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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data</title>
      <link>https://trid.trb.org/View/1467590</link>
      <description><![CDATA[With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.]]></description>
      <pubDate>Tue, 30 May 2017 08:23:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1467590</guid>
    </item>
    <item>
      <title>A study on multi-resolution scheme of macroscopic-microscopic traffic simulation model</title>
      <link>https://trid.trb.org/View/1354587</link>
      <description><![CDATA[Multi-Resolution traffic flow schemes, coupling a microscopic (vehicle based), mesoscopic (cell based) and macroscopic (flow based) representations of traffic flow may be a useful tool to better understand and utilize the relationships between the various types of representation. The paper proposes a new classification of traffic model which is according to the representation scale and the behavioural law firstly. In addition, it shows that the Lighthill-Whitham-Richards (LWR) model which is a macroscopic continuum model can be numerically solved in Weighted Essentially Non-Oscillatory (WENO) scheme. Furthermore, the paper mainly studies the hybrid scheme which can couple a macroscopic model (LWR) and a microscopic model (Wiedemann). Simple transformation methods are analyzed to translate the variables parameters between both models. Finally, simulation results show that two shock waves (stopping-starting wave) can propagate along two models differently described. The hybrid scheme can keep the result consistency between the both models.]]></description>
      <pubDate>Sat, 13 Jun 2015 15:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1354587</guid>
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    <item>
      <title>Real-time freeway traffic state prediction: A particle filter approach</title>
      <link>https://trid.trb.org/View/1354554</link>
      <description><![CDATA[The research presented in this paper develops a multi-step traffic state prediction algorithm using spot speed measurements. The traditional Lighthill-Whitham-Richards (LWR) flow continuity equation is combined with the Van Aerde traffic stream model to generate a new partial differential equation (PDE) named the Van Aerde flow continuity model. The numerical solution of the PDE is obtained using the Godunov discretization scheme to generate a time series equation that characterizes the temporal and spatial relationship of traffic speed data. Because of the strong nonlinearity of the discretized speed update equation, a robust particle filter is applied to conduct a multi-step speed prediction using speed measurements. The prediction accuracy of the proposed approach is compared to the state-of-the-art Ensemble Kalman filter with the Greenshields traffic stream model using simulated loop detector data from Interstate 66. The results demonstrate that the proposed particle filter approach in combination with the discretized Van Aerde flow continuity model produces the lowest prediction error of 4.3 km/h for a five-minute prediction horizon, and accurately predicts the spatial and temporal propagation of shock waves.]]></description>
      <pubDate>Mon, 08 Jun 2015 16:00:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1354554</guid>
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    <item>
      <title>Modelling cooperative driving in congestion shockwaves on a freeway network</title>
      <link>https://trid.trb.org/View/1354483</link>
      <description><![CDATA[The development of advanced driver assistance technology continues to proceed rapidly. Cooperative systems based on wireless communication are a specific form of advanced driver assistance that is currently evolving rapidly. A drawback in the development of such systems is that options for large scale field-testing and the development of these automated systems are limited. Traffic simulation, however, offers widespread options for testing. In this paper the effects of cooperative driving using cooperative adaptive cruise control (CACC) to influence congestion shockwaves are evaluated on a part of the Amsterdam freeway network. The effects of congestion shockwaves on a network scale can be different to uniform freeway sections due to interaction between varying traffic flows. The application of CACC to mitigate the negative effects of shockwaves on a network level are simulated and analysed in this research for varying levels of CACC penetration. The results are analysed on both a quantitative as well as qualitative level and give a deeper understanding into the possibilities of the mass application of CACC systems.]]></description>
      <pubDate>Sat, 30 May 2015 18:00:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1354483</guid>
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    <item>
      <title>A Real-Time Process for Predicting Shockwave Trajectory on Freeway Traffic</title>
      <link>https://trid.trb.org/View/1338228</link>
