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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Estimating origin-destination matrices with sparse seed matrices</title>
      <link>https://trid.trb.org/View/2679122</link>
      <description><![CDATA[Origin-destination matrices of traveller flows are a key ingredient to transport planning. In public transport planning, most agencies conduct origin-destination surveys to extract line-level origin-destination matrices. These matrices, however, only partially represent ridership, as only a fraction of travellers are surveyed. Hence, they need to be scaled to real ridership, typically by using automatic passenger counts (APC) and algorithms such as iterative proportional fitting (IPF). This procedure works well for busy lines, where seed matrices present few or no zeros (i.e., absence of observations for a given origin-destination pair), however it becomes less reliable on sparsely used lines, where seed matrices present a high percentage of structural and sampling zeros. It is currently unknown, up to which percentage of zeroes IPF can be reliably used, and how to handle zeroes more generally. In this paper, we apply IPF to simulated (ground truth) and real origin-destination seed matrices to quantify the reliability of IPF and to test different replacement values for zeroes. We work with matching data from automatic passenger counters and a large origin-destination survey with 26,000 + participants on 70 + public transport lines that was conducted in 2022 in Geneva, Switzerland. We find that the reliability of IPF measured by the estimation error exponentially correlates with the percentage of zeros in the seed matrix. We test replacement values of zeroes between 0 and 10 and find that 1 is the best replacement for sampling zeros in the seed matrix to minimize the estimation error and simultaneously improve convergence. Practitioners and academics can use these results to maintain the advantages of IPF in practice (computational lightweight, simplicity, implementation in most common software) yet improve its reliability on sparsely used lines.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679122</guid>
    </item>
    <item>
      <title>Public Transit Origin–Destination Matrix Determination Using Historical Tracking of Automated Databases</title>
      <link>https://trid.trb.org/View/2613627</link>
      <description><![CDATA[Great efforts have been made and significant capital has been invested in obtaining, storing, and processing public transportation automatic data. Although public transportation systems require numerous data inputs for proper operation, most of this automatic data have not been utilized. This study focused on estimating the public transit origin–destination (OD) matrix using automated fare collection and automatic vehicle location data. Usually, OD matrices are calculated using the classic four-stage model (trip generation, distribution, modal split, and assignment). The OD estimation in this study is based on a disaggregated model and does not follow the classic model order. The data were obtained from the entry-only transaction system in Mashhad, Iran. A trip chain heuristic was developed to determine passengers’ alighting stops/stations. Then, another heuristic was developed to determine the passengers’ destination zones. The results for OD estimation yielded a 253 by 253 matrix, along with trip production and attraction for each zone. Some important performance measures, such as the transfer rate, were also calculated. The results were verified by extensive manual data collection and showed that the proposed method has an average root mean square error per line equal to 3.56 persons per hour. Based on the results, this study shows that the proposed method offers a less labor-intensive and more accurate way to estimate the OD matrix compared to traditional methods of determining the origin–destination matrix.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613627</guid>
    </item>
    <item>
      <title>Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation</title>
      <link>https://trid.trb.org/View/2512317</link>
      <description><![CDATA[Intelligent Transportation Systems (ITSs) are designed to alleviate traffic congestion and provide convenience for travelers or decision-makers. However, the challenge lies in obtaining complete and accurate traffic data because of various factors. Therefore, many models have been studied for traffic data imputation. Most studies overlook the extreme case where the missing rate of observed traffic data exceeds 90%, causing the existing traffic data imputation models to struggle in achieving satisfactory results. To address this extreme case, this paper proposes a novel Latent Factor Analysis model with Temporal Regularized Constraint (LFA-TRC) for handling road traffic data imputation. The proposed model is built on three main parts: a) Enhancing the stability of the training process through spatio-temporal linear biases, b) Capturing information in the traffic data time series by the first-order temporal difference constraint, and c) Accelerating convergence during the training process by incorporating generalized momentum. Experimental results on seven real-world traffic datasets demonstrate that the proposed LFA-TRC model outperforms eight state-of-the-art models in traffic data imputation and reduces the Mean Absolute Percentage Error (MAPE) by an average of 20.81%.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:55:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512317</guid>
