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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Residual-Enhanced Fuzzy State-Space Modeling for Real-Time Temperature Monitoring of Battery Thermal Processes</title>
      <link>https://trid.trb.org/View/2665582</link>
      <description><![CDATA[Temperature monitoring is essential for preventing severe thermal failures in the battery management system (BMS). Therefore, accurately modeling the thermal dynamics, particularly under complex operation conditions such as cyclic charge–discharge, is crucial for effective battery management. In this article, a residual-enhanced fuzzy state-space modeling is proposed for the battery thermal process to achieve real-time temperature monitoring. First, an updated nominal distributed parameter system (DPS) is constructed, consisting of two components: a nominal part identified offline via Takagi–Sugeno (T–S) fuzzy approximation, and an updated part derived from the temporal residual state estimation of the residual extended state observer (ESO). Then, an online temporal model is established by integrating the temporal part of the updated nominal DPS with the residual ESO. In addition, a fuzzy spatial mapping filter (SMF) is designed to provide update signals for the online model under a few sensors. The convergence of the proposed online model will be demonstrated in Hilbert space. Finally, the proposed modeling method is applied to the battery thermal process for real-time temperature monitoring successfully.]]></description>
      <pubDate>Thu, 11 Jun 2026 09:33:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665582</guid>
    </item>
    <item>
      <title>Fusion Optimization of KAN and Transformer Under the Hesitant Fuzzy Environment and Its Application in Intelligent Transportation Planning</title>
      <link>https://trid.trb.org/View/2665539</link>
      <description><![CDATA[Intelligent transportation planning holds an important position in modern urban management. However, due to the complexity and variability of traffic conditions as well as the inherent uncertainty of information, relying solely on algorithms is insufficient. Therefore, it is crucial to incorporate expert judgment to dynamically adjust and optimize path planning by combining experience with real-world conditions. To address these challenges, this article integrates the Kolmogorov-Arnold networks (KANs) into the transformer to propose the transformer KANs. Building upon this, we integrate the hesitant fuzzy set (HFS) into the Transformer-KAN architecture to propose the hesitant fuzzy Transformer-KANs (HF-Transformer-KAN) model, which can address subjective information in intelligent transportation planning processes. Furthermore, we propose forward and backward propagation of the model, parameter updating process, and design an optimization algorithm to be applied it successfully. Lastly, two illustrated examples of intelligent transportation are provided from two perspectives of comparison and application, which fully show the robustness and accuracy of classification results derived by the proposed methods.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665539</guid>
    </item>
    <item>
      <title>A Fuzzy Granular Support Vector Machine for Network Traffic Anomaly Detection</title>
      <link>https://trid.trb.org/View/2659007</link>
      <description><![CDATA[To address the challenges in network anomaly detection, such as data label imbalance and the poor performance of traditional Support Vector Machines (SVM) in fitting high-dimensional, large-sample datasets, this paper proposes a novel framework for network anomaly detection. The core contribution is the proposal of a Fuzzy Granular Support Vector Machine (FGSVM) based on fuzzy granular vectors. FGSVM constructs fuzzy granular vectors by performing fuzzy granulation on data features, thereby comprehensively capturing both local feature distributions and global data characteristics. This method granulates all data, avoiding the information loss caused by sample compression in traditional methods. To further optimize model performance, this paper also introduces a Noise-Aware Hybrid Weighted Sampling (NA-HWS) method, which optimizes the data distribution by purifying the data and applying weighted sampling to critical boundary regions. This approach combines with FGSVM to form the more powerful S-FGSVM model. Comprehensive experimental results on several internationally recognized benchmark datasets, including NSL-KDD, CIC-IDS-2017, and UNSW-NB15, confirm the significant superiority of the proposed methods. The S-FGSVM model demonstrated outstanding performance on all key metrics, significantly surpassing most baseline algorithms, including both deep learning and classic machine learning models. Compared to deep learning models, which generally have high computational overhead, the model proposed in this study shows a significant advantage in computational efficiency, achieving an ideal balance between detection performance and operational efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659007</guid>
    </item>
    <item>
      <title>Multi-objective optimization of truck-drone cooperative routing problem based on customer classification and fuzzy time windows</title>
      <link>https://trid.trb.org/View/2592386</link>
