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    <title>Transport Research International Documentation (TRID)</title>
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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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      <title>Algorithmization and Hardware Implementation of Polar Coding for 5g Telecommunications</title>
      <link>https://trid.trb.org/View/2403977</link>
      <description><![CDATA[The article explores a recursive algorithm for determining Bhattacharyya parameters, which is a measure of the degree of channel polarization for telecommunications with polar coding. A method is given for determining the positions of information and fixed bits in a polar code. The principles of constructing a polar encoder and a polar decoder successive cancellation are considered. The Field-Programmable Gate Array implementation of the successive cancellation list decoder is considered. Experimental studies of the construction of a polar code using Gaussian approximation have been carried out. The influence of the design signal-to-noise ratio on the bit error rate of telecommunications with polar codes has been studied. The use of multi-position modulation in telecommunications with polar codes has been studied. It is expected that the results obtained will be useful in hardware and software implementation of 5G telecommunications with polar coding.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403977</guid>
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    <item>
      <title>Off-Grid Electric Vehicle Charging Station with Integrated Local Server OCPP Protocol as a Management System</title>
      <link>https://trid.trb.org/View/2403975</link>
      <description><![CDATA[Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, The authors' main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. The authors analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. The authors' research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, the authors tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403975</guid>
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      <title>Accuracy Low-Cost “Handshake” Two-Way Ranging Protocol Applied to Road Traffic Communications</title>
      <link>https://trid.trb.org/View/2403976</link>
      <description><![CDATA[The densification of the vehicle fleet leads to numerous challenges in road safety management. Wireless Sensor Networks (WSN) helps to manage vehicle-vehicle-infrastructure interactivity, in particular to predict dangers and avoid accidents, detect and penalize traffic violations. This requires a massive deployment of communicating nodes embedded in vehicles and in road infrastructure. The reliability of the systems built on these nodes lies in the accuracy of the ranging. In order to improve the factors that impact accuracy, various ranging protocols have been developed, but their implementations most often involve the use of expensive and specialized transceivers, which incorporate a dedicated IEEE 802.15.4a ranging layer. The authors' approach implements the basic and advanced functions integrated into low-cost transceivers and microcontrollers. As result, in two or three links according to the case, the authors'  protocol performs simultaneously the ranging and the data exchange, against a specific ranging session with a minimum of three exchanges for the other protocols. Moreover, each node knows the ranging data and has its clock frequency adapted to the controlled process. So, the authors meet the challenges of large-scale low-cost node deployment, while ensuring high reliability and performance of ranging.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403976</guid>
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    <item>
      <title>Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport</title>
      <link>https://trid.trb.org/View/2403973</link>
      <description><![CDATA[Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403973</guid>
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    <item>
      <title>Improving Traffic Safety in Existing and New Road Tunnels with the Novel NDBA Concrete Safety Barrier</title>
      <link>https://trid.trb.org/View/2403974</link>
      <description><![CDATA[Two main elements are essential in terms of road traffic safety. The first element is accident prevention and the second is the minimization of accident severity once a crash has occurred. Concrete safety barriers have very good anti-collision performance against roadside obstacles, relatively modest construction and maintenance costs, and low dynamic deflection and therefore are widely used in tunnels. Thanks to their characteristic redirective profile these barriers can redirect errant vehicles into their original lane after collisions. However insufficient research has been done for increasing the performance of concrete barriers purposely designed for tunnel installations. This research presents the new “NDBA Tunnel” concrete safety barrier designed and constructed by the Italian Road Operator ANAS indicated to be installed in road tunnel sections for safety improvements. In Europe, road safety barriers must be designed in compliance with the European Standard EN 1317. Therefore, the barrier “NDBA Tunnel” was subject to the TB11 and TB81 full-scale crash tests according to the European EN 1317 regulation. The results prove the barrier's ability to absorb impact loads of light and heavy vehicles with a working width W2. Therefore, the NDBA concrete barrier can be installed on existing or new tunnels at a distance less than or equal to 70 cm from the facing of the tunnel wall.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403974</guid>
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    <item>
      <title>Blockchain-Based Security Framework for VNF Package Protection in 5g Network Slicing Services</title>
      <link>https://trid.trb.org/View/2403972</link>
      <description><![CDATA[The demand for scalable, open and granular networks has enabled mobile networks such as 5G to adopt new concepts including NFV (Network Function Virtualization). NFV separates the dependency of network functions from the hardware component, allowing them to be deployed flexibly and dynamically across network slices. In this way, operators can deliver personalized services and optimize the use of network resources, contributing to greater operational efficiency and an enhanced end-user experience. However, there are several issues to be addressed, such as the security of VNFs and the way in which service provider customers instantiate NSSIs (Network Slice Subnet Instance). In this article, the authors present a new approach based on blockchain 2.0, which guarantees the immutability of VNFs and templates, as well as highly secure instantiation with access management and support for replay attacks.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403972</guid>
