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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>
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    <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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      <title>Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles</title>
      <link>https://trid.trb.org/View/1483512</link>
      <description><![CDATA[Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.]]></description>
      <pubDate>Thu, 28 Sep 2017 09:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1483512</guid>
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      <title>Predicting Traffic Flow Regimes From Simulated Connected Vehicle Messages Using Data Analytics and Machine Learning</title>
      <link>https://trid.trb.org/View/1480379</link>
      <description><![CDATA[The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine accuracy for various levels of market penetration; and 2. Examine the tradeoff between information insight and cost of data processing and management. Data from a virtual (simulated) testbed for the I-405 corridor in Seattle was used to conduct the study. The field data and VISSIM simulation model were obtained from Washington State Department of Transportation (WSDOT). The simulation model went through rigorous calibration and validation process as part of a separate study conducted by Noblis for the Federal Highway Administration (FHWA) Traffic Analysis Tools Program. The Trajectory Conversion Algorithm (TCA V2.3), an open source tool developed by Noblis for the United States Department of Transportation (USDOT), was used to emulate SAE J2735 Basic Safety Messages (BSM). Traffic flow regimes (free flow, speed at capacity, and congested) were predicted for 100’ x 100’ boxes overlaid on the I-405 traffic network, every 5 minutes an hour ahead of time using the simulated BSMs. The study made use of Apache Spark’s machine learning libraries for Logistic Regression, Decision Tree and Random Forest to develop models to predict the traffic flow regimes. The computational resources and analytic environment used for this work were provisioned via the Microsoft Azure cloud environment. The computing cluster used for the analysis consisted of four nodes in total: 2 head nodes for job submission and management and 2 worker nodes for computation. Prediction accuracy was tested for two types of communication technologies [Cellular, Dedicated Short Range Communications (DSRC)], two market penetrations (20%, 75%), and six traffic operational conditions. The three algorithms were tested for 6, 8, and 11 principal components. In addition, the Decision Trees and Random Forest algorithms were tested using two node impurity metrics (entropy, Gini), and Random Forest was tested for multiple ensembles of trees (10, 250, 1000). The model that used the Random Forest algorithm with 11 principal components, 250-tree ensemble, and the Gini node impurity metric, had the best results with an average F1 score of 0.83 over all scenarios. The F1 scores were 0.87 for free flow, 0.67 for at capacity and 0.95 for congested traffic regimes. The model was able to fully process an hour’s worth of BSMs into the 100’ x 100’ grid boxes, and make a prediction for the following hour, at 5-minute intervals for each of the 100’ x 100’ boxes in 6 to 16 minutes.]]></description>
      <pubDate>Sun, 27 Aug 2017 18:11:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1480379</guid>
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    <item>
      <title>Red Light Running at Heterogeneous Saturated Intersections in Mumbai, India: On the Existence of Two Regimes and Causal Factors</title>
      <link>https://trid.trb.org/View/1439649</link>
      <description><![CDATA[This paper presents an analysis of red light running (RLR) conducted at saturated intersections in the city of Mumbai, India, where the traffic is highly heterogeneous with respect to vehicle classes and driver behavior. When all vehicles are considered, almost one in 17 drivers is seen to be jumping red signals there. Unlike the RLR behavior that has been previously reported from intersections elsewhere, a peculiarity observed here is that, within a single red phase, two distinguishable segments of RLR behavior exist. The authors classified them into two regimes: Regime 1, just after the onset of red, and Regime 2, just before the onset of the next green. About one-third of RLR events occur in Regime 1 and the rest in Regime 2. The authors fit different distributions on the time distribution of RLR events. The Kolmogorov–Smirnov test suggests that, at all intersections, exponential distribution fits best for RLR behaviors in Regime 1, and extreme value distribution fits for Regime 2. In addition to those two regimes, RLR at a lower rate is observed in the period between those regimes, and normal distribution fits there. To analyze the causal factors of RLR behavior in the two regimes, the authors developed models at a mesoscopic level specific to vehicle class and regime. Although the red-to-green ratio and the presence of policing prove to be relevant factors affecting RLR in both the regimes, the relative time for which the conflict area is free affects RLR in Regime 2 but not in Regime 1.]]></description>
      <pubDate>Wed, 15 Mar 2017 17:15:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439649</guid>
    </item>
    <item>
      <title>Workshop 1 Report: Developing an effective performance regime</title>
      <link>https://trid.trb.org/View/1334325</link>
