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    <title>Transport Research International Documentation (TRID)</title>
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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>
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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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      <title>Towards automated Physical Internet system: Simulations of two privacy-protecting routing protocols</title>
      <link>https://trid.trb.org/View/2618113</link>
      <description><![CDATA[The purpose of this paper is to address the trust issue that leads to reluctance to share data within the logistics sector. This paper leverages the latest logistics paradigm concept Physical Internet (PI), and introduces two decentralised routing protocols for PI, focusing on their performance and impact on privacy by minimising data sharing. We use Agent-Based Modelling (ABM) and Monte Carlo (MC) simulations to evaluate the effectiveness of the protocols in optimising route quality, monetary costs and external costs in a realistic business setup on the Belgian scale. In addition, a sensitivity analysis was performed to assess the impact of response delays in a logistics network. Our research demonstrates the possibility of sharing less data without compromising the optimality of routes. We find that at our problem scale, trucks are the preferred mode when only considering monetary costs. Our findings also illustrate the significant impact of response delays and the handling capacity of intermodal hubs on the efficiency of route planning and the need for automation to improve PI systems’ reliability. We further suggest that trust issues should become one of the primary focuses for the current stage of PI research.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618113</guid>
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    <item>
      <title>The Simple Complexity of Reverse Engineering a Vehicle CAN Bus</title>
      <link>https://trid.trb.org/View/2691856</link>
      <description><![CDATA[Automotive Original Equipment Manufacturers (OEMs) closely guard information about their products due to the significant investment in vehicle research and development. However, advancing automotive innovation often requires insights from existing systems to improve safety, efficiency, and performance. The Controller Area Network (CAN) bus remains the industry standard for communication between electronic control units (ECUs), yet CAN message specifications are typically proprietary and undocumented. This paper presents a case study involving the reverse engineering of CAN messages from a 2024 Toyota Grand Highlander powertrain. By capturing and analyzing communication between a diagnostics tester and the vehicle’s ECUs and replicating the communication, substituting A CANcase and software in place of a diagnostics tester, we were able to reverse engineer the vehicle’s CAN bus, demonstrating a practical methodology for decoding and interpreting CAN traffic without prior access to proprietary data. The approach highlights both general principles and OEM-specific variations in message structure and encoding. The goal of this work is to support researchers and engineers in developing their own reverse engineering workflows. It illustrates that while the foundational techniques are consistent, adapting to vehicle-specific implementations is essential. The paper aims to provide a replicable process and to encourage further exploration in the field of automotive CAN analysis.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691856</guid>
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    <item>
      <title>1Q: First-Generation Wireless Systems Integrating Classical and Quantum Communication</title>
      <link>https://trid.trb.org/View/2672974</link>
      <description><![CDATA[We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical (FSO) links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUE), and hybrid resource allocation spanning classical time–frequency and quantum entanglement domains. Several application scenarios are discussed and illustrated through system design requirements for quantum key distribution (QKD), blind quantum computing (BQC), and distributed quantum sensing. A range of unique quantum constraints are identified, including decoherence timing, fidelity requirements, and the interplay between quantum and classical error probabilities. Protocol adaptations extend cellular connection management to incorporate entanglement generation, distribution, and handover procedures, expanding the quantum Internet (QI) to cellular wireless.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672974</guid>
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    <item>
      <title>Centrally Coordinated Routing of Freight in Smart Cities</title>
      <link>https://trid.trb.org/View/2581351</link>
      <description><![CDATA[The efficient routing and distribution of goods are vital to the survival and sustainability of any smart city. Currently, different truck companies route their trucks on a road network that is often congested and unbalanced in time and space with respect to traffic load distribution. The lack of coordination among truck operators often leads to long waiting times at pick-up and drop-off points and/or along popular routes that trucks usually follow due to overlaps. Due to connectivity and the Internet of Things new opportunities open for centrally coordinated solutions in order to achieve better load balancing in space and time for trucks across the road network. In this chapter, we present a centrally coordinated approach for routing freight in urban environments where truck traffic loads are balanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. The electric trucks add the constraint of charging time, charging station locations and driving range that is less than that of diesel. The routing problem is formulated as an optimization problem with several constraints. A co-simulation load-balancing approach is used to generate routes for trucks that reduce the overall cost. The co-simulation approach employs a traffic simulator in a feedback loop that replaces simple mathematical models often used to predict the states of the network. The simulation model captures the complexity and dynamics of the network and generates predicted states that are used to generate optimum routes and achieve load balancing. Numerical experiments are performed using a traffic simulator of the Los Angeles/Long Beach Metropolitan road traffic network that includes two major ports. The simulation results demonstrate strong potential for the proposed centralized truck routing system to reduce the impact of trucks on traffic and make their routing more efficient.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581351</guid>
