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    <title>Transport Research International Documentation (TRID)</title>
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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 Spatial-Temporal Analysis of Lane-Changing Behavior Characteristics Using NGSIM Data</title>
      <link>https://trid.trb.org/View/2237679</link>
      <description><![CDATA[This study aims to characterize the lane-changing behavior in a spatial-temporal manner using the Next Generation Simulation (NGSIM) data, and to reveal its association with lane-changing safety. The lane-changing duration and distance of vehicles, as well as the relative speed and distance between the vehicles before and after lane-changing, were calculated and analyzed. Results showed that the lane-changing duration was mostly concentrated in 5−30 s, and lane-changing distance was largely concentrated in 0−4 m; the relative speed difference between the ego vehicle and preceding vehicle was greater than between the ego vehicle and the following vehicle both before and after lane-changing; and the distance difference was much smaller between the ego vehicle and the preceding vehicle. These findings suggest that the interaction between the ego vehicle and the preceding vehicle is comparatively more unstable than that of the following vehicle before and after lane-changing, which is more likely to lead to conflicts.]]></description>
      <pubDate>Thu, 01 Feb 2024 10:13:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237679</guid>
    </item>
    <item>
      <title>Trajectory Guidance for Connected Human-Driving Vehicles through the Interactions between Drivers and Roadside Units</title>
      <link>https://trid.trb.org/View/2244303</link>
      <description><![CDATA[Utilizing massive real-time traffic information in the vehicle-to-everything (V2X) environment, road traffic systems can be enhanced by optimizing vehicle trajectory patterns. Because intelligent decisions can be made by roadside units (RSUs) with multiaccess edge computing (MEC) devices, this paper presents a trajectory guidance method for connected human-driving vehicles (CHVs) based on human–RSU interactions. Optimal guidance commands were determined based on a trajectory predictive control method, helping drivers operate the vehicles to follow the expected trajectories. The authors utilized the Gaussian mixture model to analyze the naturalistic driving data set collected by the project of the Next Generation Simulation (NGSIM) and determine the acceleration distributions of different guidance commands, including decelerate rapidly, decelerate slowly, keep velocity, accelerate slowly, and accelerate rapidly. The Monte Carlo sampling method was used to simulate different acceleration choices for command-based guidance information, considering human driver uncertainty. Sensitivity analysis was conducted to evaluate the performance of the proposed trajectory guidance method with different parameters. Experimental results showed that the average trajectory deviations at all positions are less than 5 m, indicating that guidance performance with reasonable guidance parameters is acceptable. Therefore, the proposed trajectory guidance method by human–RSU interaction can effectively support CHVs participating in V2X cooperation and has good practical application prospects.]]></description>
      <pubDate>Mon, 16 Oct 2023 17:26:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2244303</guid>
    </item>
    <item>
      <title>A Novel Lane-Changing Recognition Method Using Frequency Analysis</title>
      <link>https://trid.trb.org/View/2072039</link>
      <description><![CDATA[Lane-changing recognition is an important task for advanced driver assistance systems, but is heavily challenged by poor driving habits, such as turning without using turn signals. To address this problem in this study, a lane-changing recognition method using frequency analysis was proposed. First, highest-frequency–based and frequency-bands–based methods were employed to evaluate the three behaviors of left lane changing, lane keeping, and right lane changing. To improve the recognition accuracy, the two methods were fused according to their classification advantages for different behaviors. The fused method was verified by lateral position data incorporating lane features that were manually extracted and annotated from the Next-Generation Simulation dataset. The frequency analysis framework achieved recognition accuracy of 91.8%, 97.4%, and 99.1% in 2, 1, and 0 s, respectively, before the vehicle crossed the lane line, which were significant improvements over the time-domain analysis methods. The proposed method was also validated by real-world road data with promising results.]]></description>
      <pubDate>Tue, 20 Dec 2022 09:12:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2072039</guid>
    </item>
    <item>
      <title>Analysis of Factors Affecting Discretionary Lane Change</title>
      <link>https://trid.trb.org/View/2015342</link>
      <description><![CDATA[Discretionary lane changing (DLC) decision on freeways is influenced by several factors and varies across vehicle classes. This research aims to investigate the role of vehicle attributes (e.g., length, width) and flow characteristics (e.g., headways and lead-lag gaps) on the DLC decision making process. The authors analyzed the vehicle trajectory data extracted from the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) program for the Interstate 80. The data were collected during rush hours (between 4:00 p.m. and 4:15 p.m. on April 13, 2005) on a segment of Interstate 80 in Emeryville, San Francisco, California. The data set contains only the discretionary lane change that made by autos and trucks only. As a result, two lanes out of six lanes were used for the purpose of this research. The logistic stepwise selection procedure was applied to estimate statistically significant predictor variables that contribute to increasing the likelihood of DLC. The dependent variable was whether discretionary lane change was executed or not. Nine explanatory variables were included to find out the associated factors for DLC. The logistic regression model identified three statistically significant predictor variables out of seven independent variables considered in the current research. The predictors that increase the probability of discretionary lane change are distance between vehicles (space headway) on the original lane, distance between subject vehicle and vehicle on the target lane (lead gap), vehicle class (auto and truck). Overall, the model results provide an overview of factors associated with discretionary lane change that could be addressed when providing highway safety improvement.]]></description>
