<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    </image>
    <item>
      <title>IMG2Speed: Generative AI and Multimodal Machine Learning for Predicting Operating Speed Distributions from Roadway Design and Context</title>
      <link>https://trid.trb.org/View/2725360</link>
      <description><![CDATA[Designers set target speeds to achieve safe operations, yet observed operating speeds often diverge because the influence of geometric and contextual elements (e.g., lane width, medians, trees, curb extensions, etc.) is not quantified in a way that is practical for design. A modern data-driven machine learning approach can be a potential solution to learn the quantitative mapping from observable design elements to operating speed distributions. This project proposes to (i) automate data curation from spot-speed reports using Generative Artificial Intelligence (AI) like Large/Vision Language Models (LLMs/VLMs); (ii) fuse the curated evidence base with street-view imagery and Geographic Information System (GIS)/context layers to extract geometric and streetscape attributes; and (iii) develop a machine learning (ML) model that estimate the percentiles of operating speeds used in practice (e.g, median, 85th) from cross-section and visual/context features.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:24:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725360</guid>
    </item>
    <item>
      <title>Evaluating the Reliability and Consistency of Statistical Models for Speed Distribution Analysis at Hazardous and Non-Hazardous Roadway Locations: Multifraction Data Set Approach</title>
      <link>https://trid.trb.org/View/2719396</link>
      <description><![CDATA[Understanding the statistical dynamics of traffic speeds at hazardous and non-hazardous locations is essential for effective roadway safety interventions. This study investigates the distinct characteristics of spot speed distributions across six Indian highway segments, including National and State Highways. It uses continuous probability distributions and hypothesis testing to assess the statistical significance of speed differences between hazardous and non-hazardous locations. The analysis is based on observed spot speed measurements, stratified into four data fractions (25%, 50%, 75%, and 100%), obtained using a simple random sampling with replacement approach. Seven continuous probability distributions, including normal, lognormal, gamma, logistic, Weibull, Burr, and generalized extreme value (GEV), have been fitted independently for each location type and data fraction to capture their distributional characteristics. The location, scale, and shape parameters of the models have been estimated using maximum likelihood estimation. However, model adequacy has been confirmed using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Furthermore, a two-sample Kolmogorov–Smirnov test has been conducted to assess the statistical difference in speed profiles between hazardous and non-hazardous locations. The results reveal that the GEV distribution consistently outperforms other models across all locations and data fractions, demonstrating strong parameter stability and model adequacy. Larger data fractions improved model performance and hypothesis testing power, indicating greater distributional robustness. To address the potential effect of vehicle interactions and transient congestion during the observation periods, a modified Kaplan–Meier (KM) framework is used to estimate congestion-adjusted desired speed distributions. The KM-based findings show that hazardous roadway locations exhibit higher desired speed potential and greater upper-tail speed characteristics compared with non-hazardous locations. Interestingly, statistically significant speed differences have been found in nearly all settings, confirming the notion that crash-prone zones exhibit distinct speed dynamics. These findings have significant implications for road safety policy and infrastructure design, as well as the need for location-specific speed management strategies.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:20:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719396</guid>
    </item>
    <item>
      <title>Early detection of traffic flow breakdown at highway bottleneck using section-based transitional instability</title>
      <link>https://trid.trb.org/View/2636212</link>
      <description><![CDATA[Traffic flow breakdowns refer to abrupt transitions from free-flow to congested conditions, typically occurring at freeway bottlenecks. Due to their sudden onset and the complex interactions driving them, early detection remains a challenging task in traffic operations. These events are often preceded by a transitional state—referred to as the pre-breakdown state—during which traffic conditions exhibit growing instability. Most existing studies define breakdowns using threshold-based criteria such as a sudden shift in speed at a single detector. Although such point-based methods can capture local changes, they often fail to reflect broader spatial interactions across adjacent sections. This study proposes a section-based framework that jointly analyzes upstream and downstream conditions to capture the spatial heterogeneity within a segment. A composite metric, the Dynamic Instability Score (DIS), is introduced to quantify sectional instability based on short-term temporal responsiveness and spatial imbalance. The methodology includes traffic state identification, DIS computation, and early warning detection. The DIS is calibrated using ROC-based thresholding at the boundary between free-flow and transitional states, ensuring sensitivity to early instability while minimizing false alarms. Empirical evaluations on freeway segments with recurrent bottlenecks demonstrate that DIS serves as a reliable leading indicator. Compared to point-based measures, it achieves higher accuracy and lower false-alarm rates, while preserving practical lead times. These results highlight the importance of capturing transitional instability during the pre-breakdown state and support the use of section-based indicators for proactive congestion management.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636212</guid>
    </item>
    <item>
      <title>Modeling Speed Distribution of Cars Moving on Two-lane Bidirectional Roads</title>
      <link>https://trid.trb.org/View/2647767</link>
