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    <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" />
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    <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>
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    <item>
      <title>Multi-Objective Reinforcement Learning With Physics-Aware Vehicle Dynamics for Safe and Efficient Adaptive Cruise Control</title>
      <link>https://trid.trb.org/View/2703769</link>
      <description><![CDATA[Adaptive Cruise Control (ACC) enhances safety and comfort in autonomous vehicles by maintaining appropriate inter-vehicular distance and speed regulation. Traditional ACC systems based on PID or Model Predictive Control (MPC) often struggle to handle complex, unforeseen traffic scenarios such as sudden braking, pedestrian crossings, or lane changes. Reinforcement Learning (RL) offers an adaptive alternative by enabling policy learning through environment interaction. However, existing RL-based ACC methods frequently suffer from poor smoothness and energy inefficiency under emergency conditions. This work proposes an enhanced RL-based ACC framework that integrates a physics-informed, multi-objective reward function to jointly optimize safety, ride comfort, and energy efficiency. The reward components are normalized and dynamically weighted based on the current driving context, allowing the agent to adaptively prioritize objectives. Vehicular dynamics are explicitly incorporated into the learning process to improve real-world applicability. The system is trained using the DDPG algorithm, which supports continuous control and stable policy convergence. Extensive MATLAB-based simulations were conducted across diverse urban driving scenarios including stop–go traffic, traffic signals, lane changes, and pedestrian interactions. Comparative analysis against PID and MPC-based ACC controllers demonstrates that the proposed framework achieves superior performance in maintaining safe inter-vehicular distance, reducing jerk, and improving energy efficiency. This study validates the feasibility of deploying a computationally efficient, model-free RL-based ACC for robust and safe autonomous driving in dynamic traffic environments.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703769</guid>
    </item>
    <item>
      <title>Research on One-Way Multi-Lane Overtaking Based Warning System</title>
      <link>https://trid.trb.org/View/2732261</link>
      <description><![CDATA[In order to improve the driving safety of one-way multi-lane overtaking behavior, this paper designs and validates an overtaking warning system. By analyzing the dynamic characteristics and potential risks of the overtaking process, the prediction model of overtaking time and target lane safety gap is constructed, key parameters such as safety time distance and vehicle parameters are introduced, and three levels of danger levels and corresponding warning strategies are set. MATLAB simulation is used to verify the design of three types of typical overtaking scenarios (safe, cautious, and dangerous), and the test results show that the system can effectively differentiate the risk levels and output the warning consistent with the expectation, which verifies the reasonableness of the model and strategy.]]></description>
      <pubDate>Sun, 02 Aug 2026 17:23:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732261</guid>
    </item>
    <item>
      <title>Lightweight design of automotive parts based on the FPTO method</title>
      <link>https://trid.trb.org/View/2698262</link>
      <description><![CDATA[Topology optimisation is a method to maximise or minimise an objective function by optimising material distribution under design constraints. In the theoretical research of topology optimisation, most algorithms are developed on regular geometric models in software such as MATLAB. However, applying these findings directly to complex structures with irregular geometries in engineering is challenging. Optimisation of such structures relies on commercial software using density-based methods, hindering open algorithm research. The floating projection topology optimisation (FPTO) is a stable, efficient method producing good results. This study introduces the FPTO principles and investigates its integration on the MATLAB-ABAQUS platform, including conducting analysis in ABAQUS, performing optimisation solution and result visualisation in MATLAB, and facilitating data exchange. The boundaries of the topology structure are smoothed to better meet actual engineering requirements. This research explores the application of FPTO to automotive components, achieving lightweight designs for wheel hubs and control arms, offering an effective engineering solution.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698262</guid>
    </item>
    <item>