    </item>
    <item>
      <title>Updating origin–destination matrices and link probabilities in public transportation networks</title>
      <link>https://trid.trb.org/View/2582958</link>
      <description><![CDATA[To update a public transportation origin–destination (OD) matrix, the link choice probabilities by which a user transits along the transit network are usually calculated beforehand. In this work, we reformulate the problem of updating OD matrices and simultaneously update the link proportions as an integer linear programming model based on partial knowledge of the transit segment flow along the network. We propose measuring the difference between the reference and the estimated OD matrices with linear demand deficits and excesses and simultaneously having slight deviations from the link probabilities to adjust to the observed flows in the network. In this manner, our integer linear programming model is more efficient in solving problems and is more accurate than quadratic or bilevel programming models. To validate our approach, we build an instance generator based on graphs that exhibit a property known as a “small-world phenomenon" and mimic real transit networks. We experimentally show the efficiency of our model by comparing it with an Augmented Lagrangian approach solved by a dual ascent and multipliers method. In addition, we compare our methodology with other instances in the literature.]]></description>
      <pubDate>Tue, 02 Sep 2025 08:49:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582958</guid>
    </item>
    <item>
      <title>A Generalized Perturbation Matrix-Based Fractional-Order Optimization Method of GM(r,2) for Inferring Driving Intention</title>
      <link>https://trid.trb.org/View/2559295</link>
      <description><![CDATA[A fractional-order optimization method based on generalized perturbation matrix of GM(r,2) is proposed in this article. The smaller the perturbation bound, the more stable the model. By minimizing perturbation bound, a generalized perturbation matrix is given, which is the solving equation of the optimized fractional order. With different coefficients δ, different fractional orders can be calculated by a linear equation in one variable. Maximum relative error (RE) eₘ and mean absolute percentage error (MAPE) with different fractional orders can be obtained. Based on the smallest eₘ and the MAPE, the optimized fractional order of GM(r,2) can be determined. Compared with particle swarm optimization (PSO) and long short-term memory (LSTM) network transfer learning optimization methods, the MAPE of the proposed method is much smaller than that of PSO and slightly greater than LSTM network transfer learning optimization. The proposed method is superior to others without iteration calculation, and the convergence problem of PSO and the computational burden problem of transfer learning based on LSTM network optimization can be further improved. A GM(r,2) with optimized fractional order rₒₚₜ is evaluated in inferring driving intention of an active collision avoidance system for electric vehicles. Car-following simulations are performed to demonstrate the effectiveness of the proposed fractional-order optimization method with simple structure and flexible implementation.]]></description>
      <pubDate>Thu, 28 Aug 2025 17:11:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559295</guid>
    </item>
    <item>
      <title>Automatic configuration analysis method of planetary gear automatic transmission</title>
      <link>https://trid.trb.org/View/2506330</link>
      <description><![CDATA[Planetary gear trains (PGT) with the aim of achieving multiple speed ratios are widely used in automatic transmission (AT). A fast configuration analysis method is of significance for improving the efficiency of AT design, and it is essential to study the automatic configuration analysis method for AT. For this purpose, the AT configuration is represented by a matrix model, which includes the structural matrix of PGT and shifting elements (SEs) matrix. Based on the proposed matrix representation, the speed relation and torque relation matrices of AT are automatically derived to analyze the kinematics and dynamics. Five performance indexes of AT configuration, specifically transmission ratio, rotation speed of components, torque transmitted by SEs, power flow, and transmission efficiency, are automatically calculated through the analysis result. The entire analysis process can be completed in 1?s on the computer. The proposed matrix model offers a unified representation for the configuration of AT derived from different synthesis algorithms. It facilitates rapid automated configuration analysis and performance evaluation, thereby positively influencing the design of automatic transmissions. Furthermore, the matrix model can be applied to the configuration synthesis of AT.]]></description>
      <pubDate>Tue, 11 Feb 2025 16:55:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2506330</guid>
    </item>
    <item>
      <title>Control of transient response of Marine Surface Vehicles based on parameterization of full-order Lyapunov matrices</title>
      <link>https://trid.trb.org/View/2485980</link>