      <pubDate>Mon, 24 Oct 2022 10:22:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2015342</guid>
    </item>
    <item>
      <title>Fault tolerance analysis of an adaptive neuro-fuzzy inference system for mandatory 4 lane changing decisions in automated driving</title>
      <link>https://trid.trb.org/View/1977523</link>
      <description><![CDATA[Past research has developed a binary decision model for mandatory lane changes based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). This ANFIS Decision Model (simply called ADEM), developed and tested with the Next Generation Simulation (NGSIM) data, mimics the sensory inputs and decisions of human drivers. This research assumed that ADEM will be implemented as part of the automated lane changing system in Automated Vehicles (AVs). The system in AVs will depend on active radar sensors to make measurements. The sensor outputs will be converted into the input parameter values of ADEM. This research tested ADEM’s performance when the sensors could only measure the distance of surrounding vehicles within 50m, and when one of the sensors malfunctions. The original NGSIM test data set was modified to simulate the sensors’ detection range limit in Scenario 0, plus six other scenarios in which each sensor took turns to fail and assumed either the minimum or maximum possible output values. The results show that: (i) ADEM performs in a safer manner when considering the sensors’ limited detection range; (ii) the minimum value of 0m should be used as the default sensor output when a sensor fails, so that ADEM makes safer mandatory lane changing decisions; and (iii) the most critical sensors, by which failure of any of them would cause the greatest degradation to ADEM’s performance, are the two sensors that measure the distances to the preceding and the following vehicles in the target lane.]]></description>
      <pubDate>Fri, 22 Jul 2022 14:18:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1977523</guid>
    </item>
    <item>
      <title>Vehicle Interaction Behavior Prediction with Self-Attention</title>
      <link>https://trid.trb.org/View/1905228</link>
      <description><![CDATA[The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. The authors propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.]]></description>
      <pubDate>Mon, 24 Jan 2022 17:24:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1905228</guid>
    </item>
    <item>
      <title>Evaluation of Methods for Modeling Vehicle Activity at Signalized Intersections for Air Quality Hot-Spot Analyses, Final Report</title>
      <link>https://trid.trb.org/View/1858266</link>
      <description><![CDATA[This report summarizes an evaluation undertaken to compare methods of representing vehicle activity at signalized intersections  for  use  in  project  scale  air  quality  analysis.  This  evaluation  is intended  to advance  the  state  of  the practice  for  emissions  and  dispersion  analysis  at  signalized  intersections,  which  will  improve  the  accuracy  of pollutant concentration estimates.  The  Next Generation  Simulation (NGSIM) Lankershim Blvd. dataset  was the basis for a detailed baseline that was compared to other methods that are more practical to implement. This Phase 2 report includes information on data inputs, modeling, method comparison, results, and recommendations based on two intersections of NGSIM data in Los Angeles, California. The target audience is transportation agency technical staff undertaking project-level air quality analyses.]]></description>
      <pubDate>Wed, 14 Jul 2021 13:43:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1858266</guid>
    </item>
    <item>
      <title>Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model</title>
      <link>https://trid.trb.org/View/1736506</link>
      <description><![CDATA[This paper analyzes the impact of uphill grades on the acceleration drivers choose to impose on their vehicles. Statistical inference is made based on the maximum likelihood estimation of a two-regime stochastic car-following model using Next Generation SIMulation (NGSIM) data. Previous models assume that the loss in acceleration on uphill grades is given by the effects of gravity. We find evidence that this is not the case for car drivers, who tend to overcome half of the gravitational effects by using more engine power. Truck drivers only compensate for 5% of the loss, possibly because of limited engine power. This indicates not only that current models are severely overestimating the operational impacts that uphill grades have on regular vehicles, but also underestimating their environmental impacts. We also find that car-following model parameters are significantly different among shoulder, median and middle lanes but more data is needed to understand clearly why this happens.]]></description>
      <pubDate>Wed, 09 Sep 2020 18:05:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1736506</guid>
    </item>
    <item>
      <title>Modeling Individual Travel Time with Back Propagation Neural Network Approach for Advanced Traveler Information Systems</title>
      <link>https://trid.trb.org/View/1697830</link>
