<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3ABthst" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Assessing Road Network Performance through Zonal Resilience Metrics</title><link>http://pubsindex.trb.org/view/2658330</link><description><![CDATA[Road networks are often subjected to disruptions caused by demand and capacity uncertainties, leading to excessive delays. A resilient transport system could absorb and recover quickly from such events. However, resiliency varies across different links, which may be because of zonal characteristics, network structure, or other factors. This study develops a methodology to quantify road network resilience at the zonal level and identify factors affecting it. Crowdsourced traffic speed data from approximately 33,000 locations was used to calculate resilience metrics, including the zonal resilience index, zonal vulnerability index, and zonal recoverability index. These metrics were modeled using geographically weighted regression to explore their relationship with independent variables. The results revealed that zonal trip heterogeneity, land use heterogeneity, and road category heterogeneity within a zone significantly reduce resilience.In contrast, connectivity measures, such as the clustering and degree assortativity coefficients, improve the recoverability of the zone. The increase in households owning more than two motorized vehicles in a zone reduces zonal resilience. The models were validated using subsets of the data, splitting weekdays from June 1–15 and June 16–30 and testing the model under different zone sizes. Results showed consistent variable effects across subsets and configurations, with slight variations in the significance of certain factors. Policymakers can utilize these insights to create land use or congestion pricing policies for individual zones to curb congestion. In addition, the network topology results can help plan a resilient road network for developing cities.]]></description><pubDate>Tue, 27 Jan 2026 09:19:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2658330</guid></item><item><title>Effects of Speed Feedback Trailer Positioning and Police Enforcement on Vehicular Speeds in Freeway Work Zone Lane Closures</title><link>http://pubsindex.trb.org/view/2344898</link><description><![CDATA[This field study sought to evaluate select strategies for improving compliance with work zone speed limits, which included a speed feedback trailer (SFT) and active police enforcement. The initial evaluation included an SFT positioned at the start or end of the taper within a freeway single-lane closure to determine which position provided the most favorable speed reductions. Positioning the SFT near the end of the taper caused the speed reductions to be sustained over a greater distance into the work zone compared with when the SFT was positioned near the start of the taper. With the SFT positioned near end of the taper, the average speed was 1.5?mph lower at the end of the taper and 0.8?mph lower 1,350-ft beyond the end of the taper compared with when the SFT was inactive. The second evaluation assessed the effectiveness of a specialized work zone enforcement strategy that included a covert speed measurement vehicle positioned near the end of the work zone along with four police cars positioned just beyond the end of the work zone to stop speeding drivers. This speed enforcement strategy reduced work zone speeds by up to 7?mph, on average, shortly beyond the end of the work zone as motorists passed by the police cars positioned on the shoulder. These speed reductions were only observed when at least one law enforcement vehicle was visibly present at the site.]]></description><pubDate>Fri, 01 Mar 2024 13:41:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2344898</guid></item><item><title>Evaluation of the Minimum Number of Local Driving Cycles Required to Represent the Traffic of Distinct Cities: A Case Study of Two Brazilian Metropolises</title><link>http://pubsindex.trb.org/view/2219093</link><description><![CDATA[This study aimed to determine whether a single local driving cycle (LDC) can effectively represent different cities in the same country, in both urban and highway routes, and for cars and motorcycles. To achieve this, experienced drivers drove different monitored vehicles (five cars and three motorcycles) on seven selected routes in two Brazilian states (Pernambuco and São Paulo State), collecting 170?h of speed data in urban and highway routes during peak and off-peak hours. Using the micro-trip and Markov chain methods, LDCs were then developed based on the collected real-world data. The kinematic and energy parameters of different route groupings were compared, revealing that two LDCs, one for cars and one for motorcycles, could be used to represent all urban routes. However, each highway route required a unique LDC. When compared with standard driving cycles adopted in Brazil, the created LDCs presented a coefficient of variation of 13%–46% in kinematic characteristic parameters, highlighting the need for developing LDCs to better represent Brazilian traffic.]]