      <description><![CDATA[With the growing demand for personalized logistics services, the combined use of drones and trucks as collaborative delivery services has become increasingly crucial for improving service levels. A key challenge lies in the rational allocation of limited logistics resources to enhance customer satisfaction. To address this challenge, this study proposes a novel multi-objective optimization model for truck-drone collaborative routing, utilizing customer value classification and fuzzy time window management. First, considering the Pareto principle (also known as the 80/20 rule) of customer profitability, customers are classified into three levels: high, medium, and low based on their current purchase value (CPV) and potential purchase value (PPV). This classification allows for a differentiated delivery strategy: high-level customers receive door-to-door delivery via drones, medium-level customers are served by trucks at designated pickup nodes, and low-level customers are directed to centralized self-pickup locations. Second, to better accommodate customer preferences, flexible time windows are introduced, including desired and tolerated time frames, with varying sensitivity coefficients assigned to different customer levels. Finally, a multi-objective optimization model is constructed to minimize costs and maximize customer satisfaction. To solve this model, a hybrid genetic algorithm-simulated annealing (GA-SA) approach is employed, incorporating dynamic adjustment strategies and a fast, non-dominated sorting algorithm to enhance computational efficiency. Benchmark instances are used to evaluate the proposed algorithm, demonstrating its capability to generate high-quality solutions. Additionally, a real-case study in Chongqing, China, validates the effectiveness of both the proposed model and algorithm. The results indicated that while the costs of truck-drone collaborative delivery were comparable whether or not customer classification was considered, customer satisfaction improved by 22.11% when classification was taken into account. This proves the potential of the proposed delivery strategy to enhance customer satisfaction while optimizing logistics delivery routes. The findings also have practical implications for various supply chains, confirming that integrating our proposed framework can significantly improve customer satisfaction.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592386</guid>
    </item>
    <item>
      <title>Effect of Time Intervals on Short-Term Traffic Flow Prediction Performance for an Adaptive Fuzzy Inference System Approach</title>
      <link>https://trid.trb.org/View/2628259</link>
      <description><![CDATA[Time interval plays a pivotal role for short-term traffic flow prediction since time interval determines traffic data characteristics and, hence, the performances of such prediction methods. In this sense, the effects of time intervals on prediction performance should be investigated systematically when developing short-term traffic flow prediction methods. However, the investigation into time interval effects on short-term traffic flow prediction methods is still limited in the literature, hindering the incorporation of such prediction methods into real-world transportation applications. To this end, this paper proposes an online adaptive fuzzy inference system (FIS)-based short-term traffic flow prediction method with recursive parameter adjustment and designs an experiment to analyze the effect of time intervals on the prediction performance of this proposed method. Using real-world traffic flow data collected from highway systems of the United Kingdom and the United States, the proposed FIS-based method was calibrated through sensitivity analysis, and the effects of time intervals on the proposed FIS-based method were investigated through aggregated performance, group performance, disaggregated prediction performance, and comparative performance. Empirical results showed that the prediction performance of the proposed FIS-based method increases sharply for time intervals from 1- to 10-min, remains stable for time intervals between 10- and 20-min, and decreases slightly for time intervals from 20- to 30-min. This finding delineates the applicable range of time intervals for the proposed FIS-based method, which will be helpful for integrating the proposed prediction method into real-world proactive transportation management systems based on these time intervals. Future studies are recommended to advance the investigation into the effects of time intervals in transportation-related studies.]]></description>
      <pubDate>Thu, 05 Feb 2026 11:52:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628259</guid>
    </item>
    <item>
      <title>Task-based human error probability assessment in maritime pilotage using a dual-intuitionistic fuzzified SLIM framework</title>
      <link>https://trid.trb.org/View/2627184</link>
      <description><![CDATA[Maritime pilotage is a critical navigational practice that ensures the safe passage of vessels through high-risk areas such as ports, straits, and canals. These operations require advanced ship-handling skills and deep local knowledge, particularly when bridge teams are unfamiliar with the operating environment. The complexity of pilotage, which arises from variable vessel characteristics, changing environmental conditions, and regulatory demands, makes human reliability a central concern. Traditional human reliability methods often struggle to fully capture the uncertainty inherent in expert judgment. To address this, the present study proposes a novel framework based on the Intuitionistic Fuzzy Success Likelihood Index Method (IF-SLIM) for estimating human error probabilities (HEPs) during pilotage operations. By integrating intuitionistic fuzzy set theory into SLIM, the model allows expert input to be captured through membership and non-membership functions improving the realism of performance assessment under uncertainty. The dual-fuzzification approach is applied to both performance shaping factors and manoeuvring tasks, offering detailed insights into task-specific risk. The proposed framework aims to support pilotage authorities and maritime safety stakeholders in identifying high-risk areas, enhancing pilot assignment strategies, and improving overall reliability in manoeuvring operations.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627184</guid>
