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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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    <item>
      <title>Feasibility of Using Traffic Data for Winter Road Maintenance Performance Measurement</title>
      <link>https://trid.trb.org/View/2635954</link>
      <description><![CDATA[The research presented in this report is motivated by the need to develop an outcome based WRM performance measurement system with a specific focus on investigating the feasibility of inferring WRM performance from a traffic state. The research studied the impact of winter weather and road surface conditions (RSC) on the average traffic speed of rural highways with the intention of examining the feasibility of using traffic speeds from traffic sensors as an indicator of WRM performance. Detailed data on weather, RSC, and traffic over three winter seasons from 2008 to 2011 on rural highway sites in Iowa, US are used in this investigation. Three modelling techniques are applied and compared to model the relationship between traffic speed and various road weather and surface condition factors, including multivariate linear regression, artificial neural networks (ANN), and time series analysis. Multivariate linear regression models are compared by temporal aggregation (15 minutes vs. 60 minutes), types of highways (two-lane vs. four-lane), and model types (separated vs. combined). The research then examined the feasibility of estimating/classifying RSC based on traffic speed and winter weather factors using multi-layer logistic regression classification trees. The modelling results have confirmed the expected effects of weather variables including precipitation, temperature, and wind speed; it verified the statistically strong relationship between traffic speed and RSC, suggesting that speed could potentially be used as an indicator of bare pavement conditions and thus the performance of WRM operations. It is also confirmed that a time series model could be a valuable tool for predicting real-time traffic conditions based on weather forecast and planned maintenance operations, and that a multi-layer logistic regression classification tree model could be applied for estimating RSC on highways based on average traffic speed and weather conditions.]]></description>
      <pubDate>Mon, 02 Mar 2026 16:12:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635954</guid>
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    <item>
      <title>Scalable analysis of stop-and-go waves: Representation, measurements and insights</title>
      <link>https://trid.trb.org/View/2626071</link>
      <description><![CDATA[Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in these data. This paper makes a first step towards addressing this challenge by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. It enables the measurement of wave properties at scale. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. Our results show insights on the complexity of traffic waves. Traffic waves can bifurcate and merge at a scale that has never been observed or described before. The clustering analysis of all the identified wave components reveals the different topological structures of traffic waves. We explored that the wave merge or bifurcation points can be explained by spatial features. The gallery of all the identified wave topologies is demonstrated at https://trafficwaves.github.io/.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2626071</guid>
    </item>
    <item>
      <title>Modelling Trajectory Deviation to Improve Safety on Four-Lane Rural Highway Curves</title>
      <link>https://trid.trb.org/View/2648592</link>
      <description><![CDATA[Drivers adjust their trajectory and speed to safely navigate horizontal curves on multilane highways. Studies on two-lane highways show that curve geometry and operating speed influence vehicle trajectories and may contribute to run-off-road crashes. The present study investigated the combined influence of curve geometry and operating speed on mean trajectory deviation (TD_mean) for horizontal curves of a four-lane Indian rural highway. A naturalistic driving experiment was conducted on thirty-seven horizontal curves using an instrumented sedan car equipped with a GPS data logger. The selected curve sites were located on plain terrain (gradient ≤ 3%) and featured radius (from 250m to 1200m), uniform carriageway width, lane markings, shoulder and median widths, and smooth pavement. Thirty-four drivers were selected for the study based on age and experience, yielding 1,258 trajectory and speed profiles. After applying the free-flow inclusion criteria, 1,117 valid trajectory and speed profiles were retained for analysis. TD_mean was estimated for each trajectory profile by taking the difference between the maximum and minimum lateral placement (in meters) of the vehicle along a curve measured from the median edge. For each curve, the 85th percentile of minimum speed (Vc_min85), mean speed (Vc_mean85), and maximum speed (Vc_max85) were also estimated. Stepwise multiple linear regression analysis, conducted at a 95% confidence level, identified three significant predictors of TD_mean: Vc_min85 (coefficient (β) = –0.028, p = 0.014), radius (β = 0.002, p < 0.001), and deflection angle (β = 0.023, p < 0.001). The final model demonstrated an adjusted R² of 0.688. The results show that drivers exhibit greater trajectory deviation at lower operating speeds and on flatter curves with larger radius and deflection angles, consistent with previous studies. The developed model can be applied for evaluating geometric design consistency of horizontal curves on four-lane rural highways.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648592</guid>
