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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>A Formal Model for the Chain-Branch-Leaf Clustering Scheme in OLSR based Vehicular Ad hoc Networks using Event-B</title>
      <link>https://trid.trb.org/View/1626435</link>
      <description><![CDATA[Evolution of Intelligent Transportation Systems (ITS) towards connected and autonomous vehicles requires robust communication protocols with proven or at least verified properties. In a recent work, the authors pointed out the advantages and limitations of both formal and simulation approaches when they are used separately during the design and evaluation processes of communication protocols dedicated to ITS. The authors' goal is to develop new tools combining formal methods such as Event-B with simulation formalism such as DEVS (Discrete Event System Specification) for proving and verifying the properties of ITS components models in large-scale scenarios. Previously, the authors presented the simulation models of a Vehicular Ad hoc Network (VANET) in a DEVS-based environment, where the Optimized Link State Routing (OLSR) protocol was used with a recently proposed clustering scheme, namely Chain-Branch-Leaf (CBL). Pursuing their objectives, this paper presents an equivalent model realized with Rodin, a formal tool based on a variant of the B method: Event-B. It shows how the specific properties of CBL clustering scheme are introduced in an Event-B model of OLSR from the literature, and how the numerous resulting proof obligations are discharged using the B prover in Rodin.]]></description>
      <pubDate>Mon, 29 Jul 2019 10:32:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1626435</guid>
    </item>
    <item>
      <title>From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region</title>
      <link>https://trid.trb.org/View/1626743</link>
      <description><![CDATA[Considering and measuring the similarity of human activities remains challenging. Existing studies of similarity measures based on traditional edit distance (ED), specifically on activity patterns, do not reflect the spatiotemporal characteristics in the measurement model. Additionally, interdependence between activities is ignored in existing multidimensional sequence alignment methods. To address the gap, the authors initially extend the traditional edit distance to a space-time-weighted edit distance (STW-ED). Specifically, differences in distance and time between activities are considered cost functions in the operation cost calculation (insertion, deletion, and substitution). They advance STW-ED to an augmented space-time-weighted edit distance method (ASTW-ED) that integrates an optimum-trajectory-based multidimensional sequence alignment method (OT-MDSAM) with STW-ED, treating the nonspatiotemporal dimensions as augment factors. In addition, ontology is considered for the similarity measure for nonspatiotemporal dimensions.To show the feasibility of their proposed approach, the authors conduct an empirical study based on an activity-based travel survey in the Puget Sound Region. Eight clusters (homemakers, regular workers with a colorful life, regular workers with a monotonous life, part-time workers, recreation travelers, senior travelers, no-job travelers, and night owl adventurers) are identified based on ASTW-ED and ontology. To cluster the similarity matrix derived from the introduced methods, the affinity propagation (AP) clustering method is employed because it is free of prior knowledge for clustering and can produce exemplars of the clusters. The empirical study indicates that, relative to existing methods for multidimensional activity similarity measurement and clustering, ASTW-ED performs better in terms of within-group homogeneity and between-group heterogeneity of clusters. In addition, the results reveal that ontology can improve clustering performance if it is considered for nonspatiotemporal dimensions provide better understanding of human behavior for urban governance..]]></description>
      <pubDate>Wed, 26 Jun 2019 17:14:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1626743</guid>
    </item>
    <item>
      <title>Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification</title>
      <link>https://trid.trb.org/View/1629423</link>
      <description><![CDATA[Hotspot identification (HSID) is a critical part of network-wide safety evaluation. Put simply, HSID involves ranking sites (e.g., roadway segments or intersections) on the basis of observed and/or estimated safety so they may be prioritized for treatment. Typical methods for ranking sites are often rooted in use of the Empirical Bayes (EB) method to estimate safety from both observed crash history and crash frequency predictions based on similar sites. Such procedures are an improvement over naïve methods that consider only observed crash frequencies/rates as they can account for regression-to-the-mean bias and are less subject to random variation in the crash data. That said, the performance of the EB method is highly related to the selection of a reference group of sites similar to the target site from which the safety performance function (SPF) used to predict crash frequency in the EB method will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs were developed. The performances of the clustering-based EB methods were then compared by using real crash data. Here, HSID results, when computed with Texas undivided rural highway cash data, suggested that all three clustering-based EB analysis methods are preferable over conventional statistical methods. Therefore, HSID accuracy may be further improved by properly classifying roadway segments on the basis of the heterogeneity in the data.]]></description>
      <pubDate>Wed, 19 Jun 2019 16:08:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1629423</guid>
    </item>
    <item>
      <title>Fuzzy cluster approach for area FWD representative basin from deflection measurement spatial variability</title>
      <link>https://trid.trb.org/View/1602540</link>