      <description><![CDATA[This workshop discussed the challenges faced in developing performance regimes: in particular, the way in which public transport authorities secure the performance of their operator(s). Earlier Thredbo workshops focused mostly on setting and measuring performance standards and incentivizing performance. This year's workshop also looked more widely. The first additional topic was the context in which the performance regime is operating: how well is the market developed and what consequences does that have for the regime? The second was the maturity of the regime. Which conditions have to be fulfilled to have a fully-fledged and mature performance regime? These questions were addressed based on papers (and workshop participants) discussing performance in Australia, New Zealand, Japan, Greece, France, Ireland, Sweden, The Netherlands, Chile and Latin America more widely, and The United States. Key findings are that a wider set of conditions has to be in place to make a performance regime work. Appropriate technology is needed to capture good quality data. Mature institutions – that is, with the necessary legal powers to enforce contracts, guard against capture by the operators, and with appropriate staffing and resources – are also crucial. Maturity differs widely in the countries covered in the workshop, and thus different solutions are needed in different contexts. In particular, in situations of “low maturity”, regimes that place greater emphasis on passenger/demand metrics are likely to be more appropriate. The distinction between enforcing and incentivizing is also important in developing an appropriate performance regime. A suggested analytical framework for an effective performance regime which takes account of the above factors is set out, together with areas for future research. Obtaining greater information on the marginal costs and benefits of improving performance and also how better to benchmark complex and diverse operations against each other are key areas for future research. Other key research needs identified include: how to strike the right balance between enforcement versus seeking improvement; operationalizing KPIs (e.g. targeting frequency versus punctuality); and understanding real as opposed to assumed behavior by authorities and public and private operators.]]></description>
      <pubDate>Tue, 23 Dec 2014 12:07:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1334325</guid>
    </item>
    <item>
      <title>The two-regime transmission model for network loading in dynamic traffic assignment problems</title>
      <link>https://trid.trb.org/View/1308855</link>
      <description><![CDATA[Dynamic network loading (DNL) model is concerned with moving traffic in space and time along road network links in dynamic traffic assignment (DTA) models. DNL models strive to build in traffic realism such as modelling transient queues and spillback to upstream links, yet they need to remain computable. Most models in the literature are skewed towards either realism or computability and thus leave a wide scope for further research in arriving at a balanced model. This research proposes a new DNL model called the Two-regime transmission model (TTM) based on widely accepted first-order traffic flow theory. The TTM aims to be quick and accurate enough for planning purposes, when embedded into the framework of a DTA. The TTM considers the time-dependent density states of network links over two regimes namely, free-flowing and congested regimes, and dynamically models the time-dependent queue length, but without the need to break the link into cells. This article sets out the theoretical background necessary for developing the TTM and it also illustrates the principles with the help of a simple network serving a single origin-destination (OD) pair. Although the numerical tests are only preliminary indicators, the TTM has been found to produce promising results, for example producing results that are apparently closer than the cell transmission model (CTM) to predicting the dissipation and formation of a queue in a homogeneous link for the same level of time discretisation. The authors believe that their work establishes TTM as a candidate worthy of future exploration, especially for representing plausible, first-order traffic dynamics within a dynamic user equilibrium model with a lower number of variables/side-constraints than the CTM.]]></description>
      <pubDate>Thu, 29 May 2014 10:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1308855</guid>
    </item>
    <item>
      <title>Prediction of travel time variability for cost-benefit analysis</title>
      <link>https://trid.trb.org/View/1124185</link>
      <description><![CDATA[Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic 'regimes' (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.]]></description>
      <pubDate>Fri, 16 Dec 2011 14:48:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1124185</guid>
    </item>
    <item>
      <title>The Two-Regime Transmission Model Applicable for Dynamic Network Loading Model</title>
      <link>https://trid.trb.org/View/1092221</link>
      <description><![CDATA[Dynamic Network Loading (DNL) model is concerned with moving traffic in space and time along road network links in Dynamic Traffic Assignment (DTA) models. DNL models strive to build in traffic realism such as modelling the transient queues and yet they need to remain computable. Most models in the literature are skewed towards either realism or computability and thus leave a wide scope for further research in arriving at a balanced model. This research proposes a new DNL model called the Two-regime Transmission Model (TTM) based on widely accepted first order traffic flow theory. The TTM is aimed to be quick and accurate enough for planning purposes, and is intended for embedding into the framework of a DTA at later stages. The TTM considers the time dependent density states of network links over two regimes viz., free-flowing and congested regimes and dynamically models the time dependent queue length. This article sets out the theoretical background necessary for developing the TTM and it then illustrates the principles with the help of a simple network serving a single origin/destination (OD) pair. The TTM has been found quick and it produced results which are close to the standard Cell Transmission Model (CTM).]]></description>
