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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Flight Plan Unclear: McCarran-Ferguson’s Possible Preemption in the Sky</title>
      <link>https://trid.trb.org/View/2775401</link>
      <description><![CDATA[U.S. homeowners have reported increases in insurance premiums, and even cancellation of coverage, based on surveillance images captured by drones.  These drones, deployed by insurance companies, without notice or consent, document alleged property risks or improvements, dramatically altering policyholder outcomes.  While U.S. states have traditionally been constrained from regulating airspace due to federal preemption under the Federal Aviation Act and the Airline Deregulation Act, the McCarran-Ferguson Act (MFA) may provide an overlooked legal opening.  Though federal laws and regulations typically enjoy broad preemptive effects over their state counterparts, the MFA was enacted to ensure that this preemptive effect does not extend to state regulation of insurance activities.  This paper explores whether states can invoke reverse preemption under McCarran-Ferguson to restrict insurance companies from using drone-gathered data to modify or cancel homeowner insurance policies without express home-owner permission.  The paper argues that insurance companies’ use of drones for this purpose falls within the traditional domain of state insurance regulation.  Because no federal statute expressly addresses the use of drone surveillance in the insurance business, the MFA opens the door for reverse preemption, allowing state laws that limit or bar these practices.  Situating this novel legal conflict at the intersection of aviation regulation, federalism, and insurance practices, the paper concludes that insurance remains firmly within the states’ regulatory authority, which may extend to limiting drone use in furtherance of insurance practices that result in modified or canceled homeowners’ insurance policies.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775401</guid>
    </item>
    <item>
      <title>Cross-sectoral synergies for household energy savings: the role of electric vehicles, solar photovoltaics, and remote work</title>
      <link>https://trid.trb.org/View/2709499</link>
      <description><![CDATA[Over the past two decades, new technologies and behavioral shifts – such as electric vehicles, solar photovoltaics, and increased work-from-home practices – have reshaped residential electricity consumption and cost. However, their combined or synergistic impact on a household’s electricity cost remains unexplored. Leveraging data from the 2020 Residential Energy Consumption Survey, this study uses a structural equation model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. Results indicate that adopting both EVs and PV reduces electricity costs by 31% despite a 16% rise in consumption. When combined with stay-at-home practices, households still experience a 13% cost reduction, even with a 25% increase in electricity consumption. These findings suggest that financial savings are not merely a byproduct of adopting new technologies or behavioral changes but could serve as a key consideration for household contemplating engagement with multiple modern energy solutions simultaneously.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709499</guid>
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    <item>
      <title>Characterization of electric vehicle usage patterns to estimate the flexibilities and potentials for smart charging</title>
      <link>https://trid.trb.org/View/2752085</link>
      <description><![CDATA[Electrification of passenger vehicles is an important measure to decarbonize the transport sector. An efficient introduction of electric vehicles (EVs) requires an understanding of how the charging of EVs impacts the electricity system and if, and to what extent, smart charging strategies, including vehicle to grid (V2G) services, can support the electric grid in the future energy systems. The aim of this thesis is to characterize the flexibility of smart charging including V2G by analyzing the real-world driving, parking, and charging patterns obtained from logged EVs. The analysis is based on data collected from 394 privately owned EVs and survey responses from their owners. The results reveal substantial flexibility potential for smart charging from several perspectives. However, the findings also highlight important limitations that must be carefully considered when estimating flexibility or implementing flexible charging into energy system models. Using the lower state of charge (SOC) threshold for charging decisions and the SOC when charging ends, the flexible battery capacity range is calculated to be 59% on average. The aggregated SOC for all logged EVs is within 60%-80% throughout the entire logging period. The results show that charging is needed in fewer than half of the days in a week for more than 73% of weeks, regardless of the attributes of the EV owners, including commuter category and battery capacity. Furthermore, EVs are charged more frequently than the minimum number of charging events required per week. Thus, there is potential for charging in a way that is flexible in time depending on, for example, grid congestion or spot prices. This is particularly the case for non-commuters with large-battery EVs. The amount of time that EVs are plugged in for smart charging differs by more than a factor of two if one assumes that EVs are plugged in whenever they are parked at home and that EVs are plugged in only when they charge during the parking event.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752085</guid>
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    <item>
      <title>Energy management for vehicle-home-grid systems : degradation-aware online control for residential bidirectional charging</title>
      <link>https://trid.trb.org/View/2752043</link>