      <description><![CDATA[This paper presents a double-loop sliding mode control design for a Marine Surface Vehicle (MSV) tracking a time-varying trajectory through a reduced-order error dynamics structure. This paper has the following unique contributions — firstly, unlike in literature on double-loop control, a common sliding surface is designed comprising both the position and velocity errors defined in the body-frame. Secondly, a virtual velocity command is designed so to ensure that the position and error dynamics in the body frame resemble a reduced-order structure and this order-reduction property of the error dynamics enables global exponential stabilization of errors in sliding mode. Thirdly, sliding function coefficients are chosen via the parameterization of full-order Lyapunov matrices, thereby enabling the modification of transient performance through characteristics like settling time and damping ratio and eliminating the need for enforcing state constraints using Barrier Lyapunov functions. Model uncertainties, disturbances are rejected without knowledge of their bounds by a robust compensator along with input saturation errors, ensuring uniform and ultimate boundedness of all error signals in the system. Closed-loop system stability is shown via Lyapunov theory. This approach is shown to be advantageous for particular types of obstacle avoidance problems. Comparative numerical simulations are presented to validate the effectiveness and superiority of the proposed approach.]]></description>
      <pubDate>Mon, 27 Jan 2025 08:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485980</guid>
    </item>
    <item>
      <title>Estimating OD Matrix with Capacity Limited Method</title>
      <link>https://trid.trb.org/View/2263919</link>
      <description><![CDATA[Most of the existing origin destination (OD) estimators are not suited for crowded highway network. This paper puts forward the capacity limited method, which takes the interaction between volume and capacity into account in the estimating of OD matrix. An example shows estimated OD matrix with capacity limited method can get more accurate result than without it.]]></description>
      <pubDate>Mon, 06 Jan 2025 15:55:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2263919</guid>
    </item>
    <item>
      <title>Path-Based Origin-Destination Matrix Estimation Utilizing Macroscopic Traffic Dynamics</title>
      <link>https://trid.trb.org/View/2414096</link>
      <description><![CDATA[The origin-destination (OD) matrix is a crucial requirement for transportation management and planning. Efficient OD matrix estimation is important to enhance the advancement of intelligent transportation systems. The authors present a novel approach for the estimation of static OD matrices using within-day traffic flow dynamics. The signalised cell transmission model (CTM) is utilised to capture the dynamics of a specific network and associate road segment count observations with path demands. This model is extended to capture per-path densities, yielding a path-based OD matrix problem formulation that results in a nonlinear optimisation problem. Efficient solution methodologies, based on convex and nonconvex optimisation theory, are developed for free-flow and congested conditions, respectively. In contrast with the majority of research for the OD matrix estimation problem, this work offers the following advantages: 1) no prior or target OD matrices are needed to implement the approach outlasting the bias and dependency on such matrices, 2) no historical data are required for accurate estimations, 3) no route choice model or split ratios are needed, 4) no user equilibrium conditions are required for high-quality estimation, and 5) even low partial coverage of the network is sufficient to provide high-quality OD matrix estimation. The authors illustrate the efficiency of the proposed approach on three literature real-life arterial networks and show that the proposed approach yields accurate results under both free-flow and congested scenarios.]]></description>
      <pubDate>Mon, 11 Nov 2024 09:39:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2414096</guid>
    </item>
    <item>
      <title>Multi-Port Model of Non-Cooperation Competition Strategy</title>
      <link>https://trid.trb.org/View/2282877</link>
      <description><![CDATA[First, the paper analyzes the necessity of a number of competing ports, then moves to a detailed description of container transport supernetwork and stochastic equilibrium model, in which it extends from the dynamic Stackelberg Game Model to EPEC (Equilibrium Problem with Equilibrium Constraints) model. It ensures the existence and use of diagonalization algorithm by building a continuous concave function (maximum).]]></description>
      <pubDate>Tue, 17 Sep 2024 13:45:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282877</guid>
    </item>
    <item>
      <title>Origin–destination matrices from smartphone apps for bus networks</title>
      <link>https://trid.trb.org/View/2417458</link>
      <description><![CDATA[The knowledge of passenger flows between each origin–destination (OD) pair is a main requirement in public transport for service planning, design, operation, and monitoring, and is represented by OD matrices. Although they can be determined by traditional approaches (e.g., surveys, ride-check counts, and/or smartcard-based methods), the availability of new technologies and the proliferation of portable devices triggers an emerging interest in building OD matrices from the apps of bus operators. This research proposes the first framework for the estimation of OD matrices on transit networks by processing smartphone app call detail records (SACDRs). The framework is experimentally tested on a sample of 30 workdays of an Italian bus operator. The results are represented by easy-to-read control dashboards based on maps, which help quantify and visualise the OD matrices in the metropolitan area of Cagliari (Italy). The experimentation shows that the framework can properly estimate the number of trips for both origin and destination w.r.t. OD matrices built from household surveys: the mean absolute error is on average lower than five movements for 90% of the origins and 85% of the destinations.]]></description>
