Highway Performance Monitoring System Research Articles

Urban Roadway in America: The Amount, Extent, and Value

Problem, research strategy, and findings We predicted the amount, share, and value of land dedicated to roadways within and across 316 U.S. primary metropolitan statistical areas. Despite the amount and value of land dedicated to roadways, our study provides the first such estimate across a broad range of metropolitan areas. Our basic approach was to estimate roadway widths using a 10% sample of widths provided by the Highway Performance Monitoring System and apply our estimates to the rest of the roadway system. Multiplying estimated widths by segment length and netting out double counting at intersections provided estimates of land area. We also matched roadway segments and areas to existing land value estimates and satellite-based measures of urbanized land. We found that a little less than a quarter of urbanized land—roughly the size of West Virginia—was dedicated to roadway. This land was worth around $4.1 trillion in 2016 and had an annualized value that was higher than the total variable costs of the trucking sector and the total annual federal, state, and local expenditures on roadways. Conducting a back-of-the-envelope cost–benefit analysis, we found that the country likely has too much land dedicated to urban roads. Takeaway for practice Federal, state, and local agencies dedicate substantial time, money, and resources to providing roadways. Even with relatively generous assumptions and no external costs from driving, however, we estimated that the average cost of expanding roadways exceeded the benefits by a factor of nearly three when accounting for land value. Policymakers should question policies focused on roadway expansion and consider options to reduce the amount of space dedicated to roadway in favor of more housing, offices, and other land uses. In addition to our findings, we provide a novel data set that academics and policymakers can use to draw their own conclusions about the state of America’s urban roadways.

Development of a Mechanistic-Empirical-Based Highway Cost Allocation Model for Flexible Pavements

Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.

Nationwide Demand Modeling for an Urban Air Mobility Commuting Mission

In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using the implemented model, we identify New York City, San Francisco, and Los Angeles as cities with the highest potential commuter demand, and Seattle as the city most resilient to increases in delay time. A sensitivity study of demand is performed and shows that strong demand exists for short trips with low total delay times and for longer trips with a low ticket price per kilometer, with the former showing resilience to increases in operational costs and the latter showing resilience to increases in delays. The demand model is supported by a speed-flow model, which fuses highway performance monitoring system data with OpenStreetMap data to provide traffic-adjusted road segment speeds to OSRM. The speed-flow model has the capability of providing congestion data for road segments across the United States without the use of commercial data sets or routing services and is shown to improve routing duration accuracy in congested regions.

Sustainable Infrastructure Is a Two-Way Street: Balancing Environmental and Condition Performance Goals

Transport agencies are under increasing pressure to mitigate the global warming impact of our pavement systems. This objective, however, must be carefully balanced with other performance metrics of interest (e.g., pavement condition) for federal and state agencies. This paper details a network-level tool aimed at supporting transport agencies in achieving two competing objectives: (1) maximizing the number of pavement segments in a good state-of-repair; and (2) minimizing the global warming impact of the network. The stochastic optimization model follows a two-stage bottom-up approach, where optimal policies are learned for individual facilities, and those decision-rules are subsequently used to guide the allocation of resources across the network. The model is applied to a realistic roadway network composed of 159 miles of pavement segments based on data made available via the Highway Performance Monitoring System. The case study results highlight that, over a 20-year analysis period, maximizing the condition of pavement assets across the network increases its expected global warming impact by 1% to 8%. The results also highlight that, invariant to the selected objective and/or available budget, increasing the allocation of funds toward rehabilitation activities rather than reconstruction treatments improves the overall condition of the network by as much as 25% and reduces its global warming impact by up to 7%. The results of the case study provide decision-makers with important insights around the impact of pavement management performance goals and budgetary policies on the global warming impact of pavement systems.