      <description><![CDATA[Electric vehicles (EVs) are rapidly becoming a mainstream transport technol ogy, and their growing diffusion is influencing mobility and electricity systems. Since most cars are parked for most of the day, their idle time can offer an opportunity to provide flexibility. When equipped with bidirectional charging capability, an EV can support vehicle-to-home (V2H) operation by supply ing household demand, vehicle-to-grid (V2G) operation by exchanging energy with the grid, and increased photovoltaic (PV) self-consumption by storing surplus solar generation. Unlocking this potential, however, requires control strategies that manage uncertainty in household demand and solar generation, satisfy user-driven mobility requirements, and avoid excessive battery wear. This thesis develops an online framework for residential vehicle-home-grid energy management with rooftop PV integration. During each home-parking interval, energy flows among the EV battery, household load, grid, and PV system are scheduled using a shrinking-horizon model predictive control (SH MPC) approach that updates decisions as new information becomes available. Battery lifetime effects are modeled by combining calendar and cycle aging mechanisms. To handle imperfect foresight, the controller is coupled with a neural-network-based forecaster to estimate future household load and PV generation. The control objective is to minimize total operating cost, jointly accounting for electricity purchase/sale and battery degradation expenses. Simulation studies under Swedish residential conditions demonstrate that degradation-aware bidirectional charging can provide tangible economic value while maintaining acceptable battery aging. Sensitivity analyses confirm that these benefits persist across a wide range of operating scenarios and uncer tainty levels.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752043</guid>
    </item>
    <item>
      <title>Underground Metro-Induced Noise versus Vibration: A Comparative Assessment of Impacts on People's Mental Health Dimensions</title>
      <link>https://trid.trb.org/View/2698812</link>
      <description><![CDATA[Metro systems offer significant environmental, economic, and social benefits to urban areas; however, noise and vibration generated by train operations may adversely affect the mental health of residents living nearby. While previous studies have largely examined noise and vibration separately, their relative contributions to different mental health dimensions remain insufficiently understood. This study addresses this gap through field measurements and resident assessments. Noise and vibration levels were measured in 31 residential buildings adjacent to an underground metro, including 13 without reported complaints and 18 with documented noise or vibration complaints. Residents’ mental health was evaluated using the Adult Self-Report (ASR) questionnaire. Correlation and comparative analyses were conducted to assess relationships between measured noise and vibration indicators and mental health outcomes, considering resident characteristics such as gender, age, and employment status. Results indicate that attention/concentration problems and sleep disturbances were the most affected mental health domains. Threshold levels of train-induced noise and vibration beyond which mental health impacts exceeded annoyance and physiological intervention limits were identified. It was shown that the findings of this research can serve as a basis for promoting sustainable cities and communities through efficient urban planning, determining appropriate buffer distances between metro lines and residential areas, and supporting regulatory authorities in decision-making processes when addressing residents’ complaints.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698812</guid>
    </item>
    <item>
      <title>Valuing parking in the presence of minimum parking requirements and mobility management concepts—Evidence from Munich</title>
      <link>https://trid.trb.org/View/2686985</link>
      <description><![CDATA[The minimum parking requirements for residential buildings in Munich, Germany, are set at one parking unit per apartment. This policy appears to contradict Munich’s policy targets of providing affordable housing and promoting sustainable modes of transportation as it fosters car dependency and inflates housing prices. The objective of this study is to measure residents’ demand for residential off-street parking based on stated preferences. Using a survey with 226 respondents in the Munich metropolitan region, we analyze residents’ willingness to pay (WTP) for a guaranteed private parking unit under different supply scenarios using the contingent valuation method. Beyond the status quo, the supply scenarios encompass a multimodal mobility management concept (e.g., sharing services and public transit subscriptions), additional amenities at the household location (e.g., essential shops within 500 m distance), and enhanced transit accessibility. We use a linear mixed-effects regression model and weighting to derive representative average WTP estimates. We further predict population shares of residents willing to pay at least €49.50 or €95.11 per month as minimum and realistic construction costs of a parking unit, respectively. Our analysis indicates that a mobility management concept reduces of the average WTP by €7.34. We provide evidence that the minimum parking requirements do not reflect the demand given a realistic supply, and that a multimodal mobility management concept can reduce demand by an additional 16.89%. Planners and decision makers can use these findings to adapt parking requirements according to demand and strategic planning goals.]]></description>
      <pubDate>Wed, 15 Jul 2026 15:05:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686985</guid>
    </item>
    <item>
      <title>Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts</title>
      <link>https://trid.trb.org/View/2724620</link>