></description><pubDate>Wed, 26 Jul 2023 10:57:03 GMT</pubDate><guid>http://pubsindex.trb.org/view/2219093</guid></item><item><title>Estimating Demand Volume for Signalized Corridors Under Oversaturated Conditions Using Aggregated Probe Vehicle Speed Data</title><link>http://pubsindex.trb.org/view/2195178</link><description><![CDATA[Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of this study is to develop methods that could be implemented in practice based on readily available data. To this end, two methods are proposed: an innovative method based on shockwave theory and the volume delay function adapted from the Highway Capacity Manual (7th edition). Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. Probe vehicle data are used to determine the critical times when the queue reaches the end or beginning of a road segment. From these critical times, the shockwave speed for the boundary between congested (high density) traffic and arriving (low density) traffic is estimated. The proposed methods are tested with simulation data generated in VISSIM and validated based on volume data from the field. The field data are collected from a congested arterial in Virginia Beach, VA, and include the ground truth volumes and INRIX speed data aggregated at one-minute intervals. The results show that both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data.]]></description><pubDate>Thu, 15 Jun 2023 08:51:03 GMT</pubDate><guid>http://pubsindex.trb.org/view/2195178</guid></item><item><title>Sparse Data Traffic Speed Prediction on a Road Network With Varying Speed Levels</title><link>http://pubsindex.trb.org/view/2107945</link><description><![CDATA[Most works on graph neural networks (GNNs) for traffic speed prediction assume near-complete data and little variance of base speed levels. However, both assumptions do not necessarily hold true for network-wide probe vehicle data (PVD). Therefore, we applied two state-of-the-art GNNs to sparse PVD from a road network with highly varying speed levels and to dense motorway data for comparison. We introduce two methods to adapt preexisting GNNs for improved prediction performance: normalization of speed values with respect to the base speed levels of different roads led to significant improvements of prediction performance on both datasets. Using the number of observations supporting each speed value as an additional input feature can improve prediction performance. Furthermore, we identified characteristics of data and models encouraging the use of either method. As no fitting dataset to evaluate these approaches was found, a novel dataset derived from PVD is introduced. It features sparse speed values and underlying numbers of observations for a road network with varying speed levels.]]></description><pubDate>Fri, 03 Feb 2023 09:29:31 GMT</pubDate><guid>http://pubsindex.trb.org/view/2107945</guid></item><item><title>Arterial Signal Offset Optimization Using Crowdsourced Speed Data</title><link>http://pubsindex.trb.org/view/2001759</link><description><![CDATA[Signal offset for coordinated traffic signal control is traditionally optimized based on posted speed limit, free-flow speed, or average speed among intersections, without considering the variations of travel speed. Variation in travel speed caused by interference on arterials may lead to inaccurate offset estimation, reducing the efficiency of coordination control. Therefore, this study develops an arterial offset optimization method for traffic signal coordination control using real-time speed collected from high-resolution crowdsourced data. The objective of the proposed method is to minimize the average delay on the corridor. The optimization problem is formulated as integer programming, and a genetic algorithm (GA) is utilized to search for the best offset solution. The proposed method is evaluated on a major arterial (Speedway Boulevard) in Tucson, Arizona. In the numerical exercise, the effectiveness and performance of the proposed method are evaluated in various scenarios, including a scenario with non-recurring congestion. The results show that using high-resolution real-time speed data can reduce travel delay time in a coordinated direction by 32.5% and 17.6% when compared with methods using speed limit and free-flow speed, respectively, and the proposed method is more reliable and robust for handling traffic conditions with varying volume and speed.]]