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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>
    <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>Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/1596163</link>
      <description><![CDATA[The phenomenon of stop-and-go waves is frequently observed in congested traffic. With the development of connected and autonomous vehicle (CAV) technologies, it is possible to reduce traffic oscillation via control of CAVs in a mixed traffic flow with both human drivers and autonomous vehicles (AVs). This paper introduces a stochastic Lagrangian model which is capable of simulating stop-and-go traffic considering the heterogeneity of drivers. The sample paths of the stochastic process are smooth without aggressive oscillation. The model is further extended to the mixed traffic flow condition, considering stochastic human driving behavior and deterministic behavior of AVs. With the proposed model, the variation of performance of AV control strategies can be quantified in addition to the average performance. A numerical example with a single lane circular road is used to investigate the impact of the AV control strategy on mitigating stop-and-go waves. Both qualitative and quantitative results show that the phenomenon of stop-and-go waves can be reduced significantly with only one AV, while the increase of AVs from 10% (two AVs) to 50% (10 AVs) offers just marginal improvement in relation to the ensemble-averaged performance and 95% confidence interval of the ensemble-averaged performance. The proposed simulation approach based on the stochastic Lagrangian model can effectively investigate the impact of AV control strategies on traffic oscillation, considering in particular the uncertainty of human driver behavior.]]></description>
      <pubDate>Wed, 01 May 2019 10:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1596163</guid>
    </item>
    <item>
      <title>Analysis of Relationships in Traffic Oscillation Features with Field Experiments</title>
      <link>https://trid.trb.org/View/1573396</link>
      <description><![CDATA[Understanding of propagation mechanisms and impacts of traffic oscillation largely relies on the relationships between a number of oscillation features, such as shift time, period, speed variation, spacing, headway, etc. Despite numerous theoretical models, only limited field experiments have been conducted to investigate traffic oscillation propagation, and the relationships between traffic oscillation features have not received quantitative analysis. This study conducts a set of field experiments designed to inspect such relationships. In these experiments, 15 vehicles equipped with high-resolution GPS devices following one another on public roads with different speed limits, and the lead vehicle is asked to move with designed trajectory profiles incorporating various traffic oscillation parameters. Five set of feature measurements are extracted from processing the field vehicle trajectory data. A serious of linear regression analyses are conducted to investigate the relationships of these features. The analyses ravel a number of new findings on the relationships between the features. For example, the authors find that interestingly, the shift time between two consecutive trajectories is positively correlated with the average speeds of the preceding vehicle, and the spacing between them. The findings are helpful in constructing new microscopic traffic models better describing traffic oscillation dynamics. To illustrate this benefit, a revised IDM (Intelligent Driver Model) is proposed to capture the relationship between the shift time and the other features. Finally, the simulation results of IDM and revised IDM demonstrates that the revised IDM gives a better prediction accuracy than IDM.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1573396</guid>
    </item>
    <item>
      <title>Experimental study and modeling of car-following behavior under high speed situation</title>
      <link>https://trid.trb.org/View/1573694</link>
      <description><![CDATA[To investigate the car-following behavior under high speed driving conditions, the authors performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.]]></description>
      <pubDate>Wed, 19 Dec 2018 16:52:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1573694</guid>
    </item>
    <item>
      <title>Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways</title>
      <link>https://trid.trb.org/View/1470402</link>
      <description><![CDATA[Adaptive cruise control (ACC) has been considered one of the critical components of automated driving. ACC adjusts vehicle speeds automatically by measuring the status of the ego-vehicle and leading vehicle. Current commercial ACCs are designed to be comfortable and convenient driving systems. Little attention is paid to the safety impacts of ACC, especially in traffic oscillations when crash risks are the highest. The primary objective of this study was to evaluate the impacts of ACC parameter settings on rear-end collisions on freeways. First, the occurrence of a rear-end collision in a stop-and-go wave was analyzed. A car-following model in an integrated ACC was developed for a simulation analysis. The time-to-collision based factors were calculated as surrogate safety measures of the collision risk. The authors also evaluated different market penetration rates considering that the application of ACC will be a gradual process. The results showed that the safety impacts of ACC were largely affected by the parameters. Smaller time delays and larger time gaps improved safety performance, but inappropriate parameter settings increased the collision risks and caused traffic disturbances. A higher reduction of the collision risk was achieved as the ACC vehicle penetration rate increased, especially in the initial stage with penetration rates of less than 30%. This study also showed that in the initial stage, the combination of ACC and a variable speed limit achieved better safety improvements on congested freeways than each single technique.]]></description>
