<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Smart Infrastructure-Based Anomaly Detection under Cyberattacks</title>
      <link>https://trid.trb.org/View/2709395</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are increasingly exposed to cyberphysical attacks, yet most existing detection methods overlook the potential of infrastructure-based monitoring. In this article, we propose a novel infrastructure-based anomaly detection framework to identify cyberattacks on CAVs under time interference attacks and vehicle-to-everything communication attacks. The optimal attack strategies are generated using a Pareto optimization that jointly maximizes safety risk and stealthiness. To detect the attacks, a transformer-based trajectory prediction model is developed to predict normal driving behaviors. An XGBoost classifier is then developed to detect anomalies by comparing predicted and observed vehicle trajectories. We validate the proposed framework using real-world trajectory data. The results show that the proposed anomaly detection model achieves high accuracy with low false positive and false negative rates in both offline and online settings. These results demonstrate that smart infrastructure can significantly improve the safety of CAVs by enabling timely and accurate detection of cyberattacks.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709395</guid>
    </item>
    <item>
      <title>Optimal Double-Layer Smart EV Charging Using a Portable Multiconnector System</title>
      <link>https://trid.trb.org/View/2665580</link>
      <description><![CDATA[The reluctance of clients to actively engage in electric vehicle (EV) charging, combined with the scarcity of EV charging stations (EVCS), creates accessibility challenges and inefficiencies that hinder EV adoption. Existing planning approaches rely on static queue structures and fixed infrastructure, limiting their responsiveness to real-time demand and user urgency. To address this gap, we introduce a patented portable multiconnector system (PMS) that reduces CAPEX by allowing multiple EVs to connect to a single charger without major infrastructure upgrades. The system is governed by a two-stage strategy: 1) a double-layer laxity-based allocation (DLBA), managed by a queue management system (QMS), enabling dynamic, nonpreemptive reordering of EVs based on urgency; and 2) an enhanced energy management system (EMS) with a penalty mechanism to discourage noncritical charging, thereby reducing energy costs and mitigating grid impact. Designed to improve user experience by increasing accessibility and reducing waiting times while ensuring efficient infrastructure utilization, the PMS-DLBA-EMS framework is validated through a shopping center case study, demonstrating substantial reductions in operational expenses and superior service delivery compared to traditional architectures and static scheduling.]]></description>
      <pubDate>Thu, 11 Jun 2026 09:33:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665580</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>Sharing Private Charging Piles to Develop Electric Vehicle Charging and Vehicle-to-Grid Services</title>
      <link>https://trid.trb.org/View/2567456</link>
      <description><![CDATA[The increasing use of electric vehicles (EVs) has led to challenges in determining the most effective methods for charging their batteries. A potential solution to address this issue is the expansion of smart homes equipped with renewable energy sources, such as wind turbines and solar panels, to meet the growing demand for EV charging. By installing private charging piles (PCPs) in homes and enabling their sharing, both homes and EVs can benefit economically. Moreover, these PCPs can provide vehicle-to-grid services that can enhance the stability of the power system. This study develops a two-stage stochastic model to optimize the energy system of homes equipped with various types of distributed generation and PCPs. To address the role of uncertainty in parameters such as demands and renewable power generation, a sample average approximation (SAA) method is used to optimize the model. The SAA method is developed in a way that an autoregressive moving average (ARMA) model is employed to generate scenarios, and a fuzzy c-means (FCM) clustering algorithm is utilized to reduce the number of scenarios. This study utilizes data from the city of Calgary, Canada, to examine the application of the proposed model in a real-life setting. The results demonstrate that sharing PCPs benefits households by optimizing energy use, supports EV owners by making charging more convenient, and helps governments by reducing the need to build additional public charging stations.]]></description>
      <pubDate>Thu, 28 Aug 2025 17:11:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567456</guid>
    </item>
    <item>
      <title>Vehicle-to-Home and Vehicle-to-Building: A Techno-Economic Analysis Under Varying Charging Methods and Tariff</title>
      <link>https://trid.trb.org/View/2553596</link>
      <description><![CDATA[Electric vehicles (EVs) can be used as energy storage as well as flexible loads in modern power systems. The use of bidirectional EV chargers enables energy arbitrage in smart houses and buildings by facilitating vehicle-to-home (V2H) and vehicle-to-building (V2B). This article presents a comprehensive techno-economic analysis of V2H and V2B systems, with a focus on the impact of various tariff structures on their feasibility and economic viability. The study considers real-life residential and workplace load profiles, as well as the variability in solar photovoltaic (PV) generation, to design cost-effective V2H and V2B charging schedules. The synergies and trade-offs between different tariff structures for V2H and V2B technology are analyzed, identifying key factors that influence system profitability and scalability across various market contexts. The economic viability of transitioning between different types of charging is assessed for both household and building applications. The results suggest that the proposed approach helps in developing an adequate cost-effective framework that helps in realizing techno-economic benefits of V2H and V2B systems.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:10:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553596</guid>