      <description><![CDATA[Pavement evaluation surveys represent the key element for efficient pavement management and for assuring pavement mission capability. In the Department of Defense (DOD), the Unified Facilities Criteria (UFC) 3-260-03 Airfield Pavement Evaluation provides the current guidance for pavement structural evaluations. During structural surveys, falling weight deflectometer (FWD) tests are executed at different locations within the same section, with the objective of obtaining a full assessment of the section’s structural capability. The availability of multiple deflection measurements for the same section raises the challenge of identifying the deflection basin best representing the entire section and its use in the backcalculation routine to determine the section’s structural strength. This manuscript proposes a fuzzy-based approach for the selection of a representative basin over multiple deflection basins collected for a specific section. The approach accounted for the spatial variability enclosed within the basin membership function obtained by fuzzy c-mean partitioning. The proposed methodology showed promising results for flexible pavements by offering more robust structural assessment that can account for spatial variability and thus minimize some aspects of mission risk that had a large effect on funding allocation and mission readiness.]]></description>
      <pubDate>Sun, 19 May 2019 16:49:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1602540</guid>
    </item>
    <item>
      <title>Who Do We Miss by Moving Travel Surveys Online? Assessments from Vermont</title>
      <link>https://trid.trb.org/View/1600884</link>
      <description><![CDATA[Online travel surveys are increasingly common because of cost, user burden, and geocoding advantages. Consequently, it is important to ask how online survey samples compare to paper survey samples. This study compares paper and online responses to a 2016, state-wide, Vermont transportation planning survey. Internet and smartphone access were analyzed by socioeconomic characteristics as well as by residential location to assess rural coverage. Respondents’ selection of the paper option was linked to lower population density. Online respondents showed significant spatial clustering. Crucially, the travel behavior and transportation attitudes of paper and online respondents differed even after weighting for demographic attributes. Smartphone ownership in Vermont is too skewed by age to be a primary travel survey method. Internet access is more widespread but does exclude some population segments. The authors recommend consideration of respondents by geographic location as well as socioeconomic characteristics when selecting survey mode and weighting, especially for state-wide surveys.]]></description>
      <pubDate>Wed, 24 Apr 2019 16:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1600884</guid>
    </item>
    <item>
      <title>Clustering Approach toward Large Truck Crash Analysis</title>
      <link>https://trid.trb.org/View/1590711</link>
      <description><![CDATA[Heterogeneity of crash data masks the underlying crash patterns and perplexes crash analysis. This paper aims to explore an advanced high-dimensional clustering approach to investigate heterogeneity in large datasets. Detailed records of crashes involving large trucks occurring in the state of Florida between 2007 and 2016 were examined to identify truck crash patterns and significant conditions contributing to the patterns. The block clustering method was applied to more than 220,000 crash records with nearly 200 attributes. The analysis showed promising results in segmenting a large heterogeneous dataset into meaningful subgroups (with 95.72% average degree of homogeneity for selected blocks). The goodness of fit for clustering methods is evaluated and both integrated completed likelihood (ICL) and pseudo-likelihood values improved significantly (20.8% and 21.1% respectively). Attribute clustering showed distinct characteristics for each cluster. Crash clustering revealed significant differences among the clusters and suggested that this crash dataset could be portioned as same-direction, opposing-direction, and single-vehicle crashes. Individual blocks defined by both row and column clustering were further investigated to better understand the contribution set of conditions that lead to large truck crashes. Major features for each of the three major types of crashes were analyzed, which may provide additional insights to develop potential countermeasures and strategies that target specific segments. The clustering approach could be used as a preanalysis method to identify homogeneous subgroups for further analysis, which will help enhance the effectiveness of safety programs.]]></description>
      <pubDate>Mon, 22 Apr 2019 13:44:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1590711</guid>
    </item>
    <item>
      <title>Identifying areas of interventions for improvement of shared modes for school trips</title>
      <link>https://trid.trb.org/View/1579720</link>
      <description><![CDATA[The paper reports an investigation to identify priority areas of intervention for improvement of shared modes for school trips. The perception of parents with and without car ownership was considered while undertaking the present work. A paper-pencil based survey instrument was designed to collect importance-performance data in five-point Likert-type scale from parents of school children with respect to available modes for school trips in Kolkata city. A database of 5929 responses was developed. The priority areas of intervention were identified using revised importance-performance analysis (revised-IPA) with fuzzy c-mean clustering. The analysis technique was instrumental in classifying school trip attributes under the clusters, namely ‘basic factors’, ‘performance factors’, and ‘excitement factors’. Management schemes namely, ‘concentrate here’, ‘keep up good work’, ‘possible overkill’, and ‘low priority’, for the shared modes of school trip were identified based on the perception of parents. Priority areas of interventions for shared modes were suggested by comparing the factor structure and the management schemes. The results indicate that there is a substantial difference in the requirements of ‘car owning’ and ‘non-car owning’ parents towards shared modes of school trip. The study identified weaknesses in the shared modes of school trip and highlighted areas of interventions necessary to improve the quality of school-bus and shared-cab in the context of Kolkata city. Although the work presented here is case specific, the methodology could be used suitably in formulating policy measures for improvement of school trips considering specific mode and requirements of target user group in other urban areas.]]></description>