      <pubDate>Tue, 27 Jun 2017 16:15:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1470402</guid>
    </item>
    <item>
      <title>Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow</title>
      <link>https://trid.trb.org/View/1426812</link>
      <description><![CDATA[This paper firstly shows that a recent model (Tian et al., Transpn. Res. B 71, 138–157, 2015) is not able to replicate well the concave growth pattern of traffic oscillations (i.e., the standard deviation of speed is a concave function of the vehicle number in the platoon) observed from car following experiments. The authors propose an improved model by introducing a safe speed and the logistic function for the randomization probability. Simulations show that the improved model can reproduce well the metastable state, the spatiotemporal patterns, and the phase transitions of traffic flow. Calibration and validation results show that the concave growth pattern of oscillations and the empirical detector data can be simulated with a quantitative agreement.]]></description>
      <pubDate>Wed, 26 Oct 2016 14:44:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1426812</guid>
    </item>
    <item>
      <title>Empirical analysis and simulation of the concave growth pattern of traffic oscillations</title>
      <link>https://trid.trb.org/View/1426801</link>
      <description><![CDATA[This paper has investigated the growth pattern of traffic oscillations in the NGSIM vehicle trajectories data, via measuring the standard deviation of vehicle velocity involved in oscillations. The authors found that the standard deviation of the velocity increases in a concave way along vehicles in the oscillations. Moreover, all datasets collapse into a single concave curve, which indicates a universal evolution law of oscillations. A comparison with traffic experiment shows that the empirical and the experimental results are highly compatible and can be fitted by a single concave curve, which demonstrates that qualitatively the growth pattern of oscillations is not affected by type of bottleneck and lane changing behavior. The authors have shown theoretically that small disturbance with an angular frequency ω increases in a convex way in the initial stage in the traditional models presuming a unique relationship between speed and density, which obviously deviates from the authors' findings. Simulations show that stochastic models in which the traffic state dynamically spans a 2D region in the speed-spacing plane can qualitatively or even quantitatively reproduce the concave growth pattern of traffic oscillations.]]></description>
      <pubDate>Wed, 26 Oct 2016 14:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1426801</guid>
    </item>
    <item>
      <title>Calibration of nonlinear car-following laws for traffic oscillation prediction</title>
      <link>https://trid.trb.org/View/1417515</link>
      <description><![CDATA[Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver’s car-following behavior but also the vehicle trajectory’s time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). The authors use Newell’s car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.]]></description>
      <pubDate>Mon, 29 Aug 2016 11:12:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1417515</guid>
    </item>
    <item>
      <title>Impact of Traffic State Transition and Oscillation on Highway Performance with Section-Based Approach</title>
      <link>https://trid.trb.org/View/1406215</link>
      <description><![CDATA[To investigate the impact of traffic state transition and oscillation on highway performance, this paper develops a section-based identification methodology to classify traffic state into stationary (FF, BN, CT, and BQ), transitional and oscillatory traffic using point measurement data. Firstly, the section-based perspective presents the asymmetry in the intensity of two different transition paths (breakdown and recovery) resulting inequality in the capacity and recovery rate (section-hysteresis). It is found that there exists decreasing tendency of transition intensity according to the number of lanes and the positive relationship between the intensity of transition and the amount of capacity loss. Moreover, accompanied by traffic oscillations, a series of capacity losses suggests the negative impact of oscillations on traffic dynamics triggering subsequent performance degradations. In addition, one observes the shrinkage of incoming flow to the queue. The findings emphasize the adverse impact of traffic state transition and guide one to settle the management strategy by providing statistical estimations for main highway performance.]]></description>