    </item>
    <item>
      <title>Integrating activity-based transport and building occupancy models for campus-scale energy management</title>
      <link>https://trid.trb.org/View/2556727</link>
      <description><![CDATA[Arrivals and departures lie at the intersection of travel and building occupancy behaviours which dominate the landscape of energy demand in urban areas. Although transport and building systems are clearly linked, existing studies rarely consider the interactions between these systems in their modelling frameworks, thus restricting the policy-relevant scenarios that can be tested. This paper contributes to the field of data-driven energy modelling by proposing a flexible framework to integrate the modelling of travel and building occupancy behaviours, in which a travel simulator is coupled with a building occupancy model through a proposed mesoscopic link. The framework is operationalised in the context of the South Kensington Campus, Imperial College London, using the UK Time Use Survey data and Wi-Fi traceable logs. Implementing the framework for a hypothetical transport incident (i.e. sudden closure of the nearest underground station) generates people’s occupancy and circulation patterns across buildings, thus providing actionable insights for district-level smart grid planning and management. From a district planning perspective, occupancy schedules and dynamics in closed buildings are sensitive to incidents, whereas open and shared buildings are relatively stable. This finding indicates the need for flexible energy controls and smart grids with energy storage. From a building management perspective, occupancy durations generally reduce when affected by incidents, suggesting shortening the schedules of heating, ventilation and air-conditioning systems. From a facility management perspective, big changes in occupancy of closed buildings indicate unstable demands for the surrounding equipment (e.g. e-scooters, chargers), and efficiencies may be gained by allocating spaces/schedules to meet the dynamic demand.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556727</guid>
    </item>
    <item>
      <title>Harmonics Measurement, Analysis, and Impact Assessment of Electric Vehicle Smart Charging</title>
      <link>https://trid.trb.org/View/2553764</link>
      <description><![CDATA[Smart charging for Electric Vehicles (EVs) is gaining traction as a key solution to alleviate grid congestion, delay the need for costly network upgrades, and capitalize on off-peak electricity rates. Governments are now enforcing the inclusion of smart charging capabilities in EV charging stations to facilitate this transition. While much of the current research focuses on managing voltage profiles, there is a growing need to examine harmonic emissions in greater detail. This study presents comprehensive data on harmonic distortion during the smart charging of eight popular EV models. The authors conducted an experimental analysis, measuring harmonic levels with charging current increments of 1A, ranging from the minimum to the maximum for each vehicle. The analysis compared harmonic emissions from both single and multiple EV charging scenarios against the thresholds for total harmonic distortion (THD) and individual harmonic limits outlined in power quality standards (e.g. IEC). Monte Carlo simulations were employed to further understand the behavior in multi-vehicle scenarios. The results reveal that harmonic distortion increases as the charging current decreases across both single and multiple vehicle charging instances. In case studies where several vehicles charge simultaneously, the findings show that as more EVs charge together, harmonic cancellation effects become more pronounced, leading to a gradual reduction in overall harmonic distortion. However, under worst-case conditions, the aggregate current THD can rise as high as 25%, with half of the tested vehicles surpassing the individual harmonic limits.]]></description>
      <pubDate>Fri, 20 Jun 2025 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553764</guid>
    </item>
    <item>
      <title>Charging station localization and sizing determination considering smart charging strategies based on NSGA-III and MOPSO</title>
      <link>https://trid.trb.org/View/2519562</link>
      <description><![CDATA[The ownership of electric vehicles (EVs) has experienced a significant increase in recent years all over the world. However, the unmanaged and uncontrolled connection of a large number of EVs to the grid poses significant threats to grid stability and may result in heightened carbon emissions. This study introduces a smart charging scheduling method that concurrently takes into account charging costs, grid stability, and carbon emissions for EV users. This method is solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Based on the derived solution, three charging strategies were compared with data from four different countries with different energy structure. Both the total distance cost and total construction cost were considered to determine four options for the localization and sizing of EV CSs. The findings indicate that, in temporal terms, the optimal case for each strategy reduces charging costs, grid peak-valley difference, and carbon emissions by 6.66 %, 42.39 %, and 3.38 %, respectively. In spatial terms, the study elucidates the impact of various charging strategies on the localization and sizing of CSs. This study demonstrates the potential of an innovative method for long-term CS localization and sizing determination to provide direct guidance to management department.]]></description>