    </item>
    <item>
      <title>Integration of MIMAH and Fuzzy Bayesian Networks for risk analysis in chemical tanker loading operations</title>
      <link>https://trid.trb.org/View/2616224</link>
      <description><![CDATA[This study provides a systematic risk assessment approach for chemical tanker loading operations, focusing on a high-risk scenario identified through operational data from a model vessel. To address the complexities of chemical transportation, a hybrid methodology combining the Methodology for the Identification of Major Accident Hazards (MIMAH) and Fuzzy Bayesian Network (FBN) analysis was developed. MIMAH's structured framework systematically identifies critical events using a Bow-Tie (BT) diagram, integrating Fault Tree (FT) and Event Tree (ET) providing a thorough breakdown of potential accident pathways. This BT structure was converted into a Bayesian Network (BN) to improve probability estimations by incorporating conditional dependencies and expert-driven fuzzy logic, particularly where historical data was limited. The study further employed a dual-method sensitivity analysis, integrating Fussell-Vesely (FV) importance measures and Improvement Index (II), to identify critical and improvement-prone basic events (BEs). Key findings highlight the dominance of human error in high-risk events, particularly manifold connection failures and incorrect valve operations, alongside mechanical vulnerabilities with significant improvement potential. This hybrid approach extends ARAMIS principles to maritime contexts, integrating reliability-based and fuzzy-based probability estimation methods in chemical tanker operations, and provides a detailed and adaptable framework that enhances safety and resilience in hazardous maritime transport.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616224</guid>
    </item>
    <item>
      <title>Probabilistic modeling of domestic ferry accident causes in Kenya's Likoni ferry route using fuzzy Bayesian network</title>
      <link>https://trid.trb.org/View/2627057</link>
      <description><![CDATA[Enhancing the reliability of Kenya's domestic ferry transportation system is crucial for mitigating safety-critical risks. This helps safeguard the lives of a daily average of 150,000 passengers and 5800 motorists who rely on ferry transport in the Mombasa channel. This study utilizes causal probabilistic modeling through a fuzzy Bayesian network approach to assess the risks associated with 18 causal factors and their 11 interdependencies. These risk factors were extracted from historical ferry accidents in the Likoni ferry route using the grounded theory approach. The developed Bayesian network model calculated a risk value indicating a 39.1 % probability of ferry accidents. Propulsion System Failure was identified as the most critical causal factor, with a fuzzy possibility score (FPS) of 0.423. Forward predictive reasoning demonstrated that implementing risk-reduction measures could lower the likelihood of ferry accidents to 21.2 %. Additionally, backward diagnostic reasoning pinpointed the two most critical causes: inadequate crew competence and propulsion system failure. The model was validated using sensitivity analysis and three-axiom validation approach, underscoring its capability to evaluate the influence of causal factors.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627057</guid>
    </item>
    <item>
      <title>Low-Carbon Dispatch of Integrated Energy Systems Considering Charging/Discharging Flexibility of Electric Vehicles</title>
      <link>https://trid.trb.org/View/2604022</link>
      <description><![CDATA[Constructing low-carbon power systems generally requires high penetrations of renewable energy sources (RESs). This results in increasing demands for operating flexibility to accommodate RES uncertainty. Therefore, this work exploits the flexibility of multienergy coordination and electric vehicles (EVs). First, we construct network models for an integrated electricity-heat–gas system considering heterogeneous energy resources, energy generation and heat conversion devices, and EVs. We also categorize EV charging modes according to the uncertainties in user charging behaviors. In addition, the Minkowski method is employed to develop a load-storage dispatchability model for EV clusters. Second, we present a two-stage market framework that integrates carbon emission trading (CET) and tradable green certificates (TGCs) to mitigate the carbon emissions of integrated energy systems and accelerate the adoption of RES. Furthermore, we present a fuzzy chance-constrained optimization model that incorporates uncertainties in RES and load through fuzzy parameters to assess their influence on operating costs, as well as EV charging and discharging behaviors. The results show that the proposed method effectively balances the economic, environmental, and stability considerations while enhancing robustness against uncertainty.]]></description>