    </item>
    <item>
      <title>Impact Analysis of Lane Change Behavior Based on Trajectory Data</title>
      <link>https://trid.trb.org/View/2613199</link>
      <description><![CDATA[The paper proposes a method to quantify the impact of lane changes based on causal inference. Using Zen traffic data, lane-changing and car-following trajectories are first extracted and then matched based on a causal inference framework. The correlation between the impact of lane change and the driving characteristics of surrounding vehicles and macro traffic flow states is analyzed. Additionally, the distributional characteristics of vehicle type and lane change type are analyzed for measuring different impact levels. The results show that lane change increases the average speed of the traffic within 100 m behind the original lane by about 1.47 km/h and decreases the average speed of the traffic behind the target lane by 0.58 km/h in the causal framework. The causal effect differs from the observed speed fluctuations before and after the lane change. Moreover, left lane change and large vehicle lane change improve traffic flow speeds.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613199</guid>
    </item>
    <item>
      <title>A Comprehensive Survey of the New Generation Pavement Structural Condition Assessment in Pavement Management System: Traffic Speed Deflection Device</title>
      <link>https://trid.trb.org/View/2591232</link>
      <description><![CDATA[The rapid development of the transportation industry has raised demands on highway maintenance, particularly highlighting the inefficiency of pavement structural performance evaluation based on fixed-point deflection detection. The traffic speed deflection devices (TSDD) can continuously collect pavement deflection data at normal driving speeds and have the advantages of minimal traffic disruption and realistic loading modes. In terms of motivation, the TSDD aligns with the development trends of the highway industry and data intelligence era. In terms of reliability, data comparisons between TSDD and falling weight deflectometer (FWD) show similar trends. However, the actual detection process is more complex, requiring analysis and adjustment of factors such as vehicle speed, pavement roughness, environment temperature, and reported data range. When the TSDD is running on the road, it requires a complete data analysis system, including powerful noise reduction algorithms, accurate deflection algorithms, reasonable deflection index models, and effective modulus inversion models. Finally, consideration should be given to integrating TSDD assessment into the pavement management system, focusing on the relationship between pavement structural and functional conditions, as well as balancing economic considerations and decision-making effectiveness. Given the complex issues, various aspects of TSDD research are systematically investigated in this review. The survey research aims to summarize the development process and current applications of TSDD, investigate the technical challenges and phased solutions in its applications, and evaluate its capability and practices in improving PMS. These analyses are expected to help relevant personnel deepen the understanding of this emerging technology and jointly promote its rapid development.]]></description>
      <pubDate>Thu, 19 Feb 2026 17:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591232</guid>
    </item>
    <item>
      <title>Incorporating Pavement Structural Capacity into TxDOT Pavement Management Information System</title>
      <link>https://trid.trb.org/View/2666837</link>
      <description><![CDATA[The research team will provide the Texas Department of Transportation (TxDOT) a means to use Traffic Speed Deflectometer (TSD) data to assess the structural condition of their roadways at the network-level by (a) leveraging TSD measurements and pavement data from existing databases in the US to complement the information collected in Texas for proposing and validating indices derived from velocity-based TSD measurements. To do this, the research team will develop a novel, velocity-based methodology for analyzing TSD data, as existing approaches rely on deflection-based methods not suited for the TSD, consider appropriate velocity indices and thresholds for classifying pavement structural condition, assess load transfer efficiency of jointed pavements, and ensure seamless integration of these data into PMIS.]]></description>