      <pubDate>Wed, 25 May 2016 16:06:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406215</guid>
    </item>
    <item>
      <title>An empirical study on the traffic state evolution and stop-and-go traffic development on freeways</title>
      <link>https://trid.trb.org/View/1375459</link>
      <description><![CDATA[Stop-and-go traffic, related to traffic breakdown and instability, is the core mechanism of traffic state transition to congestion. The purpose of this paper is to provide improved empirical understanding on the development of the stop-and-go traffic from asymmetric theory's viewpoint. The authors analyse traffic state transition before and after stop-and-go wave by observing platoon trajectories using Next Generation Simulation (NGSIM) data. The authors observe that stop-and-go wave develops into growth (dissipation) in unstable (stable) traffic, which becomes stable (unstable) by passing the wave, respectively. Alternating patterns can explain recurring oscillatory traffic. Besides, the authors investigate the relationship between the evolution of stop-and-go wave and lane-changes. The authors can find that the growth region in the flow-density plane is located on the higher flow and density area above dissipation region. Also, as the number of lane-change increases (decreases), traffic will be developed to growth (dissipation), respectively. The findings of this research have potential for detailed traffic control over stop-and-go waves’ development.]]></description>
      <pubDate>Wed, 23 Dec 2015 08:12:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1375459</guid>
    </item>
    <item>
      <title>A simple nonparametric car-following model driven by field data</title>
      <link>https://trid.trb.org/View/1368074</link>
      <description><![CDATA[Car-following models are always of great interest of traffic engineers and researchers. In the age of mass data, this paper proposes a nonparametric car-following model driven by field data. Different from most of the existing car-following models, neither driver’s behaviour parameters nor fundamental diagrams are assumed in the data-driven model. The model is proposed based on the simple k-nearest neighbour, which outputs the average of the most similar cases, i.e., the most likely driving behaviour under the current circumstance. The inputs and outputs are selected, and the determination of the only parameter k is introduced. Three simulation scenarios are conducted to test the model. The first scenario is to simulate platoons following real leaders, where traffic waves with constant speed and the detailed trajectories are observed to be consistent with the empirical data. Driver’s rubbernecking behaviour and driving errors are simulated in the second and third scenarios, respectively. The time–space diagrams of the simulated trajectories are presented and explicitly analysed. It is demonstrated that the model is able to well replicate periodic traffic oscillations from the precursor stage to the decay stage. Without making any assumption, the fundamental diagrams for the simulated scenario coincide with the empirical fundamental diagrams. These all validate that the model can well reproduce the traffic characteristics contained by the field data. The nonparametric car-following model exhibits traffic dynamics in a simple and parsimonious manner.]]></description>
      <pubDate>Fri, 25 Sep 2015 16:28:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1368074</guid>
    </item>
    <item>
      <title>Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns</title>
      <link>https://trid.trb.org/View/1368069</link>
      <description><![CDATA[This paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To this aim, a “traffic-informed” methodology is proposed to restore physical and platoon integrity of trajectories in a finite time–space domain, and it is applied to one Next Generation Simulation (NGSIM) I80 dataset. However, as the actual trajectories are unknown, it is not possible to verify directly whether the reconstructed trajectories are really “nearer” to the actual unknowns than the original measurements. Therefore, a simulation-based validation framework is proposed, that is also able to verify indirectly the efficacy of the reconstruction methodology. The framework exploits the main feature of NGSIM-like data that is the concurrent view of individual driving behaviors and emerging macroscopic traffic patterns. It allows showing that, at the scale of individual models, the accuracy of trajectories affects the distribution and the correlation structure of lane-changing model parameters (i.e. drivers heterogeneity), while it has very little impact on car-following calibration. At the scale of traffic simulation, when models interact in trace-driven simulation of the I80 scenario (multi-lane heterogeneous traffic), their ability to reproduce the observed macroscopic congested patterns is sensibly higher when model parameters from reconstructed trajectories are applied. These results are mainly due to lane changing, and are also the sought indirect validation of the proposed data reconstruction methodology.]]></description>
      <pubDate>Fri, 25 Sep 2015 16:28:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1368069</guid>
    </item>
    <item>