    </item>
    <item>
      <title>Integrating Machine Learning and IoT: Pioneering Solutions for Sustainable Smart Cities</title>
      <link>https://trid.trb.org/View/2581760</link>
      <description><![CDATA[Smart cities are changing due to machine learning (ML) and the Internet of Things (IoT), advancing liability, sustainability, and increased efficiency. They maximise urban services, from public safety and environmental monitoring to transportation and energy management, using machine learning algorithms and interconnected IoT devices. Through predictive maintenance, anomaly detection, and sophisticated analytics, the integration of ML expands the possibilities of IoT systems. By detecting and notifying authorities of odd activity or possible security risks, incorporating surveillance systems with ML can improve crime prevention and response. ML algorithms examine this data to enhance efforts to create a healthier and cleaner urban environment by offering insights into pollution trends and health hazards. Significant infrastructure and technological investments are also required to deploy and keep these cutting-edge systems. Government agencies, stakeholders in the corporate sector, and society must work together effectively to address these issues and guarantee data’s secure and moral use. In summary, the development of smart cities depends on the fusion of machine learning and the IoT.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581760</guid>
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    <item>
      <title>IoT-Driven Machine Learning Solutions for Smarter Urban Living</title>
      <link>https://trid.trb.org/View/2581701</link>
      <description><![CDATA[The impact of IoT and ML on smart cities from a city infrastructure development perspective has converged, and data processing, decision-making, and collection have never been more efficient. IoT Sensors are essential for collecting information on environmental parameters, energy consumption, traffic patterns, and public safety. This massive amount of data can provide valuable insights using ML algorithms to predict patterns and enhance local governance. This also aids in projects like waste preservation and traffic provisioning, which work towards efficient energy utilisation within the urbanised spots. Success would require addressing scalability, interoperability, privacy, and data security issues.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581701</guid>
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    <item>
      <title>Applications of Computational Learning and IoT in Smart Road Transportation System</title>
      <link>https://trid.trb.org/View/2579094</link>
      <description><![CDATA[This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.  Simi]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579094</guid>
    </item>
    <item>
      <title>Harnessing IoT and Machine Learning for Sustainable, Smart Urban Environments</title>
      <link>https://trid.trb.org/View/2579098</link>
      <description><![CDATA[Smart cities are a concept reshaping how urban environments operate to be more efficient, sustainable, and liveable, especially when machine learning (ML) meets the Internet of Things (IoT). This chapter delves into how these two technologies converging—with urban data being delivered in real-time from IoT and analytics taking place via ML algorithms—can create actionable insights. In this paper, we critically analyse the use cases of most importance, including benefits and challenges, alongside emerging trends from a recent survey that provides an in-depth depiction of what is feasible to create a smart city landscape thanks to IoTs and ML. In this podcast, the speakers will discuss how these disruptive technologies can address challenges related to urbanisation—from energy management, public safety and getting around a congested city to environmental sustainability.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579098</guid>
    </item>
    <item>
      <title>Optimisation Strategies for Next-Generation AI, ML, and IoT Applications</title>
      <link>https://trid.trb.org/View/2579097</link>
      <description><![CDATA[Optimization is a fundamental factor that drives the efficiency, effectiveness, and precision of structures in synthetic intelligence, device learning, and the Internet of Things (IoT). Employing optimization techniques, including refining fashions with large datasets or improving supply code, can considerably improve performance, accuracy, and dependability. This bankruptcy explores key optimization standards and their essential roles throughout those interconnected fields. We study a spectrum of optimization techniques, starting from conventional strategies like gradient descent to extra superior techniques including evolutionary algorithms and Particle Swarm Optimization (PSO). In Artificial Intelligence (AI), optimization is essential for boosting the decision-making and problem-fixing skills of algorithms. In Machine Learning (ML), it's far vital for reaching excessive prediction accuracy, fine-tuning hyperparameters, and correctly education fashions. In IoT, optimization is essential to control electricity usage, enhance community performance, and decorate real-time processing. The goal of this bankruptcy is to offer researchers, professionals, and college students with an intensive know-how of the importance of optimization in AI, ML, and IoT, allowing them to increase their paintings in those areas.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579097</guid>