      <pubDate>Tue, 10 Feb 2026 14:45:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666837</guid>
    </item>
    <item>
      <title>Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting</title>
      <link>https://trid.trb.org/View/2616185</link>
      <description><![CDATA[Deep-learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions. However, such assumptions are often unrealistic for real-world traffic forecasting tasks, where the probabilistic distribution of spatiotemporal forecasting is very complex with strong concurrent correlations across both sensors and forecasting horizons in a time-varying manner. In this paper, we model the time-varying distribution for the matrix-variate error process as a dynamic mixture of zero-mean Gaussian distributions. To achieve efficiency, flexibility, and scalability, we parameterize each mixture component using a matrix normal distribution and allow the mixture weight to change and be predictable over time. The proposed method can be seamlessly integrated into existing deep-learning frameworks with only a few additional parameters to be learned. We evaluate the performance of the proposed method on a traffic speed forecasting task and find that our method not only improves model performance but also provides interpretable spatiotemporal correlation structures.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616185</guid>
    </item>
    <item>
      <title>Optimal dedicated lane management for mixed traffic with connected and autonomous vehicles accounting for heterogeneous headways and speeds</title>
      <link>https://trid.trb.org/View/2647871</link>
      <description><![CDATA[Connected and autonomous vehicle (CAV) platooning, where a group of CAVs travel closely together at higher speeds, has the potential to improve both traffic capacity and free-flow speed of mixed traffic on roads. In this paper, we present a dedicated lane management framework based on an analytical understanding of mixed traffic involving CAVs and human-driven vehicles (HDVs), taking into account diverse headways, free-flow speeds, and CAV penetration rates. This framework is a bi-criteria optimization that maximizes both traffic capacity and free-flow time-mean speed of a multi-lane section, where each lane can be a non-dedicated lane, a CAV-dedicated lane, or an HDV-dedicated lane. In the capacity-maximizing case, through using both types of dedicated lanes, our approach can consistently maximize capacity across various environmental settings, such as lane numbers, CAV rates, and car-following aggressiveness. The optimal dedicated lane management scheme is summarized as follows: implement HDV-dedicated lane(s) when the total CAV ratio is low, and introduce CAV-dedicated lane(s) otherwise. The scheme aims to consolidate CAVs as much as possible to maximize the number of platooning events. In the capacity-and-speed-maximizing case, CAV-dedicated lane(s) are introduced at lower CAV penetration rates compared to the capacity-maximizing case, with greater emphasis on speed, resulting in more complete separation between CAVs and HDVs. In the bi-criteria optimization, a Pareto solution set is found, illustrating the tradeoff between two objectives, which allows transportation planners flexibility in selecting lane management strategies in accordance with operational priorities. Finally, we validate the proposed framework through agent-based simulations in VISSIM, demonstrating its effectiveness.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647871</guid>
    </item>
    <item>
      <title>Assessing Road Network Performance through Zonal Resilience Metrics</title>
      <link>https://trid.trb.org/View/2658330</link>
      <description><![CDATA[Road networks are often subjected to disruptions caused by demand and capacity uncertainties, leading to excessive delays. A resilient transport system could absorb and recover quickly from such events. However, resiliency varies across different links, which may be because of zonal characteristics, network structure, or other factors. This study develops a methodology to quantify road network resilience at the zonal level and identify factors affecting it. Crowdsourced traffic speed data from approximately 33,000 locations was used to calculate resilience metrics, including the zonal resilience index, zonal vulnerability index, and zonal recoverability index. These metrics were modeled using geographically weighted regression to explore their relationship with independent variables. The results revealed that zonal trip heterogeneity, land use heterogeneity, and road category heterogeneity within a zone significantly reduce resilience. In contrast, connectivity measures, such as the clustering and degree assortativity coefficients, improve the recoverability of the zone. The increase in households owning more than two motorized vehicles in a zone reduces zonal resilience. The models were validated using subsets of the data, splitting weekdays from June 1–15 and June 16–30 and testing the model under different zone sizes. Results showed consistent variable effects across subsets and configurations, with slight variations in the significance of certain factors. Policymakers can utilize these insights to create land use or congestion pricing policies for individual zones to curb congestion. In addition, the network topology results can help plan a resilient road network for developing cities.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:19:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658330</guid>