      <pubDate>Thu, 07 Feb 2019 13:54:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1579720</guid>
    </item>
    <item>
      <title>Automated Eigensystem Realization Algorithm for Operational Modal Identification of Bridge Structures</title>
      <link>https://trid.trb.org/View/1571421</link>
      <description><![CDATA[The subject of vibration-based structural health monitoring (SHM) has attracted increasing attention, especially in the field of civil engineering. However, the development of these monitoring processes is not a simple task, with user interaction playing a significant role in the extraction of modal characteristics. In this paper, an automated operational modal analysis methodology based on an eigensystem realization algorithm (ERA) and a two-stage clustering strategy is proposed. Three crucial steps are addressed in this study. In the first phase, ERA is adopted to calculate modes from state-space models of different orders. Subsequently, the dissimilarity of modal parameters is employed as the features of fuzzy C-means (FCM) clustering to separate stable modes from unstable ones. The final step consists of grouping stable modes with similar structural properties to select physical modes. No user-specified parameter is required in the clustering procedure to single out physical modes. A practical bridge example is used to verify that the proposed method can estimate modal parameters effectively in real time.]]></description>
      <pubDate>Mon, 31 Dec 2018 15:27:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1571421</guid>
    </item>
    <item>
      <title>Traffic crash analysis with point-of-interest spatial clustering</title>
      <link>https://trid.trb.org/View/1552458</link>
      <description><![CDATA[This paper presents a spatial clustering method for macro-level traffic crash analysis based on open source point-of-interest (POI) data. Traffic crashes are discrete and non-negative events for short-time evaluation but can be spatially correlated with long-term macro-level estimation. Thus, the method requires the evaluation of parameters that reflect spatial properties and correlation to identify the distribution of traffic crash frequency. A POI database from an open source website is used to describe the specific land use factors which spatially correlate to macro level traffic crash distribution. This paper proposes a method using kernel density estimation (KDE) with spatial clustering to evaluate POI data for land use features and estimates a simple regression model and two spatial regression models for Suzhou Industrial Park (SIP), China. The performance of spatial regression models proves that the spatial clustering method can explain the macro distribution of traffic crashes effectively using POI data. The results show that residential density, and bank and hospital POIs have significant positive impacts on traffic crashes, whereas, stores, restaurants, and entertainment venues are found to be irrelevant for traffic crashes, which indicate densely populated areas for public services may enhance traffic risks.]]></description>
      <pubDate>Fri, 26 Oct 2018 10:34:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1552458</guid>
    </item>
    <item>
      <title>A data dissemination scheme based on clustering and probabilistic broadcasting in VANETs</title>
      <link>https://trid.trb.org/View/1541037</link>
      <description><![CDATA[VANETs (Vehicular Ad hoc Networks) have attracted tremendous attentions due to their high applicability and commercial value. However, the frequent topology changes caused by the fast mobility of nodes create many challenges to the efficient data delivery in vehicular environment. With the aim to guarantee the stable and reliable communication between nodes, in this paper, the authors propose a novel data dissemination scheme based on Clustering and Probabilistic Broadcasting (CPB). A clustering algorithm is first presented according to the driving directions of vehicles, by which vehicles could exchange their data in a clustered way with sufficient connection duration. In the constructed clustering structure, a probabilistic forwarding is presented to disseminate data among vehicles. Each cluster member forwards the received packet to its cluster head with a calculated probability which is associated with the number of times the same packet is received during one interval. When receiving the sent packet, the elected cluster header continues to disseminate it toward the transmission direction. Simulation results show that the authors' proposed protocol CPB outperforms the existing schemes in terms of information coverage, average message delay and packet delivery ratio.]]></description>
      <pubDate>Mon, 01 Oct 2018 09:26:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1541037</guid>
    </item>
    <item>
      <title>Transformation mechanism of vehicle cluster situations under dynamic evolution of driver’s propensity</title>
      <link>https://trid.trb.org/View/1542396</link>