      <description><![CDATA[Predicting shockwave propagation speed and location was widely studied to improve traffic safety by increasing the performance of intervention measures under congested conditions. Most of the past researches in this subject were from macroscopic perspective, such as making an analogy with fluid flow. Microscopic traffic following models are only used in setting and evaluating intervention measures. This paper presents a new process to detect and analyze shockwaves by simulating vehicle trajectories with detector measurements on a real time basis. With the proposed process, a) average shockwave propagation speed and location can be obtained and b) the probability of each vehicle in the group encountering the shockwave can be determined.]]></description>
      <pubDate>Tue, 24 Mar 2015 11:27:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1338228</guid>
    </item>
    <item>
      <title>Use of Naturalistic Driving Data to Characterize Driver Behavior in Freeway Shockwaves</title>
      <link>https://trid.trb.org/View/1288194</link>
      <description><![CDATA[Recent years have witnessed significant efforts in developing and evaluating vehicle-based passive and active safety systems to reduce traffic accidents. In addition, there is growing interest in the use of microscopic simulation models for evaluating operational strategies. Both activities require quantitative characterization of driver behavior in real-world situations. Historically, such characterizations have been difficult to obtain, but the data available from large-scale naturalistic driving studies (NDS) have the potential to change this situation. However, identifying relevant events from an NDS database and reducing the NDS data to estimate relevant features of the events are still something of a challenge. This study used freeway brake-to-stop events on congested freeways as examples to describe methods for identifying relevant events. It then estimated event features, such as initial speeds for leading and following vehicles, reaction times for leading and following drivers, and changes in the drivers’ braking rates. A suitably representative sample of such estimates could be used to support evaluation of vehicle-based safety countermeasures or provide inputs to traffic simulation models.]]></description>
      <pubDate>Wed, 26 Mar 2014 10:07:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1288194</guid>
    </item>
    <item>
      <title>Traffic Flow Theory Historical Research Perspectives</title>
      <link>https://trid.trb.org/View/1112837</link>
      <description><![CDATA[Traffic flow is a kind of many-body system of strongly interacting vehicles. Traffic jams are a typical signature of the complex behavior of vehicular traffic. Various mathematical models are presented to understand the rich variety of physical phenomena exhibited by traffic. This paper provides an overview of what is currently the state of the art with respect to traffic flow theory. Starting with a brief history about vehicular traffic flows, this paper discusses the Greenshields, Greenberg, and Gurein, etc., models. This paper also discusses some basic relations between traffic flow characteristics, i.e., the fundamental diagrams; speed, volume, and density relationships; hydrodynamic analogies; and traffic hump formation (shock wave). It also sheds some light on the different points of view adopted by the traffic engineering community. Some performance indicators are reviewed that allow assessment of the quality of traffic operations. A final part of this paper gives the probabilistic description of traffic flow, distribution of vehicles on a road.]]></description>
      <pubDate>Mon, 15 Aug 2011 11:04:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1112837</guid>
    </item>
    <item>
      <title>Characteristics of Transitions in Freeway Traffic</title>
      <link>https://trid.trb.org/View/968880</link>
      <description><![CDATA[This research seeks to understand the characteristics of transitions as freeway traffic changes from one state to another. This study addresses the features of two types of transitions; transitions near a merge and transitions along shock waves during the onsets and dissipations of queues at several freeway sites. Individual vehicle trajectory data were analyzed for studying the transitions near a merge. The length of a transition zone was measured by analyzing the spatial changes in flow, density and speed along kinematic waves near a merge. It was found that the length of transition in terms of flow, density and speed were respectively around 90m, 120m and 180m indicating that the transition in flow occurs over a short distance while the transition in speed occurs in much longer space. The dynamics of the transition zone were explored by analyzing the relationship among the transition durations, rates and various traffic and geometric variables at four freeway sites. Transition durations observed from the four sites vary from 10 to 24 minutes during the onsets of queues while the durations ranged from 10 to 30 minutes during the dissipations of the queues. At each site, formations and dissipations of queues displayed similar durations. Transition rates during the onsets of queues ranged from -7.6 to -2.2 kmph/min while they ranged from 2.0 to 6.2 kmph/min during the dissipations of queues. Some lane-specific features are observed in terms of initial speeds (just prior to transition), change in speed during transition, transition durations, and rates. It is also found that the structure of transition does not change in the absence of freeway interchanges as a queue expands and recedes. Finally, it is found that the transition rates tend to be larger upstream of an on-ramp while they tend to be smaller upstream of an off-ramp, indicating that inflows and outflows have different effects on transition characteristics.]]></description>