      <pubDate>Wed, 18 May 2011 11:21:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1092221</guid>
    </item>
    <item>
      <title>Spline-Based Multiregime Traffic Stream Models</title>
      <link>https://trid.trb.org/View/881715</link>
      <description><![CDATA[A methodological framework for developing multi-regime traffic stream models using B-spline regression is presented in this study. The new method is a data-driven approach, which does not presume any linearity and monotone at any regime, and non-linearity is automatically taken into account. The paper chooses up to quadratic B-spline as basis functions for fitting a three-regime speed-occupancy relationship. The determination of number of regimes is justified based on cluster analysis, trade-off between model accuracy and model complexity, and domain knowledge of traffic flow. The benefits of the new method include good data fitting performance, model flexibility and preservation of smoothness of the developed multi-regime traffic stream models.]]></description>
      <pubDate>Fri, 17 Apr 2009 09:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/881715</guid>
    </item>
    <item>
      <title>On Traffic Flow Regimes and Transitions in Signalized Arterials</title>
      <link>https://trid.trb.org/View/802152</link>
      <description><![CDATA[The paper presents the formulation and application of a data driven methodological framework to identify traffic flow regimes and transitions on signalized arterials from point measurements of volume and occupancy. The identification of regimes is conducted by a wavelet-based fuzzy clustering approach, while transitional conditions are studied using Bayesian networks. The results from this data-driven approach indicate the existence of four distinct traffic flow regimes; these regimes hold in arterials with different geometric and signalization characteristics. An analytical model is also developed based on kinematic wave traffic flow theory to determine the boundary conditions among traffic regimes. This model provides strong evidence that the presented statistical approach is in agreement with a simple and elegant analysis including traffic parameters that are observable and measurable.]]></description>
      <pubDate>Mon, 21 May 2007 13:18:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/802152</guid>
    </item>
    <item>
      <title>Incident Detection Based on Microscopic Double Loop Detector Data</title>
      <link>https://trid.trb.org/View/801283</link>
      <description><![CDATA[Quick and reliable detection of incidents is of great importance for incident management. In this paper we assess if individual vehicle data gathered by double loop detectors can be used for the purpose of incident detection. To achieve this, loop detector data for a large number of neighboring detectors on three different Dutch freeways for two months in 2005 have been collected. Furthermore, a database is available that  contains information about all reported incidents on the considered freeways.  Using these data, first of all we verify if we can identify lane and traffic regime specific upperbounds for time headways under normal conditions such that a single headway exceeding these values can be used as an indicator for an incident.  Second of all, we test if it is possible to detect an incident on the basis of changes in headway distributions. A similar analysis is carried out for the speeds at which individual vehicles pass a detector.  The results show that it is possible to define useful upperbounds for the time headways, that can be used for incident detection, given that the traffic volume is larger than 900 veh./hour/lane. Also in comparing the headway and speed distributions under normal and incident conditions, we find clear differences between both conditions. Based on the data analysis we propose three incident detection methods using headway measurements. One method is based on upperbounds for time headways, and the other two are based on headway distributions. We find promising results in testing and cross-comparing these methods.]]></description>
      <pubDate>Mon, 21 May 2007 13:18:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/801283</guid>
    </item>
    <item>
      <title>Performance of Modern Stop Bar Loop Count Detectors over Various Traffic Regimes</title>
      <link>https://trid.trb.org/View/801482</link>
      <description><![CDATA[Accurate turning movement counts at signalized intersections are essential for retiming an intersection or corridor and also important for identifying trends in traffic data.  In recent years, technology has emerged that can provide vehicle counts at the stop bar without sacrificing the performance of presence detection.  The performance of these count detectors was investigated in two studies, which are reported in this paper. The studies involved the comparison of manual counts from recorded video with automated counts obtained from stop bar count detectors at a signalized intersection.  These stop bar detectors are historically very difficult to obtain accurate count data from due to their long length (~50 ft) and tendency for multiple vehicles to concurrently occupy the detection zone.  Recently, new detector amplifiers have been introduced that are designed to analyze the inductance profile to provide better stop bar counts.   