      <pubDate>Tue, 27 May 2025 09:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519562</guid>
    </item>
    <item>
      <title>Software-Defined Radio-Based IEEE 802.15.4 SUN FSK Evaluation Platform for Highly Mobile Environments</title>
      <link>https://trid.trb.org/View/2512397</link>
      <description><![CDATA[IEEE 802.15.4 smart utility network (SUN) frequency-shift keying (FSK) has attracted considerable attention as a wireless communication standard designed for use in essential applications required by Internet of Things (IoT) systems. However, longer transmission distances in highly mobile environments are required to support various applications in next-generation IoT systems, such as vehicle-to-everything, automated driving, and drone control systems. Although research on wide-area, highly mobile communications has been conducted via computer simulations, an experimental evaluation platform for further research has not been developed. In this study, the authors developed an experimental evaluation platform for SUN FSK in very high frequency bands. The developed platform comprises a signal generator-based transmitter and a software-defined radio-based receiver. It was proven to be capable of transmitting a power of ≥5 W through a power amplifier and was suitable for laboratory and field experiments. In addition, the authors developed received signal processing methods, including a packet detection method and a channel estimation method, which were designed to achieve wide-area, highly mobile communication. In laboratory experiments, the packet error rate characteristics required by IEEE 802.15.4 were achieved even at a transmission distance of >10 km at vehicular speeds of several tens of km/h.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512397</guid>
    </item>
    <item>
      <title>A Framework-Compatible Hierarchical Railway Power Regulation Strategy With the Integration of Energy Storage-Embedded Railway Power Flow Controller</title>
      <link>https://trid.trb.org/View/2511911</link>
      <description><![CDATA[Intelligent power regulation is a prominent feature of smart railway power systems (RPSs). To achieve this target, the energy storage-embedded railway power flow controller (ES-RPFC) can be adopted, as it provides an effective solution for demand management (DM) and power quality (PQ) improvement. In practice, those two functions of ES-RPFC are often implemented in two native less-compatible frameworks, and the converter rating, which significantly influences the whole system’s investment, is seldom considered. These deficiencies diminish the global performance of the whole system. To address them, a novel power regulation strategy is proposed. In this strategy, 1) a unified analytic mathematical framework is first proposed for ES-RPFC’s compatibility improvement on both DM and PQ control, which is also beneficial for calculation efficiency improvement; and 2) a hierarchical control strategy is developed upon the framework in 1). This strategy enables compatible implementation of DM and PQ control with minimized back-to-back converter (BTBC) rating while making the RPS exhibit satisfactory grid-connection performance. A comprehensive measured data-based performance evaluation for the proposal is carried out, and the results show that compared with the traditional method, the BTBC rating is reduced by almost 43% in the studied case. Moreover, the proposal’s real-time implementation feasibility is verified by hardware-in-the-loop (HIL) tests.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511911</guid>
    </item>
    <item>
      <title>Transactive-Based Day-Ahead Electric Vehicles Charging Scheduling</title>
      <link>https://trid.trb.org/View/2511985</link>
      <description><![CDATA[In this article, a transactive-based scheduling approach is proposed to optimize electric vehicle (EV) charging/discharging scheduling taking into account the technical requirements of EVs with different state-of-charge (SOC) levels and EV owners’ preferences. In the proposed approach, an EV aggregator (EVA) solves an optimization problem to determine the charging/discharging schedule of each individual EV in the EV Parking Lot (PL) in which the response curves of individual EVs are used to consider the EV owners’ charging/discharging preferences. Then, the EVAs provide their optimum day-ahead bids to the corresponding DSO based on calculated distribution locational marginal prices (DLMPs). The DSO’s transactive market-clearing procedure is simulated to iteratively calculate DLMPs in the local distribution area (LDA) nodes. The Monte Carlo (MC) scenarios are used to model the uncertainties associated with the EVs’ parameters and the driving behavior of the EV owners. Also, the robust optimization method is used to model the uncertainties associated with LMPs of the transmission network (TN) bus, distributed renewable energy resources (DRERs), and load demand. The proposed model is implemented on the modified IEEE-33 node distribution system and the effectiveness of the model is investigated and presented.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511985</guid>
    </item>
    <item>
      <title>A Synthesis of Railway Concrete Crosstie Advancements in North America</title>
      <link>https://trid.trb.org/View/2550966</link>