    </item>
    <item>
      <title>Lab-based dynamic spectrum investigation of asphalt mixtures under various loading conditions using fast Fourier transform</title>
      <link>https://trid.trb.org/View/2643569</link>
      <description><![CDATA[Dynamic loading structures require dynamic analysis. Understanding how flexible pavement behaves under vehicle loads is essential. This study examines the dynamic behaviour of 5 different asphalt mixture designs, which include both dense gradation and open gradation. The investigation focuses on the response of these mixtures under three types of axle configuration loading (single, tandem, and tridem) at 5 various speeds. The vibration system in the lab records dynamic responses as a time-domain function, which is converted to a frequency-domain function using the Fast Fourier Transform (FFT). The results demonstrate the presence of random vibration at lower speeds, particularly following the inclusion of the second or third axle. This can lead to an approximately 33% increase in the dynamic response of the asphalt structure, resulting in increased stress on the pavement. This phenomenon is a crucial factor in wave interference, as the minimum amount of non-harmonic vibrations matches the lowest level of wave interference observed at the speed of 80 km/h. Moreover, it is observed that multi-axle configurations have less impact than a single axle as speed increases in the range of natural frequency of asphalt. Furthermore, bitumen penetration rate and void ratio have the highest impacts on wavelength maximum amplitude, respectively.]]></description>
      <pubDate>Sat, 17 Jan 2026 11:41:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643569</guid>
    </item>
    <item>
      <title>Evaluation of association between observed driving speeds and the occurrence of crashes using naturalistic driving study data</title>
      <link>https://trid.trb.org/View/2636599</link>
      <description><![CDATA[The SHRP2 Naturalistic Driving Study (NDS) data were used to investigate association between actual driving speeds before known crashes and at other times. Associations were evaluated for the same driver at a location where a crash occurred and similar locations where crashes did not occur, relative to the speeds of other drivers at those locations. It was found that an increase in the speed differential relative to other drivers at the same location between 6 and 10 s before a crash occurred was significantly associated with a crash occurring. The quantile of the average speed over that five-second period served as a better predictor than the quantile of the maximum speed. Crashes were also more associated with road locations classified as limited access highways, minor arterials, and major collectors. These findings are consistent across different drivers and types of road locations. The best-performing model classified all of the crashes in the dataset perfectly, and less than half of the cases classified as crashes were not crashes. This suggests an ability to identify conditions that are at least 50 percent likely to result in a crash. The results could be used by road agencies to identify observed vehicle speed variations that are likely to result in crashes, as well as by vehicle manufacturers to develop algorithms for identifying high-risk conditions for crashes considering speeds of other vehicles in the vicinity.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636599</guid>
    </item>
    <item>
      <title>Solution to data imbalance and complex interactions in traffic conflict modeling: A hypergraph and generative AI approach</title>
      <link>https://trid.trb.org/View/2636540</link>
      <description><![CDATA[Existing traffic conflict models face challenges in handling minority class samples and capturing dynamic interactions in complex traffic scenarios. These limitations hinder model generalization and real-world applicability. This study employs an enhanced Two-dimensional Time-to-collision (2D-TTC) metric combined with vehicle interaction relationships to predict traffic conflicts of multiple patterns. To address imbalance in conflict and non-conflict events, both undersampling and oversampling techniques are employed, while a generative adversarial network with self-attention layers is leveraged to overcome the shortcomings of oversampling methods. Indeed, this approach proved highly effective, elevating the model’s F1-score from 76.35% with undersampling alone to 94.21%. Additionally, several machine learning and deep learning models are compared, with the hypergraph attention network combined with Shapley additive explanations (S-HGAT) demonstrating the strongest learning capability. Furthermore, vehicle speed is identified as the most influential factor associated with traffic conflicts. A comprehensive re-evaluation of feature combinations reveals that the top six features—vehicle speed, the number of vehicles ahead, the standard deviation and the average of vehicle speeds within the traffic flow, distance with the road markings, and peak traffic hour indicators—result in the highest model F1-score of 98.41% and accuracy of 97.66%. Finally, the real-world implications of these findings are discussed.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636540</guid>
