where to plug in? Assessing the users’ preferences for EV charging location</title>
      <link>https://trid.trb.org/View/2570872</link>
      <description><![CDATA[This study investigates individuals’ electric vehicle (EV) charging location preferences in the Okanagan region of British Columbia. Data comes from the British Columbia Activity Time Use Survey conducted in 2023, which collected individuals’ ranked preferences for charging their EVs in the following location alternatives: home, work, grocery stores, shopping malls, en-route, gas stations, and other locations. A random parameter rank-ordered logit model is employed to capture the relative preferences for different charging locations. The results reveal that individuals’ socio-demographics, travel attributes, built-environment characteristics, and accessibility measures significantly influence EV charging location preferences. For example, higher-income individuals show a higher preference for charging at home and workplace. Residents of detached houses prefer home and workplace charging over grocery stores and shopping malls. Apartment dwellers show a higher preferences for charging their vehicles in grocery stores, shopping malls, gas stations, and other locations. Additionally, individuals traveling longer distances daily are likely to have higher preferences for charging their EVs in shopping malls, en-route, and gas stations. The proximity of charging stations and land use mix, also play a critical role in influencing charging location preferences. A higher number of charging stations near home is found to reduce the preference for home charging. On the other hand, a higher land use mix around workplaces, indicating the availability of diverse amenities, reduces the preference for workplace charging. Providing community-based charging facilities might be able to accommodate the EV charging needs of these individuals. Nevertheless, the findings of this study provide valuable insights for policymakers and planners regarding user preferences for charging which will help in strategic investments, planning, and EV charging infrastructure development.]]></description>
      <pubDate>Fri, 29 Aug 2025 10:03:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570872</guid>
    </item>
    <item>
      <title>Safety Effectiveness of High Friction Surface Treatment at Signalized Intersections in British Columbia</title>
      <link>https://trid.trb.org/View/2587063</link>
      <description><![CDATA[High friction surface treatment (HFST) is a pavement and safety treatment that dramatically and immediately increases pavement friction to reduce crashes, injuries, and fatalities associated with friction demand issues. Understanding the effectiveness of HFST as a safety measure is crucial for estimating the expected crash reduction and evaluating the cost-effectiveness of future HFST implementations. Existing research on HFST safety effectiveness evaluation is limited to horizontal curves and ramps, despite the promising safety benefits of installing HFST at other locations, such as signalized intersections. To help fill this research gap, this paper presents a rigorous before-and-after safety effectiveness evaluation of HFST installation at signalized intersections using traffic and crash data obtained from 15 treatment sites and 90 control sites in British Columbia, Canada. To enhance the validity of the safety assessment, two before-and-after evaluation methods were applied: empirical Bayes and full Bayes. The results indicated statistically significant safety benefits of HFST at the treated sites. Specifically, the estimated reductions in serious (fatal and injury) crashes, serious rear-end crashes, and serious wet-pavement crashes, are about 51%, 57%, and 64%, respectively. It is worth noting that an unexpected decline in crashes was observed at the control sites, which introduces some uncertainty in interpreting the results and warrants careful consideration.]]></description>
      <pubDate>Sat, 09 Aug 2025 13:02:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587063</guid>
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