      <description><![CDATA[Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, and results are compared between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We find that, despite differences in demographics and economic development, the two cities exhibit similarities in the effect of transportation infrastructure and local amenities on dwelling prices. Proximity to the major central city cores (i.e., downtowns) increases sale price. The effect of transportation infrastructure is differentiated, with high quality transit (e.g., subway and BRT) increasing and conventional bus stop proximity decreasing sale price, respectively. We confirm the previous finding in other fields that non-spatial cross-validation over-estimates the prediction accuracy of machine learning algorithms on spatially referenced datasets. We find that the XGBoost model has slightly higher performance than the RF model. We recommend careful use of machine learning methods in the case of spatial data specifically in modeling of land prices.]]></description>
      <pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724620</guid>
    </item>
    <item>
      <title>Factors influencing car ownership in Toronto: Insights from a hurdle-ordered model</title>
      <link>https://trid.trb.org/View/2688660</link>
      <description><![CDATA[This study examines the effects of household socio-demographics, dwelling characteristics and the built environment on household auto ownership using data from a survey of 2007 households in the Greater Toronto Area (GTA). A two-stage hurdle model was constructed, consisting of a binary probit model of vehicle ownership (0 or 1+ cars) and an ordered probit model of fleet size (1, 2, or 3+ cars). Variables representing the five dimensions of density, diversity, destination accessibility, distance to transit, and design were included in the model. It was found that household socio-demographics and dwelling characteristics are the principal drivers of auto ownership, as shown by a greater number of variables significant at the 1% level and larger partial effects for these variables. Among the built environment variables, density, and destination accessibility were the most significant. Overall, the factors influencing ownership and fleet size were found to be the same, though there were some notable exceptions to this. These results show that policymakers seeking to reduce car dependency should focus on policies directly targeting the households, rather than those which target them indirectly through changes to the built environment around their residential location.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688660</guid>
    </item>
    <item>
      <title>Death of the front yards: How the house and car is replacing residential gardens</title>
      <link>https://trid.trb.org/View/2679704</link>
      <description><![CDATA[Middle-ring suburban Sydney is experiencing a quiet yet profound transformation. As redevelopment accelerates, larger houses with expansive footprints and additional driveways are steadily replacing traditional front yards and gardens. This study examines 370 properties across Northern and Greater Western Sydney to quantify these changes. In areas where older, low-density homes have been replaced by newer, larger detached dwellings, the average front garden area has declined by 46%, while driveways and other impervious surfaces have increased by 57%. Most notably, front yard canopy cover has fallen by 62%. These patterns are not due to a lack of policy, but to a planning system in which local controls have become discretionary under ongoing state-level legislative reforms. Broader social trends toward larger homes and greater car ownership further reinforce this shift. The cumulative effect is leading to a substantial loss of private green space, contributing to higher urban heat, reduced biodiversity, and diminished connections with nature. Urban planning reform is urgently needed to embed minimum standards for green space and canopy cover at the site level, ensuring that urban greening targets translate into tangible local outcomes.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679704</guid>
    </item>
    <item>
      <title>A Comprehensive Decentralized Control Framework for Balanced and Reliable Residential EV Charging</title>
      <link>https://trid.trb.org/View/2659181</link>
      <description><![CDATA[This research enhances residential electric vehicle (EV) charging by integrating maximum demand (MD)-based regulation, which improves grid management while minimizing infrastructure and maintenance costs. Multiple EVs connected to distribution transformers (DTs) during peak hours lead to voltage drops, increased losses, and unfair charging distribution across households. This article introduces a decentralized EV charging system that integrates MD-based regulation, considering local parameters such as: 1) power consumption of household loads; 2) grid supply voltage; and 3) state of charge (SOC) of the battery. The proposed approach maintains EV charging within the rated capacity of the DT and the maximum allowable load, effectively minimizing load variance while ensuring a continuous power supply to household loads. To validate the proposed system: 1) a hardware-in-loop (HIL) simulation is conducted with Opal-RT; 2) the responses under various loadings are tested in a Simulink model; 3) a comparison study conducted using real household consumption data; and 4) network integration test and comparison study conducted using modified IEEE low-voltage distribution network (LVDN) in Simulink. The results demonstrate that this approach minimizes load variance, reduces peak-to-average ratios, and achieves fair, reliable charging across the network, regardless of EV connection points, making it a scalable and efficient solution for residential EV integration.]]></description>
      <pubDate>Wed, 22 Apr 2026 14:04:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659181</guid>
    </item>
    <item>
      <title>Colorado Residential Resiliency and Managed Charging: Cooperative Research and Development Final Report, CRADA Number: CRD-05-00167 (Project 7)</title>
      <link>https://trid.trb.org/View/2685493</link>
      <description><![CDATA[This Cooperative Research and Development Agreement (CRADA) validated and modeled impacts of electric vehicle (EV) home charging on service transformers; analyzed diverse grid locations and configurations to determine 'risk factors'; finalized/delivered new design tools and typical EV load curves; and improved/issued new construction standards for transformers, secondaries, and services. To ensure residential charging equity, smart charge management strategies will be studied and analyzed with the goal of creating affordable demand charges for customers.]]></description>