      <description><![CDATA[The vehicle cluster situation is a kind of dynamic arrangement of a target vehicle and the surrounding vehicles during driving. Revealing the transfer mechanism of vehicle clustering in complex environments is of great significant for studying automated driving systems and driver assist systems. Taking three-lane scenario as an example, vehicle cluster situations changing with driver’s evolving propensity were studied. To this end, the data of vehicle cluster situations were collected and analyzed through driving experiments in different environments. In addition, the dual random variations of the vehicle cluster situation and driver’s propensity were modeled to explore the transfer mechanism. The verification results show that the predicted outcomes of vehicle cluster situations using the evolution rule of driver’s propensity are consistent with the real-time recognition. Therefore, the transfer mechanism of vehicle cluster situations was found to be effective and reasonable. It is important for the research of intelligent driving command system of Internet of Things (IOT).]]></description>
      <pubDate>Mon, 01 Oct 2018 09:25:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1542396</guid>
    </item>
    <item>
      <title>Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications</title>
      <link>https://trid.trb.org/View/1516362</link>
      <description><![CDATA[Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine.]]></description>
      <pubDate>Thu, 26 Jul 2018 14:40:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1516362</guid>
    </item>
    <item>
      <title>An Evolutionary Game Theoretic Approach for Stable and Optimized Clustering in VANETs</title>
      <link>https://trid.trb.org/View/1512925</link>
      <description><![CDATA[Discovering and maintaining efficient routes for data dissemination in vehicular ad hoc networks (VANETs) has proven to be a very challenging problem. Clustering is one of the control protocols used to provide efficient and stable routes for data dissemination. However, the rapid changes in network topology in VANETs creates frequent cluster reformation, which can seriously affect route stability. We propose a novel evolutionary game theoretic (EGT) framework to automate the clustering of nodes and nominations of cluster heads, to achieve cluster stability in VANETs. The equilibrium point is proven analytically and the stability is also tested using Lyapunov function. The performance of the proposed evolutionary game is empirically investigated with different cost functions using <italic> static</italic> and mobile scenarios. The simulation results demonstrate the effectiveness and robustness of our proposed EGT approach for different populations and speeds, thus reducing the overhead of frequent cluster reformation in VANETs.]]></description>
      <pubDate>Fri, 29 Jun 2018 10:34:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1512925</guid>
    </item>
    <item>
      <title>Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET</title>
      <link>https://trid.trb.org/View/1512964</link>
      <description><![CDATA[Existing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based communication suffers from various security and performance issues, hence Cluster based Communication is preferred nowadays. However, Cluster based Communication adds extra overhead and burden on the Cluster Head (CH) in dense network scenarios which eventually introduces delay and hinders network performance. To reduce the overburdening of single CH, a multi cluster head scheme is proposed in which multiple nodes in a cluster can act as CH to share the load of single CH. For a selection of stable CH, Hybrid Fuzzy Multi-criteria Decision making approach (HF-MCDM) is proposed in which Fuzzy Analytic Hierarchy Process (AHP) and TOPSIS methods are clubbed together for optimal decision making. Further because of association of Vehicular Ad-hoc Network (VANET) with life-critical applications, there is a dire need for a security framework to detect various malevolent attacks. Machine Learning based Intrusion Detection System (IDS) like Support Vector Machine (SVM) is one of the approaches for curbing such attacks. These intrusion detection based mechanism can be combined with various existing optimization techniques to improve their performance, and Dolphin Swarm Algorithm is one such approach. Dolphins have many significant biological features like echolocation, exchange of information, coordination, and division of labor. These biological features combined with swarm intelligence can be utilized for optimizing the detection and accuracy of SVM based IDS. So in this paper, a Multi-Cluster Head anomaly based IDS optimized by Dolphin Swarm Algorithm has been proposed and its results are compared with various existing Security frameworks in terms of parameters like false positive, detection rate, detection time, etc. and it is observed that the proposed approach performs better.]]></description>
      <pubDate>Tue, 26 Jun 2018 10:14:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1512964</guid>
    </item>
    <item>
      <title>Clustering-based reliable low-latency routing scheme using ACO method for vehicular networks</title>
      <link>https://trid.trb.org/View/1512967</link>
      <description><![CDATA[In vehicular ad hoc networks (VANETs), communication links break frequently due to the high velocity vehicles. In this paper, based on the existing ad hoc on-demand multipath distance vector (AOMDV) routing scheme, a new clustering-based reliable low-latency multipath routing (CRLLR) scheme is proposed by employing Ant Colony Optimization (ACO) technique. Herein the link reliability is used as criteria for Cluster Head (CH) selection. In a given cluster, a vehicle will be selected as CH if it has maximum link reliability. Moreover, the ACO technique is employed to efficiently compute the optimal routes among the communicating vehicles for VANETs in terms of four QoS metrics, reliability, end-to-end latency, throughput and energy consumption. Simulation results demonstrate that the proposed scheme outperforms the AQRV and T-AOMDV in term of overall latency and reliability at the expenses of slightly higher energy consumption.]]></description>
      <pubDate>Fri, 22 Jun 2018 16:42:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1512967</guid>
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