    </item>
    <item>
      <title>Speed Breaker and Vehicle Accident Detection with Alert Sensors</title>
      <link>https://trid.trb.org/View/2579096</link>
      <description><![CDATA[As nations become economically stronger and financially capable, more people own their vehicles. Although the road infrastructure has improved, it must cope with the increasing population. With that, more and more road accidents are increasing. Drivers often can’t recognise the appearance of unmarked speed breakers and lose control of the vehicle, causing serious accidents and loss of lives. In many instances, fatalities occur because immediate medical assistance is not readily available due to the absence of a reliable system. With the advancement of technologies like IoT, a pressing need arises to create a system capable of promptly informing relevant authorities with comprehensive data on the incidence of a road accident. The proposed IoT-based speed breaker detection system consists of strategically deployed sensors along roadways equipped with IoT capabilities to detect the presence of vehicles and analyse the vehicle's behaviour to identify potential speed breaker crossings. Upon detection, the system sends real-time alerts to drivers, either through in-vehicle displays, mobile applications, or roadside signage, enabling them to adjust their speed and driving behaviour accordingly. Key components of the system include sensor nodes equipped with accelerometers or distance sensors to detect changes in road surface elevation indicative of speed breakers. These sensor nodes communicate wirelessly with a centralised control system, which processes the collected data and triggers alerts when speed breakers are detected. Along with this, we have incorporated machine learning methods and image processing to accurately identify a road accident. The sensors like accelerometer, Distance sensors, camera, etc., provide data to a microprocessor which matches the sensor data with the machine learning model and determines if there is an accident or not, and if it is, the device sends the related metrics to the server through the internet. Once the data reaches a server, it determines the nearest hospitals, and police stations by looking at the GPS data and sends a notification to them and the registered phone number by the user. The system also incorporates real-time tracking of driver, vehicle, and timing information for speed breaker rule violations, which becomes a life-saving technology.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579096</guid>
    </item>
    <item>
      <title>Protocol-Based Fusion Estimator for Motion State of Surrounding Vehicles Under Connected Environment</title>
      <link>https://trid.trb.org/View/2617739</link>
      <description><![CDATA[Accurately obtaining motion states of surrounding vehicles (SVs) plays a pivotal role in achieving the safety and closed-loop optimality of intelligent vehicles (IVs) for motion control, where the connected environment serves as the hardware foundation. To mitigate data collisions and alleviate communication burdens, this paper introduces a novel protocol-based fusion estimator (PBFE) for estimating the motion states of SVs. Based on the time-varying nonlinear system models, RRP-based cubature Kalman filter (CKF) and WTODP-based CKF are designed, which embed communication protocols, i.e., round-robin protocol (RRP) and weighted try-once-discard protocol (WTODP), into the variable-structure CKF framework. Then, mathematical definitions and descriptions of RRP and WTODP are provided, which are utilized to adjust the data transmission mechanism from sensors to estimators, leading to the establishment of a novel protocol-based measurement model. Subsequently, to preemptively quantify the performance impact of communication protocols on PBFE from a theoretical perspective, the boundedness analysis of the estimation error is rigorously derived. Conclusively, virtual simulations (VSs) based on high-fidelity models from CarSim and Matlab/Simulink, covering diverse real-world driving scenarios, are conducted to compare the protocol-based CKF method with the protocol-based unscented Kalman filter (UKF) method. Furthermore, the robustness and stability of the proposed approach are verified through practical on-road tests (ORTs).]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617739</guid>
    </item>
    <item>
      <title>Microservice-Aware Deployment and Swarm Intelligence Cooperative Routing in Vehicle Edge Computing</title>
      <link>https://trid.trb.org/View/2658941</link>
      <description><![CDATA[The integration of vehicle edge computing (VEC) and microservice architectures improves real-time data processing and computational optimization in the Internet of Vehicles. Specifically, in high-traffic areas, the dynamic deployment and request routing of microservices with complex data dependencies within vehicle clusters can effectively reduce the computational load on edge devices. However, existing research has primarily focused on efficiently utilizing vehicular resources, overlooking the dynamic nature of vehicular cluster networks and the additional communication costs arising from data dependencies between microservices. Therefore, we propose a joint service deployment and request routing problem for vehicle collaboration. We first design a vehicle-road collaborative service framework assisted by temporary vehicle workers, expanding available resources and coverage by deploying microservice instances on selected temporary vehicle nodes. Second, recognizing the dependency between service deployment and request routing, we propose a dual-timescale service-deployment and request-routing policy. On a long timescale, a microservice-aware deployment method optimizes request selection and response time. On a short timescale, we propose a decentralized, swarm intelligence-based collaborative request routing method that constructs a response threshold model through agent interaction, thereby enhancing the collaborative optimization capability of the system. Finally, experimental results using real datasets show that our method outperforms other approaches in reducing request response time when communication costs are taken into account.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658941</guid>