      <pubDate>Tue, 07 Apr 2026 17:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685493</guid>
    </item>
    <item>
      <title>The impact of collapsed vacant houses on the functionality of emergency transportation road networks</title>
      <link>https://trid.trb.org/View/2613457</link>
      <description><![CDATA[Japan frequently experiences earthquakes. In the 2024 Noto Peninsula Earthquake, collapsed houses disrupted emergency transport roads, delaying recovery efforts. This study evaluates the impact of collapsed vacant houses on the emergency transportation roads network. Using data from 12 cities in southern Ibaraki Prefecture, we identified vacant houses that may obstruct traffic. We assessed road passability based on vehicle size. Two optimization problems were analyzed for routes between municipal offices: the minimum spanning tree problem, focusing on minimizing removal costs of vacant houses, and the shortest path problem, emphasizing rapid response. We quantified the trade-off between cost efficiency and responsiveness to support better disaster planning and resilient transportation network design.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:17:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613457</guid>
    </item>
    <item>
      <title>Machine Learning-Based EV Charging Management System for Vehicle-to-Home and Vehicle-to-Building</title>
      <link>https://trid.trb.org/View/2604021</link>
      <description><![CDATA[With the increase in adoption of electric vehicles (EVs) around the world, there has been an increasing interest in bidirectional EV charging that allows EV to act as an energy storage device and thus, provide energy arbitrage in smart home and building through vehicle-to-home (V2H) and vehicle-to-building (V2B) setups. However, a smart energy management system is required to efficiently manage the exchange of energy between EVs and homes and buildings in order to fully capitalize on the potential of V2H and V2B. In this article, a machine learning (ML)-based approach that uses long short-term memory (LSTM) to predict EV charging/discharging power in real time has been proposed. The proposed approach factors various parameters, such as the state of charge (SoC) of the EV, generation from rooftop solar PV, and time of use electricity tariff to predict EV charging/discharging power. The proposed model is trained using historical data and then deployed to predict EV charging/discharging power in the V2H and V2B systems. The proposed LSTM-based algorithm is comprehensively tested using real-life data, and the results demonstrate that the proposed algorithm provides accurate predictions without violating the physical constraints associated with EVs.]]></description>
      <pubDate>Wed, 10 Dec 2025 16:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604021</guid>
    </item>
    <item>
      <title>What drives you to relocate your home? Investigating preferences and residential mismatching of recent (prior to COVID and during-after COVID) movers in the Greater Toronto Area</title>
      <link>https://trid.trb.org/View/2598678</link>
      <description><![CDATA[This paper presents a survey of homeowners and renters who moved within or into the Greater Toronto Area (GTA), Canada, since January 2016. This timeline covers pre- and during-COVID movers. The survey investigates households’ residential preferences and the potential dissonance between their preferred and actual residential location choices. The respondents are asked to answer questions about their previous and current residences, their housing search, and hypothetical residential location choices given the option of telecommuting. Empirical analysis of the actual and hypothetical residential location choices is conducted through a joint revealed preference and stated preference (RP-SP) error component mixed logit model. The model estimations identify discrepancies between RP and SP choices, indicating the presence of residential dissonance. The positive effect of transit accessibility variables shows that people are more likely to choose residential locations with better accessibility. However, dissonance in transit accessibility is found in both models, suggesting homeowners and renters may settle for lower accessibility in real life. Moreover, the model results show that homeowners and renters are more likely to relocate when working remotely, indicating the significant influence of telecommuting on residential dissonance.]]></description>
      <pubDate>Tue, 25 Nov 2025 15:01:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598678</guid>
    </item>
    <item>
      <title>Replacing monetary with social incentives for community-based home charger owners’ sharing adoption</title>
      <link>https://trid.trb.org/View/2617207</link>
      <description><![CDATA[Promoting community-based home charger sharing is a promising strategy to address inequitable access to community charging. To achieve this, policymakers can use monetary incentives, such as higher charging price premiums, or non-monetary social incentives, such as fostering better neighborhood relationships, improving members’ sense of community belonging, or intensifying their social connections. However, the substitution effects between these incentives remain unclear. To address this, the research integrated a discrete choice experiment and an agent-based simulation model, to simulate home charger owners’ sharing adoption behavior. We found that a 15 % increase in the number of random links among home charger owners can replace a 10 % rise in the charging price premium. Adding an average 4 ties per agent or doubling the sense of community belonging yields equivalent effects. These findings imply that social incentives can cost-effectively substitute for monetary incentives in promoting home charger owners’ sharing adoption.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617207</guid>
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