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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Optimizing Spectrum Sensing in Cognitive Geo- Leo Satellite Networks: Overcoming Challenges for Effective Spectrum Utilization</title>
      <link>https://trid.trb.org/View/2601448</link>
      <description><![CDATA[The rapid growth of wireless and satellite technologies has increased the demand for efficient use of the electromagnetic spectrum. Cognitive satellite networks offer a promising solution, with spectrum sensing being crucial for the optimal utilization of radio-frequency (RF) resources. However, in cognitive satellite networks, characterized by excessive propagation delays, the effectiveness of spectrum sensing depends on the strategic placement of sensing nodes. This article represents the first endeavor in the literature to investigate optimal spectrum-sensing locations considering propagation delay for effective spectrum utilization in cognitive satellite networks. We begin by examining the geostationary (GEO) satellite downlink spectrum availability for cognitive technology through observations of the Inmarsat-4 F1 satellite downlink activities. We then analyze the impact of propagation delays on spectrum sensing in cognitive dual-satellite networks (CDSNs), where low-Earth orbit (LEO) satellites share the spectrum allocated to GEO satellites. Additionally, we address a critical design consideration: tracking GEO frequencies against Doppler shifts at LEO satellites, which significantly affects the effectiveness of spectrum sensing by LEO satellites. Finally, we explore future research directions in spectrum sensing within cognitive GEO-LEO satellite networks.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601448</guid>
    </item>
    <item>
      <title>A new authentication model for vehicular ad hoc networks with optimisation strategy</title>
      <link>https://trid.trb.org/View/2598691</link>
      <description><![CDATA[Vehicular Ad Hoc Networks (VANET) enhance road safety by distributing messages amongst vehicles. The effective method of serving vehicles devoid of choosing a path between destination and source is periodic broadcasting. However, network performance suffers issues with hidden nodes and broadcasting storms. The new authentication model for VANET involves System Initialisation, RSU registration phase, Vehicle registration phase, Authentication phase, Vehicle joining and leaving phase. Initially, cryptographic hash function is used in the suggested approach. Specifically, the proposed authentication message verification stage is conducted by signcryption with ECC-based model for encryption, which has lower computational overhead and less bandwidth consumption. During encryption, generated key is optimally tuned via Hybrid Bald Eagle Shark Smell Algorithm (HBE-SSA), which means the network can operate for more extended periods. The overall result demonstrates that the HBE-SSA achieved better KPA attack value about 0.11322 compared to existing algorithms BES, SSO, DOA and HBA, respectively.]]></description>
      <pubDate>Tue, 25 Nov 2025 15:01:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598691</guid>
    </item>
    <item>
      <title>A two-round broadcasting matching mechanism in ride-sourcing markets: Implication and optimization</title>
      <link>https://trid.trb.org/View/2594529</link>
      <description><![CDATA[The rapid growth of the ride-sourcing market has intensified competition, with driver–passenger matching strategies playing a pivotal role in platform performance. To attract more supply resources, some platforms adopt an order-broadcasting matching mechanism with a fixed broadcasting radius. Although this mechanism provides drivers more flexibility, it often struggles to balance matching probability and passenger wait times, leading to inefficiencies. In this regard, we propose an adaptive two-round broadcasting matching mechanism that dynamically adjusts the search radius based on drivers’ responses. This mechanism attempts to improve both the driver’s flexibility and the passenger experience but introduces new challenges in determining optimal configurations over multiple matching rounds. To tackle these challenges, we characterize the matching process and general results of the two-round broadcasting mechanism, particularly incorporating the heterogeneous acceptance behaviors of drivers and passengers under this special scheme. These models enable us to explore the impact of decision variables (i.e., radius in each round and time intervals) on system performance, such as matching probability and expected waiting and pickup time. Moreover, we derive the conditions which our proposed mechanism outperforms the traditional fixed-radius one. Our numerical results show that the platform can achieve better matching outcomes by appropriately adjusting its two-round matching radii and time intervals under different demand–supply conditions.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:06:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594529</guid>
    </item>
    <item>
      <title>Emergency Message Broadcast Mechanism in Vehicular Ad-Hoc Networks Based on Reinforcement Learning With Contention Estimation</title>
      <link>https://trid.trb.org/View/2591855</link>