      <description><![CDATA[The heterogeneous driving behaviors from different travelers are not considered in current advanced traveler information systems (ATIS) such as Google Maps and 511 systems, which leads the systems to generate the same travel time for everyone who inputs the same origin and destination. This paper explores the modeling of individualized travel time based on the individual behavior of each driver as opposed to average traffic information, with the ultimate goal of enabling individualized traffic information provision for the ATIS and subsequently reducing travel-time prediction errors. A back propagation neural network model was built to quantitatively estimate the driving behavior differences (i.e., the delta) between individual drivers and the surrounding traffic, with both roadway geometrics and dynamic traffic conditions considered in the modeling process. A travel-time estimation algorithm is then proposed to derive link-level traffic information that considers individual behavioral difference. Finally, individualized route travel time is computed for each traveler based on the derived link-level traffic information and individual behavioral difference. The proposed model is implemented and tested on an open-source Next Generation Simulation (NGSIM) dataset, which demonstrated the feasibility and effectiveness of the proposed model. The proposed model has the potential of being directly applied to enhance existing ATIS travel-time prediction accuracies.]]></description>
      <pubDate>Tue, 26 May 2020 10:42:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1697830</guid>
    </item>
    <item>
      <title>Applying Derived Distribution Method to Microlevel Driving Behavior Characteristics to Quantify Uncertainties in Traffic Stream Flow and Density</title>
      <link>https://trid.trb.org/View/1671415</link>
      <description><![CDATA[The flows and densities of traffic streams play an important role in defining the performance of roadways and corresponding improvement strategies. Traffic flows and densities are the outcome of complex psychophysical actions of drivers. Actions performed by the drivers while driving can be quantified in terms of the headway and/or spacing that they maintain with respect to the vehicles they follow. However, the inherent randomness that exists in human driving behaviors results in random headway and spacing, which leads to uncertainties in predicted traffic flows and densities. As a result, it is important to quantify these uncertainties, because they play an important role in proposing improvement strategies. In this study, a derived distribution method–based uncertainty quantification of traffic flows and densities is proposed; it involves the modification of deterministic flow–headway and density–spacing relationships into probabilistic ones. Analytical expressions were derived for the probability distributions of flows and densities, given the headway and spacing distributions, respectively, which are conditional on velocities. The estimation of the distribution parameters and the validation of the proposed approach were carried out using the Next Generation Simulation (NGSIM) trajectory dataset. The results indicated that the proposed analytical distribution models represented empirical field observations quite accurately.]]></description>
      <pubDate>Tue, 28 Jan 2020 09:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1671415</guid>
    </item>
    <item>
      <title>A longitudinal scanline based vehicle trajectory reconstruction method for high-angle traffic video</title>
      <link>https://trid.trb.org/View/1606034</link>
      <description><![CDATA[In this paper, a robust and efficient High-angle Spatial-Temporal Diagram Analysis (HASDA) model is built to reconstruct high-resolution vehicle trajectories from infrastructure traffic surveillance videos. A combined methodology was developed, comprising of scanline-based trajectory extraction and feature-matching coordinate transformation. A scanline-based trajectory extraction technique is introduced to separate vehicle strands from pavement background on the spatial-temporal diagram by considering color features, gradient features, and motion features. Particular cleaning algorithms for removing static object noises, shadows, and occlusions are also established. Feature-matching coordinate transformation converts the pixel coordinates to the real-world coordinates to generate the physical vehicle trajectory. To evaluate the algorithm, generated trajectory results were compared to the reconstructed version of the Next Generation Simulation (NGSIM) dataset. 15-min NGSIM video was divided into a 5-min dataset for the calibration and the remaining 10-min data for evaluation. Model parameters calibrated based on the 5-min video data are then applied to the 10-min testing data. Two levels of performance measurements are considered to evaluate both trajectory-level and point-level results. A reference algorithm based on mainstream motion-based detection and tracking methods are used as a baseline algorithm. Based on the evaluation results, the proposed method shows promising trajectory detection results, that on average more than 90% of vehicle trajectories are constructed by the proposed methods from the NGSIM videos. The HASDA model outperforms the reference algorithm and shows superior transferability in the training-testing experiment. Further work needs to be done to improve the algorithm performance against shadows and occlusions by incorporating more intelligent and advanced techniques.]]></description>
      <pubDate>Thu, 23 May 2019 09:55:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1606034</guid>
    </item>
    <item>
      <title>A Critical Evaluation of the Next Generation Simulation (NGSIM) Vehicle Trajectory Dataset - Abridged</title>
      <link>https://trid.trb.org/View/1572863</link>