      <title>The influence of different worn wheel profiles on vehicle dynamic performance and wear under traction conditions</title>
      <link>https://trid.trb.org/View/2691047</link>
      <description><![CDATA[To investigate the impact of worn wheel profiles on wheel-rail contact and vehicle dynamic performance under traction conditions, this study tracked and tested the wheel profiles of a subway vehicle with operational mileages of 0 km, 50,000 km, 80,000 km, and 140,000 km. The influence of different worn wheel profiles on wheel-rail contact characteristics under traction conditions was then analyzed. By integrating a vehicle dynamics model, a traction and resistance calculation model, and a wheel wear and fatigue damage model, a dynamics co-simulation model was developed in SIMPACK and MATLAB. Two traction characteristics were set: both with a starting torque of 1000 N・m and respective speeds of 70 km/h and 80 km/h at the end of the constant power section. The effects of four worn wheel profiles on vehicle dynamics, wheel wear, and rolling contact fatigue (RCF) under traction conditions were thoroughly studied. The results show that as the operation mileage increases, the wheel profile changes, leading to a deterioration in the wheel-rail contact relation. This results in an upward trend in the stability index, wheel-rail lateral/vertical forces, derailment coefficient, and wheel unloading rate. Additionally, the vibration acceleration of the vehicle body and frame gradually increases. The wheel wear range expands gradually, with wear depth and area initially decreasing and then increasing, while the surface fatigue index (SFI) first rises and then falls. The 80,000 km profile emerges as the optimal choice for wheel re-profiling based on a comprehensive evaluation of dynamic performance, wear, and fatigue. This study provides a scientific basis for formulating wheel maintenance strategies.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691047</guid>
    </item>
    <item>
      <title>Collapse behaviour of masonry arch bridges strengthened with an external post-tensioning system</title>
      <link>https://trid.trb.org/View/2681625</link>
      <description><![CDATA[Masonry arch bridges represent a significant portion of existing road and rail infrastructure and often require strengthening due to material degradation and structural damage. This paper evaluates the effects of an external steel cable post-tensioning system on the collapse behaviour of masonry arch bridges. A nonlinear kinematic analysis is employed to model and analyse the strengthened structure, accounting for the finite compressive strength of the masonry, the ultimate strength of the post-tensioning system and the possible failure modes. The mechanical model, developed in MATLAB, is validated against experimental tests conducted on a quasi-semicircular masonry arch model, representative of typical Italian railway bridges, which was strengthened with intrados-applied post-tensioning cables. Results highlight a significant improvement in load-bearing capacity and variations in collapse mechanisms induced by the strengthening system. Furthermore, this study introduces the vertical load-displacement capacity curve of a post-tensioned masonry bridge, providing new insights into its structural behaviour.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681625</guid>
    </item>
    <item>
      <title>A range-based method for identifying optimal foaming conditions of mechanically foamed asphalt</title>
      <link>https://trid.trb.org/View/2680353</link>
      <description><![CDATA[Mechanical foaming of asphalt can effectively reduce mixing and compaction temperatures, thereby ensuring the superior road performance of the resulting asphalt mixtures. However, current indicators for evaluating asphalt foaming performance are largely singular and fixed, failing to reflect the variable temperature, water content, and equipment conditions in actual asphalt plants. Similarly, existing methods for determining optimal foaming conditions rely on laboratory-fixed settings or single metrics, limiting their applicability and reliability for practical field production. In this study, the asphalt foam collapse test (AFCT) was conducted on 70# asphalt, 90# asphalt, and SBS-modified asphalt using a laser sensor system. Based on the four evaluation indicators—expansion ratio (ER), half-life (HL), foam index (FI), and surface area index (SAI)—the foaming performance of three types of asphalt was comprehensively analyzed. Subsequently, the effects of varying water content and foaming temperature on these two indicators were investigated. Unlike the monotonic trends observed in expansion ratio and half-life, both FI and SAI exhibited peak values under specific conditions. Building upon this analysis, interpolation and simulation methods using MATLAB were employed to develop an optimization approach for determining the optimal foaming conditions. Furthermore, the rationality of the proposed optimization method was verified through workability and coating tests of foamed asphalt mixtures. The results demonstrate that the proposed method yields a range of optimal foaming temperatures and water contents rather than a single fixed value, making it more consistent with practical engineering applications.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680353</guid>