      <pubDate>Wed, 20 Oct 2010 16:16:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/968880</guid>
    </item>
    <item>
      <title>Freeway Sensor Spacing and Probe Vehicle Penetration: Impacts on Travel Time Prediction and Estimation Accuracy</title>
      <link>https://trid.trb.org/View/910441</link>
      <description><![CDATA[Accurate travel time prediction–estimation is important for advanced traveler information systems and advanced traffic management systems. Traffic managers and operators are interested in estimating optimal sensor density for new construction and retrofits. In addition, with the development of vehicle-tracking technologies, they may be interested in estimating optimal probe vehicle percentage. Unlike most studies focusing on data-driven models, this paper extends some limited previous work and describes a concept developed from first principles of traffic flow. The goal is to establish analytical relationships between travel time prediction–estimation accuracy and sensor spacing by means of two basic travel time prediction–estimation algorithms, as well as the probe vehicle penetration rate. The methods are based on computing the magnitude of under- and overprediction–estimation of total travel time (TTT) during shock passages in a time–space plane by using the midpoint method for online travel time prediction and the Coifman method for offline travel time estimation. Three shock wave configurations are assessed with each method so as to consider representative traffic dynamics situations. TTT prediction–estimation errors are calculated and expressed as a function of sensor spacing and probe vehicle percentage. Optimal sensor spacing is calculated with consideration of the tradeoff between TTT estimation error and sensor deployment cost. The results from this study can provide simple and effective support for detector placement and probe vehicle deployment, especially along a freeway corridor with existing detectors. Optimal sensor spacing results are analyzed and compared for various methods of travel time estimation during different types of shock waves.]]></description>
      <pubDate>Sun, 30 May 2010 07:44:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/910441</guid>
    </item>
    <item>
      <title>Onset of Congestion from Low-Speed Merging Maneuvers Within Free-Flow Traffic Stream: Analytical Solution</title>
      <link>https://trid.trb.org/View/910309</link>
      <description><![CDATA[Low-speed merging maneuvers performed within a free-flow stream are believed to trigger congestion. These accelerating moving bottlenecks introduce local constraints that can disturb the flow at a local or global scale. Low-speed merging maneuvers are also suspected to cause capacity drop. Using the kinematic wave theory, this paper explores the analytical solution of a simple first-order model when moving boundary conditions are introduced. The paper shows that shock waves initiated by low-speed merging maneuvers are a linear transformation of the moving boundary conditions, no matter the shape of the moving boundary condition. These results are then applied to typical situations to show that the interaction of two moving boundaries can modify the analytical solution of the problem. The results are then extended to multiple merging maneuvers to show that they can interact. Every possible interaction between two identical merging maneuvers is explored to identify the conditions that lead to global congestion. Finally, these results are used to propose an analytical formulation of the capacity drop for multiple merging maneuvers at a single location. It is shown that capacity drop is related to the demands on the minor and major streams and to the speed of the merging vehicle.]]></description>
      <pubDate>Wed, 14 Apr 2010 07:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/910309</guid>
    </item>
    <item>
      <title>Automatic Traffic Shockwave Identification Using Vehicles’ Trajectories</title>
      <link>https://trid.trb.org/View/880850</link>
      <description><![CDATA[Knowledge of the location and speed of shockwaves in a traffic stream provides insight into the formation and dissipation of congestion – information which is important for system managers. Furthermore, this information can be used to estimate and predict travel time for a section of a roadway. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This paper proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data. In the proposed methodology first the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified and then a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is used to find propagation speed and spatial and temporal extent of each shockwave. The framework is evaluated using data obtained from a simulation of a signalized intersection and also real trajectory data from freeway US-101 near Los Angeles and shows promising results.]]></description>
      <pubDate>Thu, 23 Apr 2009 11:15:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/880850</guid>
    </item>
    <item>
      <title>Intelligent Transportation Systems and Vehicle–Highway Automation 2008</title>
      <link>https://trid.trb.org/View/882834</link>