      <description><![CDATA[Two-lane bidirectional roads are prevalent in many countries. Further, trucks generally travel at significantly lower speeds than passenger cars. On a two-lane bidirectional road, trucks act as moving obstacles for cars due to the limited scope of the overtaking maneuver. A car traveling at its desirable speed on such roads essentially meets either a downstream-moving truck or a platoon of cars following a downstream-moving truck. In such a set-up, the cars that are immediate followers of a truck need to complete an overtaking maneuver to again travel at their desired speed. Therefore, first the car which is the immediate follower of a truck needs to overtake the truck. However, the traffic flow in the opposite lane may restrict the overtaking process. Further, once the car which is the immediate follower of a truck completes the overtaking maneuver, the car which was earlier in the second place in the platoon of followers becomes the immediate follower of the truck, and this process continues. We propose a mechanism to model such a process that helps in formulating the process mathematically. To the best of our knowledge, such mathematical formulation is proposed for the first time. The proposed model helps to estimate the distribution of speed of cars on a section of a two-lane bidirectional road. The proposed model incorporates the distribution of desirable speed of cars, the variation in the number of cars moving between two consecutive trucks, and the flow rate of vehicles in the opposite lane. We validate the speed distribution of cars obtained from the model using the field data that were collected from a suitable site in India. The average speed of cars on a road stretch is considered an indicator of the quality of the road infrastructure. Therefore, the outcome of this study can help the policy makers to decide the upgradation of a two-lane bidirectional road from the traffic flow perspective.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647767</guid>
    </item>
    <item>
      <title>Relationship between Speed Distribution and Horizontal Alignment on Rural Four-Lane Highways</title>
      <link>https://trid.trb.org/View/2264216</link>
      <description><![CDATA[This study defines the roadway geometry, as a part of the review process for inducing alignment design reflecting the desires of drivers traveling on the roadway. From the standpoint of roadway design technician or road user, the most important thing is to review design consistency to know how well roadway alignment under specific design speed can meet service in terms of roadway function or if safe driving at a roadway point is possible. Current design review approach assumes that traffic safety is secured only by specifying minimum criteria of road design. However, this approach oversimplifies the desires of drivers so that it would be considered as a passive method due to meeting only minimum design criteria. From the results of this study, it is possible to confirm that design inconsistency would be found related to the relationship between horizontal alignment in a roadway segment and speed distributions with remarkably high speed variance.]]></description>
      <pubDate>Wed, 19 Feb 2025 17:14:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264216</guid>
    </item>
    <item>
      <title>Second-based queue length estimation with fusing MMW and low penetration rate CAV trajectory data</title>
      <link>https://trid.trb.org/View/2459160</link>
      <description><![CDATA[This paper combines millimeter-wave radar (MMW) data with connected autonomous vehicle (CAV) trajectory data to estimate queue length on a second-by-second basis. Firstly, queued vehicles on multiple lanes with the same traffic movement are mapped to a virtual lane. Then, the presence of CAV or human-driven vehicle (HDV) for any given queueing index is determined. A Bayesian joint probability model is subsequently established for queue length expectation, considering the existence of the queued vehicle type as a condition. The average time headway and dissipation speed distribution are derived from departure timestamps, which allows for the calculation of the prior probability ratio of queue length. Lastly, the maximum likelihood estimation (EM) algorithm is employed for iteratively estimating the CAV penetration rate. Simulation results demonstrate the method offers a compelling trade-off between precision and second-based real-time performance, while field test results further confirm the wide applicability under various traffic conditions.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2459160</guid>
    </item>
    <item>
      <title>Estimating Vehicle Turn-In Rate of Expressway Rest Areas via ETC Gantry Data – An ADPC-GMM Approach</title>
      <link>https://trid.trb.org/View/2458955</link>
      <description><![CDATA[Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2458955</guid>
    </item>
    <item>
      <title>A new car-following model with incorporation of Markkula's framework of sensorimotor control in sustained motion tasks</title>
      <link>https://trid.trb.org/View/2382203</link>
      <description><![CDATA[This study develops a car-following model called the “Markkula Intelligent Driver Model (MIDM)” based on Markkula's Framework of Sensorimotor Control. The MIDM predicts the start time of driver's reaction based on the evidence accumulation within Markkula's framework unlike the existing car-following models that use a constant reaction time parameter. The MIDM also accurately represents the actual shape and duration of acceleration maneuvers. Fifty drivers’ car-following behavior was observed in 2 different scenarios using a driving simulator – reaction to a decelerating lead vehicle and reaction to a stopped lead vehicle. Trajectory data from the NGSIM project were also used for the evaluation. Compared to the Gipps Model, the Wiedemann Model and the IDM, the MIDM realistically reproduced trajectories of speed, acceleration, jerk and spacing for both simulator and NGSIM data. The MIDM can also incorporate the effects of lead vehicle brake lights for more accurate estimation of the driver reaction time.]]></description>
      <pubDate>Fri, 14 Jun 2024 10:26:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2382203</guid>
    </item>
    <item>
      <title>An investigation of traffic speed distributions for uninterrupted flow at blackspot locations in a mixed traffic environment</title>
      <link>https://trid.trb.org/View/2365096</link>
      <description><![CDATA[Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.]]></description>
      <pubDate>Thu, 23 May 2024 09:41:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2365096</guid>