></description><pubDate>Mon, 01 Aug 2022 14:21:35 GMT</pubDate><guid>http://pubsindex.trb.org/view/2001759</guid></item><item><title>Analyzing the Difference Between Operating Speed and Target Speed Using Mixed-Effect Ordered Logit Model</title><link>http://pubsindex.trb.org/view/1945978</link><description><![CDATA[Desired operating speed (target speed) plays an important role in enhancing traffic operations and providing safe mobility to road users. Understanding the difference between vehicles’ operating speed and target speed on arterial roads is important for achieving safer speed that is consistent with the activity generated in the context classified roadways. This paper proposes a mixed-effect ordered logit model to examine the significant exogenous factors that affect the difference between the two speeds. To the best of the authors’ knowledge, no existing research has adopted the concept of target speed. Three years of probe vehicle-based data (INRIX speed data) and exogenous variables including traffic and roadway characteristics, land use attributes, and sociodemographic information are utilized in the models. The data include information for around 1,600 roadway segments in Central Florida. The results conclude that 16 variables are significantly associated with the difference between target speed and operating speed including speed limit, volume exposure, shoulder width information, sidewalk and shared path proportions, block length, number of signals, pavement conditions, residential and mixed land use, population density, and percentage of poverty. The results also indicate the effect of different time periods on the response variable. Therefore, different posted speed limits are recommended based on the time of day. Further, the study suggests the roadway measures that should be followed to achieve the desired target speed.]]></description><pubDate>Mon, 02 May 2022 16:11:55 GMT</pubDate><guid>http://pubsindex.trb.org/view/1945978</guid></item><item><title>Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method</title><link>http://pubsindex.trb.org/view/1909729</link><description><![CDATA[This paper presents an investigation into the performance of support vector machine (SVM) in short-term travel-time prediction in comparison with baseline methods, including the historical mean, current time based, and time varying coefficient predictors. To demonstrate the SVM performance, 1-month time-series speed data on a section of Pan-Island Expressway in Singapore were used to estimate the travel time for training and testing the SVM model. The results show that the SVM method significantly outperforms the baseline methods in both normal and recurring congestion over a wide range of prediction intervals. In studying SVM prediction behavior under incident situations, the results show that all the predictors are not responsive enough using 15-minute aggregated field data, but the SVM predicted outcome follows the test data profile closely for 2-minute aggregated simulated data. Finally, to improve the prediction performance, an empirical k-nearest neighbor method is introduced to retrieve patterns closest to the test vector for SVM training. The results show that k-Nearest Neighbor is an attractive tool for SVM travel-time prediction. In retrieving the most similar patterns for SVM training, k-nearest neighbor allows dramatic reduction of training size to accelerate the training task while maintaining prediction accuracy.]]></description><pubDate>Thu, 10 Feb 2022 17:05:59 GMT</pubDate><guid>http://pubsindex.trb.org/view/1909729</guid></item><item><title>Diagnosing Obstacles to Speed and Reliability with High-Resolution Automatic Vehicle Locator Data: Bus Time Budgets</title><link>http://pubsindex.trb.org/view/1872053</link><description><![CDATA[Transit riders consistently rate speed and reliability of service as primary drivers of satisfaction, and transit agencies can help retain and grow ridership by improving these components of service. The challenge for transit agency staff is to identify when and where they should focus efforts to improve service quality. Here we propose an approach to data analysis that identifies and isolates specific aspects of service that are limiting speed and reliability. In-vehicle travel time can be decomposed into time spent in motion and time stopped. Time in motion is often dependent on factors common to general traffic, whereas time stopped has some features in common with general traffic (i.e., traffic signals) and some unique to buses (i.e., passenger dwell). Other sources of delay from serving a bus stop include deceleration, acceleration, and signal delay. To improve overall travel time, transit agencies must prioritize interventions that will contribute the most to improving speed and reliability. We used high-resolution automatic vehicle locator data to assign components of speed and reliability within a trip-level “time budget.” We compared typical time budget components across service types, and used the time budget approach to evaluate local service and Rapid bus service operating simultaneously on the same alignment. Results of the delay and variability quantifications suggested particular interventions, as well as the expected size of the resulting effect. With limited resources, the bus time budget approach could aid understanding and prioritization of transit agency efforts to improve speed and reliability.]]