    </item>
    <item>
      <title>Simulation of DoS and jamming attacks on railway control systems over TCP/IP and radio frequency</title>
      <link>https://trid.trb.org/View/2658111</link>
      <description><![CDATA[We present a simulation framework for analysing cyber-attacks on intelligent railway control systems, based on threat modelling. The system simulates a TCP/IP communication link between a control centre (server) and a train unit (client), transmitting real-time operational data such as speed and signal status. The simulation includes: (1) train behaviour modelling using OpenRails and MATLAB, (2) control centre logic in MATLAB, and (3) network communication and attack emulation in real-time. Two attack scenarios are evaluated: DoS attack that floods the server with fake requests, and a Jamming attack that injects noise into the FR medium. Performance metrics such as packet loss, latency, and response time are recorded under normal, attack, and recovery phases using MATLAB Simulink. Results show increased delays, packet loss, and reduced system reliability during attacks. Telemetry data and visualisations highlight system degradation and recovery behaviour. The framework offers a practical model for simulating real-world cyber threats against railway communication systems.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658111</guid>
    </item>
    <item>
      <title>Harnessing expanded polystyrene waste for sustainable construction: NBO-HDLNN approach</title>
      <link>https://trid.trb.org/View/2643494</link>
      <description><![CDATA[The growing demand for sustainable construction materials requires incorporating industrial by-products like expanded polystyrene (EPS) in self-compacting concrete (SCC), while accurately predicting its strength and durability remains a challenge. This manuscript proposes a hybrid technique for predicting the strength and durability of SCC with EPS waste. The novelty lies in developing a hybrid model that combines a Hierarchical Deep Learning Neural Network (HDLNN) with a Namib beetle optimisation (NBO). Hence it is named as an HDLNN- NBO approach; the HDLNN predicts the compressive and flexural strength properties of SCC, while NBO optimises the weight parameters to enhance prediction accuracy. The goal of the work is to improve concrete performance and promote the sustainability and workability of SCC by incorporating EPS waste. Implemented in the MATLAB platform, the proposed approach achieves a compressive strength of 50 MPa, a flexural strength of 13 MPa and a prediction accuracy of 97%, outperforming existing methods such as Artificial Neural Networks (ANN), Recalling Enhanced Recurrent Neural Networks (RERNN) and Random Forest Algorithms (RFA). The potential impact of this research is significant as it provides a reliable tool for engineers and researchers to optimise concrete mixtures, resulting in improved mechanical properties, reduced environmental impact and cost savings in construction.]]></description>
      <pubDate>Sun, 22 Feb 2026 17:36:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643494</guid>
    </item>
    <item>
      <title>Investigating the impact of various parameters on the properties of fly ash geopolymer concrete using a hybrid optimisation approach</title>
      <link>https://trid.trb.org/View/2643495</link>
      <description><![CDATA[This paper proposes an efficient hybrid method for analyzing the effect of alkaline solutions on the properties of fly ash-based geopolymer concrete. The proposed technique, BOA-MPNN, combines the Butterfly Optimization Algorithm (BOA) and Multilayer Perceptron Neural Network (MPNN). The objective is to evaluate how different curing methods (ambient and oven curing) influence the physical, mechanical, and microstructural properties of geopolymer concrete while reducing its setting time. BOA optimizes the parameters of geopolymer concrete, while MPNN predicts its strength. The proposed model is implemented in MATLAB and compared with existing techniques. The results indicate that the BOA-MPNN technique outperforms the Salp Swarm Algorithm (SSA), Color Harmony Algorithm (CHA), and Gannet Optimization Algorithm (GOA). The proposed approach achieves a setting time of 26 min, significantly lower than SSA (30 min), CHA (35 min), and GOA (40 min). Moreover, the mixture attains a compressive strength of 35 MPa, exceeding FA-GGBFS-GPC (30 MPa), AAH-GPC (25 MPa), and CH-GPC (22 MPa). These findings confirm that the proposed technique delivers superior performance compared to existing methods, making it a promising approach for enhancing geopolymer concrete properties.]]></description>