      <pubDate>Tue, 17 Sep 2024 09:33:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417458</guid>
    </item>
    <item>
      <title>Analyzing the Effects of the Growth of Maritime Industries on the Regional Economy with Dynamic Inverse Matrix</title>
      <link>https://trid.trb.org/View/2203539</link>
      <description><![CDATA[The study on the interaction between the growth of the maritime industries and the output of other industries is important to the shipping center in Dalian. Based on an input-output table, this paper first selects five maritime industries according to the construction plan of the Shipping Center and the attributes of the maritime industries. Second, DIM (Dynamic Inverse Matrix) is calculated with the regional input-output table for six years. Then, the contributions of other industries to maritime ones and contributions of the maritime industries to increments of residential and governmental demands are analyzed The results show that Petroleum and Chemistry, Energy Mining and Supply, Metal Mining and Products industries closely relate to the maritime industries in terms of input and output.]]></description>
      <pubDate>Thu, 25 Jul 2024 17:12:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203539</guid>
    </item>
    <item>
      <title>Experimental Verification of the 3x5 Matrix Converter Using Indirect Control</title>
      <link>https://trid.trb.org/View/2319432</link>
      <description><![CDATA[This paper deals with the experimental implementation and the verification of the indirect control for the direct matrix converter in the 3x5 configuration. As matrix converters gains popularity, their application in the drives and hybrid vehicles can bring advantages. In first, the theoretical preview is presented with development and improvements in the field of the matrix converters. Then, concept and construction of the 3x5 matrix converter sample is shown, together with experimental implementation of the control algorithm to the DSP and FPGA. Finally, the model of the matrix converter used for the motor drive is verified and measured at the output power of 930W. The measured results are then compared to the simulation model to compare the overall behavior of the experimental converter and simulation model created in the MATLAB Simulink environment.]]></description>
      <pubDate>Thu, 18 Apr 2024 17:07:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2319432</guid>
    </item>
    <item>
      <title>Study On Both Theory and Application for an Algorithm of Dynamic OD Matrix Estimation Used For Urban Traffic Systems</title>
      <link>https://trid.trb.org/View/2281602</link>
      <description><![CDATA[The dynamic OD matrix is important information for urban traffic management and traffic control. Since 1990s, many scholars studied its theory. Nevertheless, few of them can be used in practice. In this paper, the model and its solutions for maximum entropy method are derived. Then the equivalent iterative algorithm model was calibrated. Finally its correctness is shown by a simulation network.]]></description>
      <pubDate>Fri, 29 Mar 2024 16:58:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2281602</guid>
    </item>
    <item>
      <title>Short-Term, Spatio-Temporal Forecasting of Commuting Origin-Destination Demand During Peak Hours</title>
      <link>https://trid.trb.org/View/2335321</link>
      <description><![CDATA[Dynamic traffic assignment (DTA) models are essential for managing urban transportation systems and tackling congestion challenges. Accurate prediction of Origin-Destination (O-D) demand matrices, which exhibit spatio-temporal dependencies, is crucial for DTA models. However, existing research primarily focuses on macro-level O-D demand forecasting, neglecting the specific needs of predicting commute demand. In this paper, the authors propose a novel approach to predict commute O-D demand matrices. The authors leverage future mainstream O-D trip observation data to establish time-dependent features for more accurate O-D predictions. Additionally, the authors utilise a community clustering algorithm to divide commuting O-D regions at the road level, effectively capturing spatial characteristics for commuting demand. To forecast commute O-D demand matrices during peak hours of weekdays, the authors design a novel 4T-GCN model that leverages both temporal and spatial features from historical data to predict O-D demand. The performance of the proposed model was compared with two state-of-the-art methods, and an ablation study was conducted to evaluate various adjacency matrices. The results demonstrate that the proposed model outperforms the conventional STGCN model by more than 35.5% in terms of prediction accuracy, offering valuable insights for urban transportation planning and management.]]></description>
      <pubDate>Sun, 18 Feb 2024 16:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2335321</guid>
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