      <description><![CDATA[In vehicular ad-hoc networks (VANETs), ensuring passenger safety requires fast and reliable emergency message broadcasts. The current communication standard for messaging in VANETs is IEEE 802.11p. As IEEE 802.11p allows carrier-sense multiple access with collision avoidance (CSMA/CA) in the media access control (MAC) layer. A large contention window (𝘊𝘞) value will increase delay, whereas a small 𝘊𝘞 value will increase the probability of collision. Therefore, adaptive regulation of the 𝘊𝘞 value is needed to achieve high reliability and low delay in VANETs, in accordance with variations in the environment. However, the traditional MAC protocol cannot achieve the aforementioned requirements. Reinforcement learning (RL) emphasizes the selection of optimal action according to observations of the environment to achieve optimal system performance. In this study, a Q-learning (QL) RL algorithm based on IEEE 802.11p was used to achieve the requirements of adaptive broadcasting. Adaptive broadcasting was achieved based on a reward definition of high reliability and low delay for the QL algorithm. In this approach, the learning state is the $CW$ size, the system sets up a Q-table using RL, and the optimal action is based on the maximum Q-value. The 𝘊𝘞 size can be provided with adaptive self-regulation by RL, providing high reliability and low delay for the broadcast of emergency messages. We also compared our proposed scheme to other QL-based MAC protocols in VANETs by performing simulations and demonstrated that it can achieve high reliability and low delay for the broadcast of emergency messages.]]></description>
      <pubDate>Wed, 05 Nov 2025 10:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591855</guid>
    </item>
    <item>
      <title>Adaptive Emergency Message Broadcast Based on Network Connectivity States for Vehicular Ad Hoc Networks in Highway Environments</title>
      <link>https://trid.trb.org/View/2512281</link>
      <description><![CDATA[Broadcast plays a significant role in the emergency message propagation in Vehicular Ad hoc Networks (VANETs). However, current broadcast relay strategies easily cause serious message loss and larger broadcast costs due to the poor environmental adaptability. In this paper, to handle the above problems, the authors propose an Adaptive Connectivity-Aware Relay (ACAR) strategy, which has two striking features: multiple network connectivity states and dynamic broadcast relay policies. They design four network connectivity states and their corresponding four broadcast relay policies. In disconnected networks, only the vehicle with the same movement direction as the message propagation direction acts as the relay, so as to relieve the message loss. Moreover, different broadcast periods are allocated for different applications, so as to reduce the broadcast costs. In connected networks, the link quality and transmission distance are adopted to select the relay in the sender-based relay pattern, so as to address the relay invalidation problem. Further, different candidate relays are assigned different broadcast priorities and different broadcast waiting time in the receiver-based relay pattern, so as to address the transmission conflict problem. Simulation results show that ACAR outperforms existing competing schemes in terms of end-to-end delay, broadcast success rate, and packet overhead.]]></description>
      <pubDate>Fri, 25 Jul 2025 11:34:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512281</guid>
    </item>
    <item>
      <title>Dynamic matching radius decision model for on-demand ride services: A deep multi-task learning approach</title>
      <link>https://trid.trb.org/View/2447555</link>
      <description><![CDATA[As ride-hailing services have experienced significant growth, most research has concentrated on the dispatching mode, where drivers must accept the platform’s assigned trip requests. However, the broadcasting mode, in which drivers can freely choose their preferred orders from those broadcast by the platform, has received less attention. One crucial but challenging task in such a system is the determination of the matching radius, which usually varies across space, time, and real-time supply/demand characteristics. This study develops a Deep Learning-based Matching Radius Decision (DL-MRD) model that predicts key system performance metrics for a range of matching radii, which enables the ride-hailing platform to select an optimal matching radius that maximizes overall system performance according to real-time supply and demand information. To simultaneously maximize multiple system performance metrics for matching radius determination, the authors devise a novel multi-task learning algorithm named Weighted Exponential Smoothing Multi-task (WESM) learning strategy that enhances convergence speed of each task (corresponding to the optimization of one metric) and delivers more accurate overall predictions. They evaluate their methods in a simulation environment designed for broadcasting-mode-based ride-hailing service. Their findings reveal that dynamically adjusting matching radii based on their proposed approach significantly improves system performance.]]></description>
      <pubDate>Tue, 31 Dec 2024 16:31:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447555</guid>
    </item>
    <item>
      <title>Enhancement of South Dakota’s Amber Alert System</title>
      <link>https://trid.trb.org/View/2434207</link>
      <description><![CDATA[From November of 2004 to May of 2006, several state departments formed a system to work in response to Amber Alerts. The South Dakota Public Broadcasting System purchased equipment to efficiently post high-quality Amber Alerts and photos to television, no matter what time of day or staff available. A cost-effective and valuable structure was installed to the South Dakota 511 Traveler Information System to alert callers to Amber Alert information. The Department of Criminal Investigation includes a call center where volunteers operate a phone system which records information received from callers. Amber Alerts are also posted at all South Dakota kiosks quickly and reliably now that they are connected to a network-based system rather than dial-up communication. The agencies have established a system and tested the program quarterly to continue being prepared in case of a true Amber Alert.]]></description>