      <pubDate>Sun, 22 Feb 2026 17:36:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643495</guid>
    </item>
    <item>
      <title>A Stable and Secure Clustering Methodology for Vehicles in VANET</title>
      <link>https://trid.trb.org/View/2642289</link>
      <description><![CDATA[In the rapidly growing field of smart cities and intelligent transportation systems (ITS), Vehicle Ad-hoc Networks (VANETs) play a key role in enabling seamless interaction between vehicles, infrastructure, and people. To support this, stable and secure clustering methods are essential for ensuring reliable communication within VANETs. However, maintaining stable and secure connectivity in such dynamic networks remains a significant challenge. This paper presents a stable and secure clustering methodology, DNN-CCAS (Deep Neural Network–Canonical Correlation Analysis Scheme), designed to enhance both cluster stability and security in VANET environments. The proposed approach involves three key phases: cluster formation using an improved K-consonance technique, cluster head selection based on a linear metric, and secure data transmission validated by a deep learning model. Simulation experiments conducted using MATLAB, SUMO, and OMNeT++ indicate that the proposed approach attains an accuracy of 92.42%. Furthermore, it consistently surpasses existing methods with respect to clustering efficiency and communication security. These findings indicate the potential of DNN-CCAS to support robust and reliable vehicular communications in future smart transportation systems.]]></description>
      <pubDate>Wed, 18 Feb 2026 08:51:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642289</guid>
    </item>
    <item>
      <title>Robust Adaptive Fault-Tolerant Control for MAGLEV Train Systems: A Nonsingular Finite-Time Approach</title>
      <link>https://trid.trb.org/View/2511589</link>
      <description><![CDATA[The magnetic levitation (MAGLEV) train faces challenging conditions, including actuator faults, high-bandwidth disturbances, and parametric uncertainties. This study presents a nonsingular fast terminal sliding mode controller renowned for its robustness and rapid convergence. The barrier function technique is employed to mitigate the adverse effects of high-bandwidth disturbances and to regulate energy consumption. The proposed controller utilizes a hyperbolic tangent function at the sliding surface (SS) and control input to alleviate issues caused by the sign function. Hardware results using the OPAL-RT system and MATLAB simulations authenticate the efficacy of the proposed controller in mitigating actuator faults and high-bandwidth disturbances, enabling the system to accurately follow defined paths with high convergence speed and minimal chattering. In addition, the study compares the performance of this controller with two other methods and presents the comparative results. In conclusion, the nonsingular adaptive barrier fast terminal sliding mode control (SMC), incorporating the hyperbolic tangent function, performs optimally in addressing issues related to actuator faults and high-bandwidth disturbances in the MAGLEV train system.]]></description>
      <pubDate>Sun, 18 Jan 2026 16:49:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511589</guid>
    </item>
    <item>
      <title>Optimal control of a four-wheel steering car using state-dependent Riccati equation controller</title>
      <link>https://trid.trb.org/View/2610997</link>
      <description><![CDATA[Optimal control of a four-wheel steering car model is performed in this paper using two strategies: linear quadratic regulator (LQR) and state-dependent Riccati equation (SDRE). In such vehicles, the system is over-constrained, so the solution of inverse dynamics is not unique. Extracting the optimal control input satisfies the desired vehicle performance while minimising a specific objective function. The bicycle model with two steering inputs is used to model the four-wheel steering car and its nonlinear behaviour. A new approach for kinematic modelling is also proposed. Two closed-loop optimal control strategies are applied: one linear and valid near its operating point, and the other global and nonlinear. Their results are analysed and compared. Model correctness is investigated via comparison with CarSim. The effectiveness of the proposed optimal control strategies and the superiority of the nonlinear SDRE over traditional methods are validated using MATLAB simulations, showing accurate path tracking with minimal energy consumption across the its full workspace.]]></description>
      <pubDate>Mon, 12 Jan 2026 09:26:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610997</guid>
    </item>
    <item>