      <pubDate>Wed, 10 Dec 2025 16:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604022</guid>
    </item>
    <item>
      <title>A Fuzzy System to Measure Additive Manufacturing Maturity: A Case Study in the Automotive Industry</title>
      <link>https://trid.trb.org/View/2565592</link>
      <description><![CDATA[To survive in an increasingly competitive environment, adopting innovative technologies is crucial, particularly in the 4th industrial revolution. Additive manufacturing (AM), a key technology of Industry 4.0, enables layer-by-layer production and fosters business model innovation. Organizations must integrate AM using maturity models to diagnose, monitor, and guide its implementation effectively. However, no existing AM Maturity Model addresses the imprecision inherent in human judgment or the uncertainty in assessing AM maturity. This article proposes a novel contribution: a fuzzy system designed to diagnose and evaluate AM maturity, incorporating fuzzy set theory to handle imprecision and ambiguity and using knowledge rules to model various maturity perspectives. The system was applied in the automotive industry, diagnosing an intermediate AM maturity level of 3, where AM complements traditional manufacturing. The findings also identified challenges, such as the need to improve employee AM competencies. This system is as a valuable tool for decision-makers, supporting strategic planning and resource alignment with with AM capabilities.]]></description>
      <pubDate>Wed, 20 Aug 2025 13:34:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2565592</guid>
    </item>
    <item>
      <title>Meta-Heuristic Adaptive Equivalent Consumption Minimization of a Fuel Cell Vehicle Incorporating Fuzzy Inference and Particle Swarm Optimization</title>
      <link>https://trid.trb.org/View/2553503</link>
      <description><![CDATA[The fuel cell electric vehicle (FCEV) is a promising pathway for transport decarbonization. Managing the FCEV’s onboard energy is a challenging task that requires advanced control of multiple power sources. The equivalent consumption minimization strategy (ECMS) has shown effectiveness in the energy management of hybrid electric vehicles (HEVs), and its performance depends on the settings of the equivalent factor (EF). This article proposed a meta-heuristic fuzzy inference ECMS method (MA-ECMS) for FCEV to improve efficiency and robustness. The fuzzy inference system (FIS) of the MA-ECMS is optimized with a meta-heuristic algorithm, chaotic-enhanced particle swarm optimization (CAPSO), which optimizes the EF settings adaptively based on battery state-of-charge (SoC) and vehicle power demand. A PI-based SoC penalty regulator is added to the MA-ECMS to improve the SoC tracking capability. The optimality and robustness are evaluated for different driving cycles, and initial battery SoC states using the ECMS and A-ECMS as baselines. The real-time performance of MA-ECMS is verified on a processor-in-the-loop (PiL) testing platform. The results suggest that the MA-ECMS can achieve lower hydrogen consumption, better robustness, and fewer SoC tracking errors. Compared with the baselines, up to 13.13% hydrogen can be saved.]]></description>
      <pubDate>Fri, 20 Jun 2025 17:03:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553503</guid>
    </item>
    <item>
      <title>A neuro-fuzzy and deep learning framework for accurate public transport demand forecasting: Leveraging spatial and temporal factors</title>
      <link>https://trid.trb.org/View/2534602</link>
      <description><![CDATA[Efficient public transportation requires innovative planning and operational strategies. Accurate demand forecasting is crucial, as it is influenced by complex, non-linear interactions of various spatial and temporal factors. This study proposes a neuro-fuzzy inference and deep learning models to predict public transport demand in Mashhad's traffic zones for enhanced operational planning. The model's flexibility allows the integration of diverse temporal and spatial variables. Four Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Long Short-Term Memory (LSTM) models developed with two datasets were evaluated and compared to each other. Datasets one and two contained all possible variables without pre-judging their impact, encompassing daily and yearly horizons, respectively. Datasets three and four employed the identified influential variables from previous datasets using the Random Forest algorithm, leading to faster processing and reduced error. Five statistical coefficients including MSE (Mean Squared Error), BIAS, R² (Coefficient of Determination), WI (Willmott Index) and NSE (Nash-Sutcliffe Efficiency were presented to evaluate the performance of the proposed models. The results showed that the LSTM neural network model in the short-term daily scale (MSE = 0.0006, BIAS = 0.9308, R² = 0.9047, WI = 0.7591, NSE = 0.9047) and the ANFIS model in the long-term annual scale (MSE = 0.0024, BIAS = 0.0229, R² = 0.9415, WI = 0.9730, NSE = 0.8738) achieved superior performance in predicting demand for bus and rail systems in Mashhad. This research's forecasting models enable planners to estimate public transport demand under varying utilization levels of urban uses in Mashhad, offering insights for both daily and annual horizons across different traffic zones.]]></description>
      <pubDate>Tue, 13 May 2025 09:54:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534602</guid>
    </item>
    <item>