      <pubDate>Mon, 30 Sep 2024 11:43:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434207</guid>
    </item>
    <item>
      <title>A Study of Public Logistics Information Delivery Using TPEG over China Multimedia Mobile Broadcasting</title>
      <link>https://trid.trb.org/View/2282433</link>
      <description><![CDATA[An intelligent transportation system (ITS) plays an important role in modern logistics industry. This paper presents a novel, visible public logistics information publishing service using transport protocol experts group (TPEG) and China Multimedia Mobile Broadcasting (CMMB). This paper presents how public logistics information can be embedded into TPEG in XML format and describes how the TpegML stream to be delivered over CMMB data channel through the stream multiplexing and electronic service guide (ESG). The economical and effective method introduced in this paper can solve the problems of heterogeneous data integration and public logistics information publishing in mobile environment.]]></description>
      <pubDate>Fri, 09 Feb 2024 14:52:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282433</guid>
    </item>
    <item>
      <title>Multiple attributes based physical layer authentication through propagation scenario identification in the internet of vehicles</title>
      <link>https://trid.trb.org/View/2304611</link>
      <description><![CDATA[The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing PLA approaches use all channel attributes for all communication scenarios and do not update reference attributes used to authenticate newly observed attributes during authentication, which leads to poor performance when the attributes become insensitive to communication scenarios. To address these limitations, the authors propose a multiple attributes-based PLA scheme that flexibly selects effective attributes according to current communication scenarios for improved authentication. First, the historical channel attributes including the ricean K-Factor (KF), the Delay Spread (DS), and the Azimuth Spread of Arrival (ASA) together with the communication scenarios (i.e., Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)) information of transmitters are used to establish a relationship and train a Long Short-Term Memory (LSTM) neural network to identify current communication scenarios. The softmax function is then used to evaluate the scenario identification performance of each attribute and the attributes with maximum softmax values are dynamically selected for authentication. Second, to track and update the reference attributes, they use the historical attributes together with the past location and communication scenarios information of transmitters and train their model to predict future attributes for authentication. Finally, they use the Euclidean distance to measure the similarity between the selected and the predicted attributes to authenticate the transmitters. The authors validate the effectiveness of their approach and compare its performance with the existing methods through extensive simulations using realistic radio channel characteristics generated from the Quasi Deterministic Radio channel Generator (QuaDRiGa) platform. The results of the evaluation show that their system improves authentication performance and its false alarm and miss detection are respectively 19.19% and 52% lower than the existing approaches.]]></description>
      <pubDate>Fri, 22 Dec 2023 08:46:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2304611</guid>
    </item>
    <item>
      <title>Antenna Combiner for Periodic Broadcast V2V Communication Under Relaxed Worst-Case Propagation</title>
      <link>https://trid.trb.org/View/2237876</link>
      <description><![CDATA[The performance of a previously developed analog combining network (ACN) of phase shifters for periodic broadcast vehicle-to-vehicle (V2V) communication is investigated. The original ACN was designed to maximize the sum of signal-to-noise ratios (SNRs) for 𝐾 consecutive cooperative awareness messages (CAMs). The design was based on the assumption of a dominant propagation path with an angle of arrival (AOA) that is constant for 𝐾 messages. In this work, the authors relax this assumption by allowing the AOA and path-loss (PL) of the dominant path to be time-variant. Assuming a highway scenario with a line of sight (LOS) propagation between vehicles, they use affine approximations to model the time variation of different path quantities, including the PL, the relative distance-dependent phase shift between antennas, and the AOA-dependent far-field function of the antennas. By leveraging these approximations, they analytically derive the ACN sum- SNR as each one of these quantities varies over 𝐾 CAMs. Moreover, they suggest a design rule for a phase slope that is robust against time variation of the dominant path and optimal under time-invariant conditions. Finally, they validate this design rule using numerical computations and an example of vehicular communication antenna elements.]]></description>
      <pubDate>Tue, 28 Nov 2023 16:28:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237876</guid>
    </item>
    <item>
      <title>A Metaheuristic MAC Protocol for Safety Applications in Cognitive Vehicular Networks</title>
      <link>https://trid.trb.org/View/2287611</link>
      <description><![CDATA[In a dynamic cognitive vehicular network (CVN) environment, ensuring reliable data delivery of safety messages (sm) is crucial. However, fast-moving vehicles and rapidly changing channel accessibility make sm broadcasting challenging. To address this issue, the authors propose a cooperative make-up technique, which leverages the 2-Hop Harris Hawks Optimization (HHO) algorithm to choose an optimal helper for sm retransmission in case of failed direct transmission. Their simulations using the Network Simulator version 2 with SUMO demonstrate that the proposed approach achieves cooperative communication within the tight 100ms sm broadcast time frame and outperforms contemporary CVN protocols regarding throughput and packet delivery ratio. Their results suggest that their approach holds great promise for reliable and efficient data delivery in CVNs.]]></description>