The first part of the study looked at count performance as a function of lane geometry and detector placement in the lane using data captured over a number of 24-hr periods. The placement of count detectors in relation to the lane lines was found to be a major factor in count accuracy leading to recommendations for sensor placement. The second portion of the study examined count detector performance under free-flowing and queued traffic regimes. A state of the art counting algorithm was observed to over count in the queued regime.  Analysis of the relative change in inductance signature indicated a clearly defined signature was observable during free flow conditions, but the signature during saturated flow regime was quite complex an required more sophisticated analysis. It is expected that stop bar detection algorithms could be further enhanced if phase status information was provided to the count detector amplifier.]]></description>
      <pubDate>Mon, 21 May 2007 13:18:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/801482</guid>
    </item>
    <item>
      <title>Tool for Calibrating Steady-State Traffic Stream and Car-Following Models</title>
      <link>https://trid.trb.org/View/801141</link>
      <description><![CDATA[The research reported in this paper develops a heuristic automated tool (SPD-CAL) for calibrating steady-state traffic stream and car-following models and compares the performance of the automated procedure to off-the-shelf optimization software including the MINOS and BARON solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multi-regime models, without the need to deal with the complexities associated with the selection of regime break points. The heuristic SPD-CAL solver is demonstrated to perform better than the MINOS and BARON solvers both in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid solution).]]></description>
      <pubDate>Mon, 21 May 2007 13:18:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/801141</guid>
    </item>
    <item>
      <title>Statistical Analysis of Driver Behavior Data in Different Regimes of the Car-Following Stage</title>
      <link>https://trid.trb.org/View/801055</link>
      <description><![CDATA[An instrumented vehicle has been used to study car-following behavior on Swedish motorways. In this study, the previous data collection and preprocessing work were briefly reviewed. To understand the driving behavior in the car-following stage more clearly, the collected time series were classified into a number of regimes using unsupervised fuzzy clustering methods. Then, the statistical relations between the driver acceleration response and the perceptual variables in each regime were analyzed using correlation and regression methods. It was found that regime classification helps discern the behavioral variance between those regime clusters. According to the data analysis, some of the car-following regimes, for example, opening and braking, can be described adequately in the statistical sense by a linear regression model (Helly’s model). Therefore, a multiple regime car-following model with simple model forms, for example, linear models, has the potential to robustly represent the general car-following behavior in most regimes.]]></description>
      <pubDate>Tue, 24 Apr 2007 07:19:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/801055</guid>
    </item>
    <item>
      <title>The Politics of Vulnerability: Constructing Local Performance Regimes for Homeland Security</title>
      <link>https://trid.trb.org/View/781656</link>
      <description><![CDATA[This paper examines homeland security initiatives, particularly the tension between risk and vulnerability, and the governance dilemmas they pose for local communities. The authors conceptualize local imperatives attendant to homeland security as collective action problems requiring the construction of local performance regimes. Performance regimes must engage three challenges: (1) to enlist diverse stakeholders around a collective local security goal despite varying perceptions of its immediacy; (2) to persuade participants to sustain their involvement in the face of competing demands, and (3) to create a durable coalition around performance goals necessary for reducing local vulnerability. Using these analytic categories casts local homeland security issues in strategic terms rather than in terms of coordination and capacity.  A governance perspective using the performance regime concept contributes to efforts to theorize emergency and disaster management studies and encourages comparisons with similar security initiatives in other cities in terms of improving the responsiveness and resilience of local communities.]]></description>
      <pubDate>Wed, 31 May 2006 09:30:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/781656</guid>
    </item>
    <item>
      <title>APPLICATION OF THE MIXTURE OF PROBABILITY DISTRIBUTIONS TO THE RECOGNITION OF ROAD TRAFFIC FLOW REGIMES</title>
      <link>https://trid.trb.org/View/505761</link>
      <description><![CDATA[In this paper we consider that traffic measurements (flow and occupancy) are produced by an abstract random variable. Empirical studies have shown that it is fairly rare for the distribution of such a variable to be unique, i.e., that the parameters associated with it have a single value throughout an entire sample.  We assume that the "complete" distribution of this abstract random traffic variable is a combination of several distributions each component of which identifies a specific behaviour of traffic, known as a regime.  This is a concept which is frequently used by the operators of road networks in order to identify different traffic conditions within a network.]]></description>
      <pubDate>Wed, 11 Aug 1999 00:00:00 GMT</pubDate>
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