      <title>Detection of urban traffic flow's synchronized oscillations using spectral envelope</title>
      <link>https://trid.trb.org/View/1365540</link>
      <description><![CDATA[This paper tries to answer an interesting question: Is there any synchronized oscillation in network-wide urban traffic flow? Particularly, the authors apply the spectral envelope method to extract the common oscillation frequencies of a set of traffic flow time series collected from several detectors distributed on a two dimensional (2D) urban traffic network. Results show that several local synchronized oscillations are detected, which are mainly distributed surrounding an intersection or on a section between neighboring junctions. However, global synchronized oscillations are hardly observed due to the truncation effect of junctions. The proposed method is also applied to the simulation data set from Paramics. The results consist with the conclusions drawn from the detected data set.]]></description>
      <pubDate>Wed, 26 Aug 2015 10:54:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1365540</guid>
    </item>
    <item>
      <title>A Describing Function Method for Traffic Oscillation Analysis: Theoretical Properties</title>
      <link>https://trid.trb.org/View/1338184</link>
      <description><![CDATA[The describing-function (DF) based method proposed by Li and Ouyang is a novel framework that analytically quantifies oscillation characteristics for a general class of nonlinear car-following laws. The DF approach was developed to predict traffic oscillation propagation across a platoon of vehicles following each other by a nonlinear car-following law with only the leading vehicle’s input. This study aims to analyze theoretical properties of DF for traffic oscillation quantification. The authors simplify the DF approach and prove a set of properties, e.g. existence and uniqueness of its solution, that assure its prediction is always consistent with observed traffic oscillation patterns. In addition, the authors will show that propagation of traffic oscillation magnitude in a platoon of vehicles is uniquely determined by their underlying nonlinear car-following models, regardless of the initial leading vehicle’s perturbation pattern. The authors also prove that the oscillation amplitude always amplifies to a unique asymptotic plateau if the car-following model is asymptotically unstable or diminishes to zero otherwise.]]></description>
      <pubDate>Tue, 24 Mar 2015 15:34:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1338184</guid>
    </item>
    <item>
      <title>A Describing Function Method for Traffic Oscillation Analysis: Environmental Impacts and Oscillation Mitigation</title>
      <link>https://trid.trb.org/View/1338267</link>
      <description><![CDATA[Traffic oscillation incurs a number of adverse impacts to highway traffic efficiency and sustainability such as excessive travel delay, extra fuel consumption and emission. This study first adapts the describing-function (DF) based method for estimating fuel consumption and emission emerged from traffic oscillation. The authors integrate the DF approach with existing estimation models of fuel consumption and emission to analytically predict environmental impacts (i.e., unit-distance fuel consumption and emission) from traffic oscillation. The prediction results by the DF approach are validated with both computer simulation and field measurements. Further, the authors explore how to utilize advantageous features of emerging sensing, communication and control technologies, such as fast response and information sharing, to smooth traffic oscillation and reduce its environmental impacts. The authors extend the studied car-following law to incorporate these features and apply the DF approach to demonstrate how these features can help dampen the growth of oscillation and environmental impact measurements. For information sharing, the authors convert the corresponding extended car-following law into a new fixed point problem and propose a simple bisecting based algorithm to efficiently solve it. Numerical experiments show that these new car-following control strategies can effectively suppress development of oscillation amplitude and consequently mitigate fuel consumption and emission.]]></description>
      <pubDate>Tue, 24 Mar 2015 11:27:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1338267</guid>
    </item>
    <item>
      <title>A parsimonious model for the formation of oscillations in car-following models</title>
      <link>https://trid.trb.org/View/1331698</link>
      <description><![CDATA[This paper shows that the formation and propagation of traffic oscillations in the absence of lane changes can be explained by the stochastic nature of drivers’ acceleration processes. By adding a white noise to drivers’ desired acceleration in free-flow, oscillations are produced that accord well with observation. This theory suggests that driver error is a function of roadway geometry, that it determines the average speed at the bottleneck, as well as oscillation period and amplitude. The model has been implemented with a single additional parameter compared to the kinematic wave model with bounded accelerations.]]></description>
      <pubDate>Tue, 02 Dec 2014 09:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1331698</guid>
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