    </item>
    <item>
      <title>Impacts of vehicle restrictions on urban traffic speeds and transit ridership: an empirical analysis using high-frequency data</title>
      <link>https://trid.trb.org/View/2612505</link>
      <description><![CDATA[The impacts of vehicle restrictions in Santiago, Chile, on the average speeds of vehicle traffic and public buses as well as on transit card validations are quantified using novel high-frequency data, coming from millions of records from a ride-hailing service and the city’s public transport system. The restrictions were in force on weekdays during May through August between 7:30 am and 9 pm in urban districts, and applied on a given day to 20 % of the stock of vehicles registered before September 2011. The quality, frequency and spatial coverage of the data we use allow us to estimate not only classic methods like before-and-after and differences-in-differences, but also triple differences, which allows for a higher number of control variables. All these methods arrived at the conclusion that the restrictions produce small increases in speed vehicle traffic (between 3.3 % and 4.2 %) and bus speeds (between 1.8 % and 2.3 %); no increases in the use of public transport were detected. Three likely reasons for the size of the effects are the low percentage of vehicles subject to the restrictions on any given day (approximately 6.9 %), the tendency for frequent drivers to be from higher-income groups and thus to own newer vehicles, and widespread violation of the restrictions due to weak enforcement with low fines.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612505</guid>
    </item>
    <item>
      <title>Congestion and Travel Time Reliability Performance Measures for SHIFT 2026</title>
      <link>https://trid.trb.org/View/2642340</link>
      <description><![CDATA[Researchers obtained and processed probe speed data from HERE Technologies and integrated them with the Kentucky Transportation Cabinet’s highway inventory to develop congestion and travel time reliability performance metrics. This report summarizes analyses conducted using HERE speed data for 2022 – 2023. HERE data were processed using an established methodology, after which they were aligned with Kentucky’s highway inventory network. Analysis found that 2022 – 2023 HERE data offer significant improvements in coverage and quality over previous years. For locations that lacked sufficient data, researchers used HERE’s speed model to estimate hourly speeds. Data were used to calculate congestion and travel time reliability measures — including cost of delay and cost of unreliable travel time — that will inform the SHIFT 2026 prioritization process.]]></description>
      <pubDate>Mon, 12 Jan 2026 09:13:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642340</guid>
    </item>
    <item>
      <title>Integration and Deployment of Novel Tools for Rapid Assessment of Pavement Conditions and Remaining Life</title>
      <link>https://trid.trb.org/View/2642464</link>
      <description><![CDATA[Extreme heat events expose pavements to more intense and prolonged stress. These events, coupled with soil movement from heavy rainfall and drought also threaten pavement functional and structural life. State and local agencies have benefited from innovative technologies and tools developed to rapidly assess pavement condition, but data has not yet been widely implemented into pavement management systems that provide cohesive, wholistic assessments of state roadways. A hybrid approach combining traffic speed deflectometer (TSD) and air-coupled ground penetrating radar (GPR) is examined that will allow for large-scale data collection and analysis for use in state pavement management systems. The ideas presented in this study outline best practices for leveraging deflection data produced by TSD at the network- and project-levels, including merging TSD and GPR field data to enhance pavement engineers’ understanding of in-situ pavement structural conditions. These strategies were applied in the field and greatly assisted in identifying failed and at-risk pavements and prioritizing future projects. The remaining life estimation and material modulus backcalculation methods produced in this study will continue to be modified and improved to better match predictions generated by existing state practices.]]></description>
      <pubDate>Fri, 09 Jan 2026 16:58:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642464</guid>
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