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Discrepancies in the spatial extent of common GIS highway networks

AbstractThe Federal Highway Administration's Highway Performance Monitoring System (HPMS) and the U.S. Census Bureau's TIGER geospatial database are relied on by countless professionals, but the two databases differ by 6.9 million km (4.3 million miles), a discrepancy greater than the Federal Highway Administration's national total of 6.6 million km (4.1 million miles). While there are several methodological reasons why a GIS network will give different answers from the HPMS, these cannot explain a difference of this magnitude. The difference exists because TIGER was based on roads shown on USGS topographic maps, the majority of which are not considered public highways today. This overestimate of highway extent can affect a range of network operations in GIS. There is, however, reason to consider TIGER's overestimate an underestimate of the actual extent of roads in the country, as well as questioning which roads should be included in a study.

Case Study using Probe Vehicle Speeds to Assess Roadway Safety in Georgia

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.

Understanding speeding behavior from naturalistic driving data: Applying classification based association rule mining

Speeding is considered as one of the most significant contributing factors to severe traffic crashes. Understanding the associations between trip/driving/roadways features and speeding behavior is crucial for both researchers and practitioners. This research utilized naturalistic driving data collected by the Safety Pilot Model Deployment (SPMD) program and roadway features from a road inventory dataset - Highway Performance Monitoring System (HPMS), provided by the United States Department of Transportation (USDOT), to investigate the hidden rules that associated trip/driving/roadway features with speeding behavior. A classification-based association (CBA) algorithm was adopted to explore the hidden rules from two perspectives of speeding: speeding duration and speeding pattern. Results indicate that the combinations of longer trips (more than 60 min), driving on the roadways with a relatively higher functional class are highly associated with longer speeding events (speeding longer than 2 min). The moderate speeding events (speeding longer than 2 min and longer than 30 s) are found highly associated with the combination of driving on roadways with lower functional class, absence of a median and relatively short trip time (less than 30 min). The research also found the combinations of driving on roadways with relatively lower functional class, experienced congestion before a speeding event, and the presence of a median is a leading cause that triggers a higher speeding pattern (speeding more than 5mph above the speed limit). Furthermore, the moderate speeding pattern (speeding more than 1mph above the speed limit and less than 5mph of the speed limit) is associated with the combinations of factors like experiencing congestion before a speed event, driving on roadways with higher functional class and a relatively shorter trip (less than 30 min). The findings can help practitioners understand the composite effect of these factors more comprehensively and provide corresponding countermeasures to mitigate the negative consequences of speeding wherever possible. These can also help in calibrating driver behavior parameters for transportation-related simulation tools.

Coupling National Performance Management Research Data Set and the Highway Performance Monitoring System Datasets on a Geospatial Level

Integration of various datasets is crucial given the emphasis placed on holistic reporting of performance measures of various variables related to road transportation by the Moving Ahead for Progress in the 21st Century (MAP-21) Act. None is more confounding than the merger of geospatial datasets, which is necessary, for example, to combine vehicle travel time and volume information for road segments. Such a merged dataset is released through the National Performance Management Research Dataset (NPMRDS). The NPMRDS is supposed to exclusively cover the National Highway System (NHS) and Strategic Highway Network (STRAHNET) sub-selected from the Highway Performance Monitoring System (HPMS). However, one finds that the coverage is not perfect. There are not only many extra road segments included in the NPMRDS, but also some NHS/STRAHNET roads segments are not fully covered by corresponding NPMRDS segments. Further, one finds very little literature about the method Texas Transportation Institute uses to orchestrate the conflation. Therefore, it was endeavored to create a conflation algorithm which might perform better. The benchmark for the proposed algorithm is the identification of the segments wrongly conflated during the creation of the NPMRDS geospatial dataset. The proposed methodology uses a combination of five measures of similarity between the HPMS and NPMRDS segments. The proposed method successfully identifies significant numbers of mismatched segments: about 5% excess NPMRDS segments, and about 3% HPMS segments without NPMRDS counterpart.

Coupled Approximation of U.S. Driving Speed and Volume Statistics using Spatial Conflation and Temporal Disaggregation

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.