      <description><![CDATA[A clear understanding of car following behavior and microscopic relationships is critical for advancing traffic flow theory. Without empirical microscopic data, plausible but incorrect hypotheses perpetuate in the vacuum. The Next Generation Simulation (NGSIM) project was undertaken to collect such data and the NGSIM data set has become the de facto standard, underlying the vast majority of empirically based advances of the past decade. But there has been a growing minority of researchers who have found unrealistic relationships in the NGSIM data. To date, the critical findings have almost exclusively come from the existing NGSIM database itself. Unfortunately, as this paper shows, the NGSIM errors are beyond anything that could be corrected strictly through cleaning or interpolation of the reported NGSIM data. This paper takes the deepest evaluation yet of the NGSIM data. This research manually re-extracts the vehicle trajectories from a portion of the original NGSIM video to explicitly quantify NGSIM errors, e.g., piecewise constant speeds punctuated by brief periods of large acceleration exhibited by the NGSIM data were not evident in the newly extracted trajectories. This point is particularly troublesome for applications that rely on acceleration, e.g., most car following models. The magnitude of errors exhibit a dependency on speed, location and vehicle length. Examples are shown where a real vehicle stopped but the NGSIM trajectory does not and then overruns the location of the real leader. Needless to say, the re-extracted trajectories showed much cleaner speed-spacing relationships than the corresponding raw NGSIM trajectories.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1572863</guid>
    </item>
    <item>
      <title>A pattern recognition algorithm for assessing trajectory completeness</title>
      <link>https://trid.trb.org/View/1563689</link>
      <description><![CDATA[Vehicular trajectories are widely used for car-following (CF) model calibration and validation, as they embody characteristics of individual driving behaviour (each trajectory reflects an individual driver). Previous studies have highlighted that the trajectories should contain all the major vehicular interactions (driving regimes) between the leader and the follower for reliable CF model calibration and validation. Based on Dynamic Time Warping and Bottom-Up algorithms, this paper develops a pattern recognition algorithm for vehicle trajectories (PRAVT) to objectively, accurately, and automatically differentiate different driving regimes in a trajectory and then select the most complete trajectories (i.e. trajectories containing a maximum number of regimes). PRAVT is rigorously tested using synthetic data and then applied to the NGSIM data. The authors have observed that the NGSIM data are dominated by the trajectories which contain only three regimes, namely acceleration, deceleration, and following, 77% of the trajectories lack the standstill regime, and no trajectory in the NGSIM data is complete. These findings’ impact on how to properly utilize NGSIM data can be profound. Given the extensive use of the NGSIM data in the traffic flow community, this paper also provides insights about the types of regimes contained in each trajectory of the NGSIM data.]]></description>
      <pubDate>Wed, 24 Oct 2018 11:17:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1563689</guid>
    </item>
    <item>
      <title>Comparisons of discretionary lane changing behavior: implications for autonomous vehicles</title>
      <link>https://trid.trb.org/View/1516842</link>
      <description><![CDATA[This  article  presents  research  on  the  statistical  properties  of  four  parameters  that  affect  a  driver’s  lane-changing  decision,  using  data  from  the  Next  Generation  SIMulation  database.  The  results  show  that there  is  statistical  evidence  to  indicate  that  the  population  averages  for  each  parameter  differ  based  on  time of day and that the  gap  parameters  are  best  described  by  the  log-normal  distribution.  This  implies  that  autonomous  vehicles  should  be  programmed  to  behave  differently  at  different  times  of  the  day.]]></description>
      <pubDate>Mon, 25 Jun 2018 09:40:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1516842</guid>
    </item>
    <item>
      <title>A vehicle type-dependent visual imaging model for analysing the heterogeneous car-following dynamics</title>
      <link>https://trid.trb.org/View/1509282</link>
      <description><![CDATA[Heterogeneity is an essential characteristic in car-following behaviours, which can be defined as the differences between the car-following behaviours of driver/vehicle combination under comparable conditions. This paper proposes a visual imaging model (VIM) with relaxed assumption on (1) a driver's perfect perception for the states of the neighbouring vehicles (e.g. spacing, velocity, etc.) and (2) uniform reaction to vehicles with different sizes in most existing car-following models. VIM utilises the visual imaging information subtended by the preceding vehicle as the stimuli drivers react to, and can generate greater stimuli from the preceding vehicle with larger apparent size (i.e. vehicle width × vehicle height) under short gap distance with the follower, but less change in stimuli from the distant leading vehicle under various apparent sizes. The NGSIM data containing vehicle type/size information is used to evaluate VIM at different levels. At the level of single trajectory pair, the calibrated VIM occupies the well capability of reproducing the trajectory of the follower, and can also reproduce statistical results from the field data, that is, the gap distance for car-following truck (C-T) is greater than that for car-following car (C-C). At the level of vehicle type, the calibration results also show the promising performance of VIM in describing heterogeneous car-following behaviours with the simple model formulation and limited model parameters compared with other six reference models.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509282</guid>
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