      <pubDate>Tue, 14 Nov 2023 09:35:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2287611</guid>
    </item>
    <item>
      <title>A Novel Protocol for Information Dissemination in Vehicular Networks</title>
      <link>https://trid.trb.org/View/1972419</link>
      <description><![CDATA[The Vehicular Ad Hoc Networks (VANETs) are rapidly emerging as we are moving towards autonomous and self-driving vehicles. Network hardware efficiency is growing day by day. However, optimal algorithms play a vital role in the effective utilization of the network. The need for supporting algorithm is vital. Cooperative network behavior is highly suitable for VANETs in comparison to infrastructure-based networks. The cooperation for active information exchange between vehicles is a prime requirement to provide safe, secure, and smoother experience on roads. Broadcasting is always the best way to disseminate information among all neighboring nodes in this kind of networks. However, broadcasting in VANET has multiple issues such as broadcast storm problem, network partition problem, network contention. The benefits of broadcasting inspire the presented research work and propose a solution as Priority Based Efficient Information Dissemination Protocol (PBEID). The work utilizes probability-based and density-based information dissemination using conventional broadcasting. The work has been compared with popular techniques for information dissemination and has been statistically proven significant.]]></description>
      <pubDate>Tue, 12 Sep 2023 09:20:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972419</guid>
    </item>
    <item>
      <title>A scalable time-division-based emergency messages broadcast scheme for connected and autonomous vehicles in urban environment</title>
      <link>https://trid.trb.org/View/2062848</link>
      <description><![CDATA[Connected and Autonomous Vehicles (CAVs) technology promises to revolutionize the transportation sector by solving many safety and traffic efficiency challenges while significantly enhancing passengers' and drivers' comfort. Most of these applications are built upon multi-hop broadcast of information among vehicles, leading to an increased contention and thus higher collision probability due to the limited bandwidth available for vehicular communications. In this paper, the authors propose a novel Time-Division-based Broadcast (TDB) scheme to mitigate interferences originating from hidden nodes (i.e., vehicles) to meet the stringent safety applications requirements. Then, they use this broadcast scheme as a basis for designing a new efficient and scalable protocol for emergency messages dissemination in urban vehicular networks (UV-TDB: an Urban, Vehicular and Time-Division-based Broadcast protocol). The simulation experiments show that UV-TDB significantly improves the performance of multi-hop broadcast and outperforms two state of the art protocols in terms of the achieved reduction in broadcast overhead (up to 77.9%), packet collisions ratio (up to 93.5%), interference rate (up to 43.5%) and increase in message reception rate (up to 337.7%). The results highlight also that UV-TDB adapts better to varying levels of vehicle density, making it more scalable.]]></description>
      <pubDate>Tue, 24 Jan 2023 09:29:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2062848</guid>
    </item>
    <item>
      <title>VP-CAST: Velocity and Position-Based Broadcast Suppression for VANETs</title>
      <link>https://trid.trb.org/View/2040164</link>
      <description><![CDATA[In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, the authors present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.]]></description>
      <pubDate>Fri, 30 Dec 2022 16:58:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2040164</guid>
    </item>
    <item>
      <title>Hybrid Combining of Directional Antennas for Periodic Broadcast V2V Communication</title>
      <link>https://trid.trb.org/View/1936083</link>
      <description><![CDATA[A hybrid analog-digital combiner for broadcast vehicular communication is proposed. It has an analog part that does not require any channel state information or feedback from the receiver, and a digital part that uses maximal ratio combining (MRC). The authors focus on designing the analog part of the combiner to optimize the received signal strength along all azimuth angles for robust periodic vehicle-to-vehicle (V2V) communication, in a scenario of one dominant component between the communicating vehicles (e.g., highway scenario). The authors show that the parameters of a previously suggested fully analog combiner solves the optimization problem of the analog part of the proposed hybrid combiner. Assuming L directional antennas with uniform angular separation together with the special case of a two-port receiver, the authors show that it is optimal to combine groups of [L/2] and [L/2]antennas in analog domain and feed the output of each group to one digital port. This is shown to be optimal under a sufficient condition on the sidelobes level of the directional antennas. Moreover, the authors derive a performance bound for the hybrid combiner to guide the choice of antennas needed to meet the reliability requirements of the V2V communication links.]]></description>
      <pubDate>Thu, 26 May 2022 16:59:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1936083</guid>
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