></description><pubDate>Tue, 17 Aug 2021 10:40:42 GMT</pubDate><guid>http://pubsindex.trb.org/view/1872053</guid></item><item><title>Case Study using Probe Vehicle Speeds to Assess Roadway Safety in Georgia</title><link>http://pubsindex.trb.org/view/1738902</link><description><![CDATA[Speed is a primary risk factor for road crashes and injuries. Previous research has attempted to ascertain the relationship between individual vehicle speeds, aggregated speeds, and crash frequency on roadways. Although there is a large body of research linking vehicle speeds to safety outcomes, there is not a widely applied performance metric for safety based on regularly reported speeds. With the increasingly widespread availability of probe vehicle speed data, there is an opportunity to develop network-level safety performance metrics. This analysis examined the relationship between percentile speeds and crashes on a principal arterial in Metropolitan Atlanta. This study used data from the National Performance Metric Research Data Set (NPMRDS), the Georgia Electronic Accident Reporting System, and the Highway Performance Monitoring System. Negative binomial regression models were used to analyze the relationship between speed percentiles, and speed differences to crash frequency on roadway sections. Results suggested that differences in speed percentiles, a measure of speed dispersion, are related to the frequency of crashes. Based on the models, the difference in the 85th percentile and median speed is proposed as a performance metric. This difference is easily measured using NPMRDS probe vehicle speeds, and provides a practical performance metric for assessing safety on roadways.]]></description><pubDate>Tue, 15 Sep 2020 12:01:30 GMT</pubDate><guid>http://pubsindex.trb.org/view/1738902</guid></item><item><title>An Augmented Bayesian Tensor Factorization Model for Missing Traffic Speed Data Imputation</title><link>http://pubsindex.trb.org/view/1572529</link><description><![CDATA[In data-driven intelligent transportation systems, advanced sensor technologies have broadened the ways to collect a large quantity of urban traffic data. However, due to the sparsity of some kinds of data and the uncertainty of sensors in data collection, the problem of incomplete data is frequently suffered. Therefore, it still remains a challenge on improving the accuracy of missing traffic data imputation. In this study, the authors propose an augmented Bayesian CP factorization (AugBCPF) model based on the standard Bayesian CP factorization one (BCPF) to estimate the missing traffic data accurately. With exception of the CP decomposition structure, which allows capture of the interactions between different dimensions, the authors further add global parameter and bias terms to the mean parameter of the Gaussian assumption on tensor entries. Therefore, the model is capable of modeling the variation effect that only associated with every specific object itself. Afterwards, the authors present a fully Bayesian treatment of AugBCPF by placing conjugate priors over all model parameters and using Markov chain Mento Carlo (MCMC) methods to perform approximation inference. Empirically, relying on the urban traffic speed data set collected from Guangzhou, China, the authors evaluate the performance of data completion task with the proposed and competing methods by varying the missing rate from 10\% to 80\%. The authors find that the proposed model outperforms the state-of-the-art methods -- BCPF and SVD-combined Tensor Decomposition (STD). Moreover, the additive global parameter and bias terms of AugBCPF have profound implications in effect that can capture the real traffic pattern.]]></description><pubDate>Fri, 01 Mar 2019 15:51:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/1572529</guid></item><item><title>Investigation of Design Speed Characteristics on Freeway Ramps using SHRP2 Naturalistic Driving Data</title><link>http://pubsindex.trb.org/view/1576989</link><description><![CDATA[Freeway ramp design guidance has existed in the United States for many decades, coinciding with the advent of the nation’s freeway network and the Interstate Highway system. Some principles associated with ramp design are largely unchanged since their inception, and a review of those principles in the context of today’s drivers and vehicles is beneficial for identifying potential updates to existing guidance. The process of collecting the necessary data may consist of a variety of methods, each with limitations on the number of ramps, vehicles, and trips that can be studied. A current research project is exploring the feasibility of using data from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) to identify relationships between ramp design speed characteristics and drivers’ choices of operating speeds on those ramps. The NDS data provides a dataset that is unprecedented in its size and detail, but its suitability for this type of analysis is largely unknown. This paper summarizes the activities and findings of the current research project, including basic models for estimating vehicle speeds on freeway ramps based on the NDS data; these models may be used in conjunction with other ongoing related research efforts to suggest material for potential updates to existing ramp design guidance.]]