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    <item>
      <title>Position Falsification Detection Approach Using Travel Distance-Based Feature</title>
      <link>https://trid.trb.org/View/2403971</link>
      <description><![CDATA[This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:42:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403971</guid>
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    <item>
      <title>The Needs of Researchers Implementing Supply Chain Digitalisation</title>
      <link>https://trid.trb.org/View/2403978</link>
      <description><![CDATA[The digitalisation of the supply chain has presented substantial opportunities for companies to enhance resource allocation, pricing strategies, and overall operational efficiency. In this context, researchers play a crucial role in developing novel methodologies and algorithms that can confer a competitive edge to stakeholders. This study employed operational research methods to investigate the specific requirements of researchers in this domain. The results highlight the challenges, motivations, and practical needs experienced by researchers. Additionally, data-related issues and databases were examined to identify areas of improvement. The findings indicate that enhancing researchers' engagement in data-driven solutions for supply chain problems primarily hinges on addressing issues related to data quality, data accessibility, and regular dataset updates. By addressing all the aspects defined in this study, organisations can enhance the practical implementation of findings and drive advancements in supply chain management.]]></description>
      <pubDate>Fri, 19 Jul 2024 13:57:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403978</guid>
    </item>
    <item>
      <title>Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus</title>
      <link>https://trid.trb.org/View/1754822</link>
      <description><![CDATA[Smart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754822</guid>
    </item>
    <item>
      <title>On Transport Monitoring and Forecasting During COVID-19 Pandemic in Rome</title>
      <link>https://trid.trb.org/View/1754821</link>
      <description><![CDATA[This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid “new normal”. The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754821</guid>
    </item>
    <item>
      <title>A Big Data Demand Estimation Model for Urban Congested Networks</title>
      <link>https://trid.trb.org/View/1754820</link>
      <description><![CDATA[The origin-destination (OD) demand estimation problem is a classical problem in transport planning and management. Traditionally, this problem has been solved using traffic counts, speeds or travel times extracted from location-based sensor data. With the advent of new sensing technologies located on vehicles (GPS) and nomadic devices (mobile and smartphones), new opportunities have emerged to improve the estimation accuracy and reliability, and more importantly to better capture the dynamics of the daily mobility patterns. In this paper the authors frame this new data in a comprehensive framework which estimates origin-destination flows in two steps: the first step estimates the total generated demand for each traffic zone, while the second step adjusts the spatial and temporal distribution on the different OD pairs. The authors show how mobile data can be used to obtain OD matrices that reflect the aggregated movements of individuals in complex and large-scale instances, while speed information from floating car data can be used in the second step. The authors showcase the added value of big data on a realistic network comprising Luxembourg’s capital city and its surrounding. The authors simulate traffic by means of a commercial simulation software, PTV-Visum, and leverage real mobile phone data from the largest telco operator in the country and real speed data from a floating car data service provider. Results show how OD estimation improves both in solution reliability and in convergence speed.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754820</guid>
    </item>
    <item>
      <title>A Big Data Application for Low Emission Heavy Duty Vehicles</title>
      <link>https://trid.trb.org/View/1754819</link>
      <description><![CDATA[Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO₂ emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOₓ emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOₓ has been decreased more than 10%.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754819</guid>
    </item>
    <item>
      <title>A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions</title>
      <link>https://trid.trb.org/View/1754818</link>
      <description><![CDATA[This work applies a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754818</guid>
    </item>
    <item>
      <title>Evaluating Suitable Glosa-Algorithms by Simulation Considering Realistic Traffic Conditions and V2X-Communication</title>
      <link>https://trid.trb.org/View/1754817</link>
      <description><![CDATA[Vehicle-to-Everything (V2X) communication allows infrastructure elements, e.g., traffic lights, to directly communicate with vehicles, thereby allowing services like Green Light Optimized Speed Advisory (GLOSA), Probe Vehicle Data (PVD), and Traffic Signal Priority Request (TSP). The idea behind GLOSA is to assist vehicles approaching an intersection with speed advices in order to fulfill a given objective, e.g., minimizing fuel usage, emissions and/or delay. In a prior work (Kloeppel et al., 2019), several GLOSA algorithms were examined and their fitness (in the form of CO₂ emissions, fuel usage and delay) were evaluated under largely realistic conditions. This paper is an extension of the prior work and presents further examinations, which include a more detailed study on the behaviour of Diesel-powered vehicles when using the GLOSA algorithm of Stebbins et al. (2017) as well as a study considering fixed-time control coupled with intelligent vehicles.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754817</guid>
    </item>
    <item>
      <title>Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification</title>
      <link>https://trid.trb.org/View/1754816</link>
      <description><![CDATA[Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. The authors provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.]]></description>
      <pubDate>Mon, 21 Dec 2020 10:05:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1754816</guid>
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