    </item>
    <item>
      <title>Efficient Vehicle Secure Scheduling and Access Authentication Scheme for 5G-Integrated Emergency Rescue Scenario</title>
      <link>https://trid.trb.org/View/2658926</link>
      <description><![CDATA[With urban population density on the rise, emergency incidents are increasing in both frequency and complexity, placing growing pressure on existing rescue systems. Meanwhile, issues such as slow response times, inadequate coordination mechanisms, and inefficient information exchange further exacerbate the challenges faced by these systems. The integration of Vehicle-to-Everything (V2X) communication and 5G technology offers unprecedented capabilities, such as ultra-low latency and high data throughput, which are critical for real-time coordination and decision-making in emergency rescue scenarios. To establish secure and efficient vehicle communication in 5G and V2X-enabled emergency rescue scenarios, we propose an efficient vehicle secure scheduling and access authentication scheme based on certificateless cryptography and multireceiver signcryption. In this scheme, the command and control center can securely dispatch rescue fleets based on disaster conditions. By enabling mutual authentication and key agreement between rescue vehicles and roadside units, the scheme ensures the reliable and swift exchange of rescue information and instructions. In addition, to address unexpected situations such as traffic congestion, we design a route-switching mechanism. Furthermore, in order to mitigate potential malicious behavior, a vehicle legitimacy revocation mechanism is implemented to ensure the normal operation of the system. The security of the scheme is verified through formal analysis and informal analysis. Performance analysis demonstrates that the scheme offers significant advantages over existing ones in terms of signaling overhead, communication overhead, computational overhead, and energy efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658926</guid>
    </item>
    <item>
      <title>Decentralized Data Integrity Auditing in Vehicular Cloud Computing</title>
      <link>https://trid.trb.org/View/2658921</link>
      <description><![CDATA[As Vehicular Cloud Computing (VCC) evolves, ensuring data integrity and availability becomes a critical challenge due to the vast amount of data being shared and stored. These properties are vital for preserving confidence in cloud services, guaranteeing that data is kept intact and readily available when needed. Traditional data auditing mechanisms, such as Proofs of Retrievability (PoR) and Provable Data Possession (PDP), are effective but often rely on centralized models that pose risks like single-point failures and susceptibility to collusion. To mitigate these risks, we introduce a blockchain-assisted protocol that leverages the decentralized and tamper-proof characteristics of blockchain to enhance the security of data auditing in VCC. Our approach incorporates a dynamic key update mechanism to counter key exposure issues prevalent in VCC and introduces a multi-replica mechanism to ensure data redundancy and reliability across different storage nodes. This feature significantly reduces the risk of data loss and improves trust in cloud services by distributing data storage responsibilities and preventing single-point failures. We conduct formal security analysis and implement a prototype of our protocol on the Ethereum blockchain. Experimental evaluations on both Ganache and Sepolia testnets validate its feasibility in decentralized environments. The results demonstrate that our scheme supports stable challenge-response latency, moderate gas consumption, and reliable multi-replica consistency—making it well-suited for VCC deployments with dynamic conditions and limited resources.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658921</guid>
    </item>
    <item>
      <title>Connected Cruise Control With Automotive Passive Optical Network Communication Limited to Cross-Vehicle Loop Delays</title>
      <link>https://trid.trb.org/View/2658915</link>
      <description><![CDATA[Connected cruise control (CCC) is an advanced system that extends traditional cruise control by incorporating vehicle-to-everything (V2X) communications. However, as such technologies continue to iterate, conventional intra-vehicle communication systems struggle to meet escalating bandwidth demands, while cross-vehicle communication delays degrade CCC’s closed-loop performance and increase collision risks. Motivated by these issues, a zone-centralized architecture integrating automotive passive optical networks and cellular V2X is proposed to reduce the upper bounds of loop delays compared to those in Ethernet-based domain-centralized systems. A novel cross-vehicle loop delay analysis framework is introduced to characterize communication system uncertainties with provable upper bounds. Building on this, a scheduling and control coordination scheme is developed to mitigate the cross-vehicle loop delays in intra-vehicle and inter-vehicle communications, while ensuring precise tracking platoon motion. For scheduling, a fraction-type basic period methodology and an earliest-deadline-priority strategy are employed to manage deterministic communications and alleviate the cross-vehicle loop delays. In terms of control, a delay-robust model predictive control approach is applied for decision-making and an H ∞ -based linear quadratic regulator technique is used for vehicle longitudinal acceleration tracking while combating vehicle communication delays. Finally, the proposed scheduling and control coordination scheme undergoes validation across diverse driving scenarios, demonstrating its effectiveness and robustness through hardware-in-the-loop verification.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658915</guid>
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