></description><pubDate>Fri, 18 Jan 2019 14:56:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/1576989</guid></item><item><title>Coupled Approximation of U.S. Driving Speed and Volume Statistics using Spatial Conflation and Temporal Disaggregation</title><link>http://pubsindex.trb.org/view/1493183</link><description><![CDATA[The advent of mobile devices with embedded global positioning systems has allowed commercial providers of real-time traffic data to develop highly accurate estimates of network-level vehicle speeds. Traffic speed data have far outpaced the availability and accuracy of real-time traffic volume information. Limited to a relatively small number of permanent and temporary traffic counters in any city, traffic volumes typically only cover a handful of roadways, with inconsistent temporal resolution. This work addressed this data gap by coupling a commercial data set of typical traffic speeds (by roadway and time of week) from TomTom to the U.S. Federal Highway Administration’s Highway Performance Monitoring System database of annual average daily traffic (AADT) counts by roadway. This work is technically novel in its solution for establishing a national crosswalk between independent network geometries using spatial conflation and big data techniques. The resulting product is a national data set providing traffic speed and volume estimates under typical conditions for all U.S. roadways with AADT values.]]></description><pubDate>Tue, 19 Jun 2018 09:34:08 GMT</pubDate><guid>http://pubsindex.trb.org/view/1493183</guid></item><item><title>Comparison of Floating-Car Based Speed Data with Stationary Detector Data</title><link>http://pubsindex.trb.org/view/1494695</link><description><![CDATA[This paper compares speed data measured by induction loops of stationary detectors with reported speeds from floating-car data which are based on most recent GPS observations of probe vehicles. Detector data are aggregated over one minute so they are 30s old on average. The time delay of floating-car data is more complex. Significant influences are (i) the update frequencies from vehicles to the backend server, (ii) the fleet size of the floating cars, (iii) the current traffic flow, and (iv) the provider treatment. The floating-car dataset has a high spatial resolution with an average segment length of 100m suited for large-scale traffic observation and management. The spatial dimension of detector data can only be reconstructed ex-post from spotty positions (mean detector positions distance approx. 1.3km). The paper analyzes which source is more advantageous in terms of detecting traffic jams, high temporal availability of detector data or detailed spatial resolution of floating-car data. The analysis includes spatiotemporal dynamics with traffic jam patterns. Furthermore, an algorithm is presented to compute the jam detection duration meaning which data source recognizes a jam earlier. The results show that regions exist along the considered road stretch where floating-car data clearly outperform stationary data because of their disadvantageous positions but in regions where detectors are placed densely, stationary sensor data recognize a jam situation approx. 2 min earlier than floating-car based speed data.The datasets cover a period of 80 days in 2015 for both driving directions on German autobahn A9 in the north of Munich.]]></description><pubDate>Tue, 27 Mar 2018 11:15:38 GMT</pubDate><guid>http://pubsindex.trb.org/view/1494695</guid></item><item><title>Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data</title><link>http://pubsindex.trb.org/view/1496374</link><description><![CDATA[Probe vehicle speed data has become an important data source for evaluating the congestion performance of highways and arterial roads. Pre-defined spatially located segments known as traffic message channels (TMCs) are linked to commercially available, temporal anonymous probe vehicle speed data. These data have been used to develop agency-wide performance measures to better plan and manage infrastructure assets. Recent research has analyzed individual as well as aggregated TMC links on roadway systems to identify congested areas along spatially defined routes. By understanding the typical congestion of all TMCs in a region as indicated by increased travel times, a broader perspective of the congestion characteristics can be gained. This is especially important when determining the impact of such occurrences in the region as a major crash event, special events, or during extreme conditions such as a natural or human-made disaster. This paper demonstrates how aggregated probe speed data can be used to characterize regional congestion. To demonstrate the methodology, an analysis of vehicle speed data during Hurricane Sandy, the second costliest hurricane in the United States, is used to show the regional impact in 2012. Further, the analysis results are compared and contrasted with comparable periods of increased congestion in 2013, 2014, and 2016. The analysis encompasses 614 TMCs, within 10 miles of the New Jersey coast. Approximately 90 million speed records covering five counties are analyzed in the study.]]></description><pubDate>Fri, 23 Mar 2018 10:32:44 GMT</pubDate><guid>http://pubsindex.trb.org/view/1496374</guid></item></channel></rss>