    </item>
    <item>
      <title>Modified two-fluid model of traffic flow</title>
      <link>https://trid.trb.org/View/2344887</link>
      <description><![CDATA[Researchers widely use the two-fluid model (TFM) to evaluate the performance of urban networks. However, the TFM is deterministic and does not capture the stochastic relation between speed and density. The present study develops a modified two-fluid model (MTFM). The variance function or the distribution of speed or travel time for a given density is incorporated using a percentile-based indicator, travel time uncertainty (TTU). The percentile-based indicators for the speed distribution are more robust than the variance or other moment-based indicators. The effect of TTU is incorporated using two parameters, 𝑎, and β. The applicability of the proposed MTFM is demonstrated using empirical data collected at the corridor and network levels. The TFM and MTFM were calibrated by formulating a nonlinear optimization problem. Based on the investigation using the corridor and network-level data, it was concluded that the MTFM showed better performance than the existing model. .]]></description>
      <pubDate>Thu, 29 Feb 2024 11:33:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2344887</guid>
    </item>
    <item>
      <title>Spacing-speed dependency on relative speeds to the adjacent lanes: a statistical test</title>
      <link>https://trid.trb.org/View/2338773</link>
      <description><![CDATA[The spacing of a vehicle is the spatial separation between this vehicle and the vehicle ahead. It forms the basis of the fundamental diagram of traffic flow. Traditional car-following models assume that the spacing of a following vehicle is strictly a function of the speed of the lead vehicle in the same lane. The recent literature, however, reports a phenomenon of spacing-speed dependency on the relative speeds to the adjacent lanes, termed speed differential effect. The aim of this paper is to statistically test the speed differential effect. To quantitatively measure the speed differential effect, the authors develop a mixture spacing-speed model to capture the probabilistic nature of the spacing and speed relationship, and then relate this spacing-speed relation to the relative speeds to the adjacent lanes. The authors apply the developed model to test the speed differential effect. The authors' empirical analysis provides strong statistical evidence to support the speed differential effect.]]></description>
      <pubDate>Mon, 26 Feb 2024 15:42:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2338773</guid>
    </item>
    <item>
      <title>Speed heterogeneity and accident reduction in mixed traffic</title>
      <link>https://trid.trb.org/View/2237085</link>
      <description><![CDATA[Various studies have investigated the relationship between speed and accidents using different definitions of speed variation. This research considers the speed in mixed traffic as heterogeneous based on the vehicle categories. This research aims to develop a traffic safety model with speed heterogeneity as expressed in accident modification factor (AMF) index. The data types include traffic data, road volumes and geometrics from 18 roads in 8 provinces in Indonesia: Central Sulawesi, Southeast Sulawesi, South Sulawesi, West Kalimantan, Central Kalimantan, NTB, NTT and Bali. The power model is adopted to model the relationship between speed changes and the number of accidents and victims. Change in paratransit speed is significant in predicting all types of AMFs, but the effects are lower than those of the other categories. Truck speed change has the highest impact of fatalities. A 10% decrease in truck speed results in a 29.9% decrease in the number of fatalities, whilst the same 10% decrease in paratransit decreases 17.4% of fatalities. The study resulted in AMF models based on the vehicle speed heterogeneity that could be used in road safety evaluation by looking at the effects of vehicle speed changes in specific categories.]]></description>
      <pubDate>Mon, 25 Sep 2023 14:46:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237085</guid>
    </item>
    <item>
      <title>Analyzing and Modeling of the Influence of Lateral Vehicles on the Speed of the Moving Vehicle</title>
      <link>https://trid.trb.org/View/2019022</link>
      <description><![CDATA[When a vehicle is moving, its speed is affected by other vehicles in its lateral position. To explore the influence of the lateral vehicle on the speed of the moving vehicle, 30 video cases were extracted and indicators like speed, acceleration, distance were obtained. A decision tree model was used to analyze the importance of factors which affect the speed change of the moving vehicle. The results show that lateral distance at the moment of decision-making is the most important factor. When the lateral distance is not greater than 0.77 m, the speed of moving vehicle would decrease. Otherwise, the speed of the moving vehicle would increase or remain unchanged. According to the frequency distribution histogram, the speed variation of the decelerated samples and accelerated samples are both 1–3 m/s. This research characterizes the speed variation of the moving vehicle because of the influence of the lateral vehicle and contributes to new ideas for studying microscopic driving behaviors.]]></description>
      <pubDate>Thu, 17 Nov 2022 10:15:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2019022</guid>
    </item>
    <item>
      <title>A data-driven, variable-speed model for the train timetable rescheduling problem</title>
      <link>https://trid.trb.org/View/1918113</link>
      <description><![CDATA[Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, the authors propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. The authors show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.]]></description>
      <pubDate>Mon, 28 Feb 2022 09:40:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1918113</guid>
    </item>
    <item>
      <title>Model on empirically calibrating stochastic traffic flow fundamental diagram</title>
      <link>https://trid.trb.org/View/1910873</link>
      <description><![CDATA[This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. The authors argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, the authors further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.]]></description>
      <pubDate>Tue, 22 Feb 2022 10:27:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1910873</guid>
    </item>
  </channel>
</rss>