      <description><![CDATA[There are over 35?million concrete crossties (sleepers) installed in track in North America, with approximately 750,000 to 1,500,000 additional new ones installed annually. These components have been the focus of much research over recent years, which is resulting in significant advancements in their design and use. New solutions are addressing rail seat deterioration, abrasion, splitting, and other issues that have been associated with past derailments or have required major intervention. Moreover, concrete crosstie design is moving toward a performance-based approach, which is more efficient than the traditional prescription-based methods with generous safety factors. Smart crossties are also emerging, which go far beyond the traditional concepts of crosstie application. This paper presents an overall picture of the state of the art of concrete crossties in North America, linking fundamentals, industry challenges, design approaches, recent developments, and trends for the future.]]></description>
      <pubDate>Mon, 12 May 2025 09:45:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550966</guid>
    </item>
    <item>
      <title>Optimal management of smart grid rental and electric vehicles for remote energy sharing between distinct-scale buildings with novel business model development</title>
      <link>https://trid.trb.org/View/2522918</link>
      <description><![CDATA[Renewable energy sharing by internal grids has been proposed to enhance the load matching of zero-emission building (ZEB) clusters. However, past research focused on how the internal grid enhanced energy sharing from an energy-based perspective. The power limitation of the internal grid, the collaboration of the internal grid and electric vehicles (EVs), and the business model for renting the grid capacity from the grid operator still need to be investigated. In this work, an internal grid and twenty EVs are used to enhance the techno-economic performance of two distinct-scale buildings by remote energy sharing. The result shows that Case 10 with vehicle-to-building (V2B) with a rented grid capacity of 1500 kW improves the matching from 0.491 to 0.506 and increases the relative net present value (NPVᵣₑₗ) from 9.52×10⁸ to 9.61×10⁸ HKD, compared to only building-to-vehicle (B2V) cases. The neutral grid rental fee is in the middle of its upper and lower limits, which solves the benefit contradictions between the stakeholder's electric savings and the grid operator's income. A proper rented capacity for the stakeholder is where the internal grid provides the highest present value (PV). To enhance the scalability and applicability, some system design parameters, EV parameters, and economic parameters are tested to show the possible deviations in the techno-economic performance.]]></description>
      <pubDate>Mon, 28 Apr 2025 08:50:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522918</guid>
    </item>
    <item>
      <title>Smart Parking Systems: A Comprehensive Review Of Digitalization Of Parking Services</title>
      <link>https://trid.trb.org/View/2529919</link>
      <description><![CDATA[Smart parking systems (SPS) address issues that plague traditional parking methods by offering data on real-time parking availability, optimizing the use of space, and facilitating convenient payment solutions. Despite the timeliness and importance of the systems, however, the literature fails to adequately identify areas within SPS that can be vastly improved by innovation. This study addresses the research gap by identifying the key limitations of 124 comprehensively reviewed academic papers and offering innovative solutions. For sensor technology, the challenge of environmental effects and camera line-of-sight issues is tackled with a proposed integrated sensor framework, combining radar precision with camera coverage, all enhanced by AI for greater detection accuracy. Communication networks, currently hindered by scalability and node failure, could be improved with a mesh network architecture for better reliability. To address data management concerns, specifically data integrity and security, the integration of blockchain technology is suggested to protect against data breaches and boost user confidence. Lastly, to simplify complex SPS user interfaces, AI-driven adaptive interfaces are recommended to personalize the user experience and improve system engagement. The findings of this study will be instrumental for city planners, SPS developers, and parking authorities who are tasked with implementing efficient and reliable smart parking solutions.]]></description>
      <pubDate>Wed, 23 Apr 2025 16:10:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2529919</guid>
    </item>
    <item>
      <title>A Predictive Two-Stage User-Centered Algorithm for Smart Charging of Plug-In Electric Vehicles Considering the State of Health of the Battery</title>
      <link>https://trid.trb.org/View/2511715</link>
      <description><![CDATA[This article presents a novel user-centered smart charging algorithm that follows different energy management scenarios for plug-in electric vehicles (PEVs) based on the user’s demand. The proposed approach consists of two separate stages: the model predictive control (MPC) with linear optimization and the rule-based fast method. This algorithm aims to reduce the total cost of energy exchanged with the grid from the user’s point of view while consuming the maximum photovoltaic (PV) power generation. In this regard, various scenarios are predicted and provided to the user in the form of charging costs and the state of health (SOH) of the PEV battery. Then, based on the user’s choices, the desired charging scenario is applied and the probable online small errors due to the other uncertainties are compensated. The effectiveness and flexibility of the proposed algorithm are evaluated through various simulation results for three PEVs at the same time. In addition, to provide further verification, the real-time part of the algorithm is executed in the OPAL-RT simulator (OP5700), including the power hardware-in-the-loop (HIL) method.]]></description>
      <pubDate>Mon, 14 Apr 2025 09:35:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511715</guid>
    </item>
  </channel>
</rss>