      <title>A New Decision Tree Based on Intuitionistic Fuzzy Twin Support Vector Machines</title>
      <link>https://trid.trb.org/View/2487917</link>
      <description><![CDATA[Effectively classifying anomalies in a multi-class setting holds significant importance in domains such as medical datasets, fraud detection, and anomaly detection. This task presents challenges that include efficient training on large datasets, accurate classification in imbalanced scenarios, and sensitivity to high imbalance ratios (IR). This paper introduces a novel approach, the Intuitionistic Fuzzy Twin Support Vector Machine-based Decision Tree (NDT-IFTSVM), aimed at addressing these issues. NDT-IFTSVM integrates IFTSVM and decision tree methodologies, offering an efficient solution for multi-class classification. The proposed algorithm constructs a decision tree comprised of a series of two-class IFTSVMs. To enhance balance and separability, the multi-class method iteratively divides into two sets based on distance between class centres and instance distribution. This recursive process continues until each subset exclusively contains a single class, facilitating effective classification. To handle highly imbalanced datasets, NDT-IFTSVM incorporates a rational weighting strategy. Additionally, the authors refine NDT-IFTSVM by introducing a regularization term that maximizes the margin between the bounding and proximal hyperplanes, mitigating the impact of noise and outliers. Finally, a coordinate descent system with shrinking by an active set is applied to reduce the computational complexity. Numerical evaluations employ the bootstrap technique with a 95% confidence interval and statistical tests to quantify the significance of performance improvements. Experimental results on 12 datasets demonstrate the efficacy of the proposed method, showcasing promising outcomes compared to other techniques documented in the literature.]]></description>
      <pubDate>Wed, 30 Apr 2025 16:58:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487917</guid>
    </item>
    <item>
      <title>Planning of Electric Vehicle Charging Stations With PV and Energy Storage Using a Fuzzy Inference System</title>
      <link>https://trid.trb.org/View/2511807</link>
      <description><![CDATA[Electric vehicles (EVs) have emerged as a promising solution to reduce greenhouse gas emissions in urban areas. The construction of EV charging stations (EVCSs) is critical to the development of the EV industry. This article proposes a novel integrated fuzzy inference system (FIS)-based planning framework for determining the optimal locations and capacities of EVCSs with photovoltaic (PV) systems and energy storage units. Several off-site factors that will affect the planning results of EVCSs are analyzed and incorporated into a multiobjective optimization problem, aiming at minimizing the cost of electricity (COE) and emission pollutants simultaneously. The proposed FIS-based planning approach introduces novel fuzzy criteria that account for the nonlinear and difficult-to-model joint effect of social and environmental factors. By incorporating these off-site factors, a more realistic framework for EVCS planning is presented. Numerical studies are conducted on a coupled 33-bus distribution system and 25-bus transportation system to illustrate the proposed planning method. According to the simulation results, employing the proposed FIS-based planning framework not only reduces the search space and simplifies the optimization problem but also makes the results more realistic according to practical system conditions.]]></description>
      <pubDate>Thu, 24 Apr 2025 09:18:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511807</guid>
    </item>
    <item>
      <title>Fuzzy Approximation ARX Model-Based Intelligent Two-Horizon Robust FCS-MPC for Power Converter</title>
      <link>https://trid.trb.org/View/2511798</link>
      <description><![CDATA[A finite control-set model predictive control (FCS-MPC) strategy is widely recognized as an interesting research topic in both theoretical and practical architectures. One barrier to the widespread application of the FCS-MPC is its sensitivity to the accuracy of the system model. Notice that it is an underexplored issue on how to attenuate such a restriction. To this end, the authors continue this topic and focus on a novel FCS-MPC methodology subject to parametric uncertainty, which can be realized by incorporating a fuzzy approximation-based autoregressive with exogenous variable (ARX) model into an intelligent two-horizon robust FCS-MPC architecture. However, it introduces a prohibitively high-computational burden, which makes it unsuitable for online implementation. To remedy this, a supervised imitation learning technique, which is inspired by artificial intelligence (AI), is leveraged herein to approximate the developed controller as a black box, thus facilitating a feasible computational load. This modification is able to simultaneously mitigate the problems of model parametric uncertainties and increased online computational demand as well as weighting factor selection inherent in the existing approach, which ensures the optimized system performance with efficient online implementation and low switching frequency (SF) operation. Finally, remarkable performance and superiority for this proposal are experimentally confirmed for power converters.]]></description>
      <pubDate>Wed, 23 Apr 2025 11:54:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511798</guid>
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