      <description><![CDATA[This collection of 16 papers is focused on intelligent transportation systems and vehicle–highway automation.  Specific topics discussed include the following:  cell phone traffic data; real-time vehicle classification using inductive loop signature data; vehicle detection in far field of view of video sequences; vision-based speed sensing; increasing the value of intelligent transportation systems; benefits and costs of advanced traffic management components; traveler information effects on commercial and noncommercial users; mobility and commute disutility effects of 511 deployment; analysis of route choice behavior; determinants of route choice and value of traveler information; dynamic speed limit control against shockwaves on freeways; wireless infrastructure-to-vehicle communication technologies; vehicle–infrastructure integration; and adaptive intelligent speed adaptation systems.]]></description>
      <pubDate>Wed, 04 Feb 2009 11:43:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/882834</guid>
    </item>
    <item>
      <title>Traffic Flow Theory: Historical Research Perspectives</title>
      <link>https://trid.trb.org/View/868746</link>
      <description><![CDATA[Traffic flow is a kind of many-body system of strongly interacting vehicles. Traffic jams are a typical signature of the complex behavior of vehicular traffic. Various mathematical models are presented to understand the rich variety of physical phenomena exhibited by traffic. This paper provides an overview of what is currently the state-of-the-art with respect to traffic flow theory. Starting with a brief history about vehicular traffic flows, it discusses the Greenshields, Greenberg’s, Gurein’s, etc., models. This paper also discusses some basic relations between traffic flow characteristics, i.e., the fundamental diagrams; speed, volume, and density relationships; hydrodynamic analogies; and traffic hump formation (shock wave), and sheds some light on the different points of view adopted by the traffic engineering community. Moving on, it reviews some performance indicators that allow one to assess the quality of traffic operations. A final part of this paper gives the probabilistic description of traffic flow, distribution of vehicles on a road.]]></description>
      <pubDate>Mon, 25 Aug 2008 08:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/868746</guid>
    </item>
    <item>
      <title>Distributed Controller Design Approach to Dynamic Speed Limit Control Against Shockwaves on Freeways</title>
      <link>https://trid.trb.org/View/848346</link>
      <description><![CDATA[Dynamic speed limits can be used to eliminate shockwaves on freeways. Shockwaves are typically short traffic jams that emerge at bottlenecks and travel in the upstream direction on the freeway. These shockwaves lead to increased travel times and possibly to unsafe situations. A speed limit control approach to resolving shockwaves was developed based on a distributed controller design technique. The controller is distributed in the sense that each speed limit sign has its own controller. The controller parameters are optimized by numerical optimization, assuming that the controller structure and parameters are the same for each controller. The resulting performances are compared for several designs, differing in the controller order and the extent that the upstream and downstream traffic states are used as inputs for the controller. Other controllers known from the literature are based on switching schemes using local information only or are centralized model-based controllers with high computational loads. The proposed method gives a systematic way to design distributed controllers using the appropriate amount of upstream and downstream traffic information. The resulting controllers are attractive from the implementation point of view because they are very efficient. They do not require extensive online computations and use only information from the neighborhood. For the design scenario used, the controller successfully resolved the shockwave and reduced the total time spent by approximately 20% compared with the uncontrolled case, which is comparable to the performance of the best controllers known from the literature.]]></description>
      <pubDate>Wed, 21 May 2008 07:05:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/848346</guid>
    </item>
    <item>
      <title>Real-World Carbon Dioxide Impacts of Traffic Congestion</title>
      <link>https://trid.trb.org/View/848801</link>
      <description><![CDATA[Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the U.S. inventory. To reduce CO2 emissions in the future, transportation policy makers are planning on making vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. Traffic congestion and its impact on CO2 emissions were examined by using detailed energy and emission models, and they were linked to real-world driving patterns and traffic conditions. With typical traffic conditions in Southern California as an example, it was found that CO2 emissions could be reduced by up to almost 20% through three different strategies: congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and shock wave suppression techniques that eliminate the acceleration and deceleration events associated with the stop-and-go traffic that exists during congested conditions.]]></description>
      <pubDate>Mon, 25 Feb 2008 14:33:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/848801</guid>
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