      <title>Smart traffic distribution in a coexistence environment of conventional and connected automated vehicles</title>
      <link>https://trid.trb.org/View/2572611</link>
      <description><![CDATA[As automated driving technology advances, its coexistence with conventional vehicles poses challenges in managing road conflicts and considering the impact of other drivers’ decisions on network performance, especially concerning environmental factors. With such concerns in mind, this paper explores the impacts associated with smarter traffic distribution in an interurban corridor under the coexistence of automated and conventional vehicles. The minimisation of travel time and reduction of external costs associated with pollutant emissions are the main goals of proposed traffic distribution. Vehicles on the road network are classified based on driving type (automated or conventional) and propulsion type (electric, diesel, gasoline, or hybrid electric). Sequential simulations using PTV Vissim, COPERT, and MATLAB and SciPy library in Python are conducted to evaluate the effects of automated electric vehicle (AEVs) introduction in different road types (highway and urban/rural). Results show the influence of Automated Electric Vehicles (AEVs) on traffic performance varies depending on route characteristics. The implementation of intelligent traffic distribution plays a crucial role in reducing emissions costs making it possible to reduce externalities related to emission costs by up to 21 per cent. However, there is a trade-off between minimising GHG and local pollutants.]]></description>
      <pubDate>Wed, 31 Dec 2025 10:55:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572611</guid>
    </item>
    <item>
      <title>Cooperative control method for connected and automated vehicle platoon based on arbitrary time headway switched system</title>
      <link>https://trid.trb.org/View/2604682</link>
      <description><![CDATA[The cooperative control of connected and automated vehicle (CAV) platoons plays a significant role in enhancing traffic efficiency and ensuring safety. In complex traffic environments, platoon dynamics frequently change due to external disturbances and varying traffic demands. These changes manifest as acceleration and deceleration, speed variations, and headway adjustments. This paper proposes a cooperative control method to manage the performance and stability of the platoon during state changes. Based on optimal control theory and arbitrary time headway (ATH) policy, a linear quadratic regulator (LQR) is constructed with safety, efficiency, and stability as objectives. The controller enables all vehicles in the platoon to maintain the desired time headway and synchronize their movements. To accommodate varying headway requirements, the model is further extended to a switched system, allowing stable transitions between arbitrary desired time headways. The stability of the optimal control and switched system is proved using transfer function and Lyapunov methods. Considering the inaccuracies in lower-level response, an active disturbance rejection controller (ADRC) is designed to refine control inputs and mitigate tracking errors. To validate the model in practical applications, simulation experiments incorporating vehicle dynamics are conducted using CarSim and MATLAB. Simulation results indicate that under the proposed control model, followers in the platoon respond more rapidly and safely to changes in the leader’s state. During transitions between arbitrary desired time headways, the platoon reconfigures more efficiently while ensuring improved safety and stability.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604682</guid>
    </item>
    <item>
      <title>Artificial Neural Network for Improving the Motion Control Quality of Underwater Remotely Operated Vehicle</title>
      <link>https://trid.trb.org/View/2610732</link>
      <description><![CDATA[The Remotely Operated Vehicle (ROV) plays a crucial role in underwater surveys but faces challenges in complex environments with unmeasured variables, leading to inefficiencies in navigation. This paper develops a motion control solution for ROVs that utilizes Artificial Neural Networks (ANNs) to enhance control quality in response to environmental impacts. Firstly, the authors develop an Adaptive Fuzzy Control (AFC) to control ROV movements under varying environmental conditions, allowing for the collection of operational datasets. Next, the ROV motion controller is designed based on the ANN architecture to enhance control quality. Finally, experimental scenarios using MATLAB demonstrated that ANNs markedly improve control accuracy and overall performance for ROVs when following predetermined trajectories. This emphasizes their substantial potential in advancing autonomous marine applications.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610732</guid>
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