Time-Series Review of Highway Performance Monitoring System Data

The passing of the Moving Ahead for Progress in the 21st Century Act required the FHWA to adopt pavement performance measures for evaluating the condition of the interstate highway system (IHS) and the national highway system (NHS). In January 2015, the FHWA issued a notice of proposed rulemaking (NPRM) to establish performance measures to assess pavement conditions on the IHS and NHS, with the final rule published in January 2017. The four measures were 1) Percentage of pavements on the IHS in good condition; 2) Percentage of pavements on the IHS in poor condition; 3) Percentage of pavements on the NHS (excluding IHS) in good condition; and 4) Percentage of pavements on the NHS (excluding IHS) in poor condition. Pavement condition is determined by the following metrics in the Highway Performance Monitoring System (HPMS): International Roughness Index, cracking percent, rutting, and faulting. FHWA commissioned a study to collate a statistically significant sample of the IHS to evaluate the efficacy of the HPMS and to recommend improvements in data collection and reporting processes to meet NPRM requirements. As part of the referenced study, a time-series review was conducted of the HPMS to identify changes in the data from 2013 to 2015 and to determine how these data compare with the study-collected data. This paper discusses the review of HPMS data, identifying changes in the pavement condition metrics, and overall condition. In general, the HPMS database exhibited improvements, with more complete data and fewer anomalies, in 2015 compared with 2013.

Interstate Speed Profiles

This paper draws the first set of high-level, national speed profiles for the entire Dwight D. Eisenhower National System of Interstate and Defense Highways (Interstate system) in the United States based on the 2016 year-long National Performance Management Research Data Set (NPMRDS) and a conflated NPMRDS-HPMS (Highway Performance Monitoring System) geospatial network. This set of quantitative profiles include: ( a) national average speeds of 2016, ( b) national average speed time of day variations, ( c) national average speed day of week variations, ( d) national average speed seasonal variations, and ( e) state average speed and travel time distributions in peak hours. This work demonstrates that the integration of the private sector’s emerging big travel-time data and the public sector’s HPMS has provided a powerful resource to monitor travel-time-related performance of the nation’s highways. As the United States is transforming the Federal-aid Highway Program into a performance-based program with enhanced accountability and transparency, this integrated resource will help states and metropolitan planning organizations (MPOs) to monitor their performance and progress towards achieving targets, and enable the Federal Highway Administration (FHWA) not only to draw high-level national highway performance profiles but also to pinpoint the exact where, when, and how much the challenges are.

Recent and Future Outlooks for Nuisance Flooding Impacts on Roadways on the U.S. East Coast

Tidal floods (i.e., “nuisance” flooding) are occurring more often during seasonal high tides or minor wind events, and the frequency is expected to increase dramatically in the coming decades. During these flood events, coastal communities’ roads are often impassable or difficult to pass, thus impacting routine transport needs. This study identifies vulnerable roads and quantifies the risk from nuisance flooding in the Eastern United States by combining public road information from the Federal Highway Administration’s Highway Performance Monitoring System with flood frequency maps, tidal gauge historic observations, and future projections of annual minor tidal flood frequencies and durations. The results indicate that tidal nuisance flooding across the East Coast threatens 7508 miles (12,083 km) of roadways including over 400 miles (644 km) of interstate roadways. From 1996–2005 to 2006–2015, there was a 90% average increase in nuisance floods. With sea level rise, nuisance-flood frequency is projected to grow at all locations assessed. The total induced vehicle-hours of delay due to nuisance flooding currently exceed 100 million hours annually. Nearly 160 million vehicle-hours of delay across the East Coast by 2020 (85% increase from 2010); 1.2 billion vehicle-hours by 2060 (126% increase from 2010); and 3.4 billion vehicle-hours by 2100 (392% increase from 2010) are projected under an intermediate low sea-level-rise scenario. By 2056–2065, nuisance flooding could occur almost daily at sites in Connecticut, New Jersey, Maryland, the District of Columbia, North Carolina, and Florida under an intermediate sea-level-rise scenario.

Relating Public Opinions of Roadway Assets to Field Data with Survey and Focus Groups

In recent years, transportation policy in the United States has increasingly focused on measuring and tracking performance outcomes. However, identifying how quantifiable outcomes—such as international roughness index ratings—influence less tangible outcomes—such as user satisfaction with a roadway—can be challenging. This paper outlines a method that enables researchers to compare stakeholder perceptions of assets with actual field-measured data for roadways. More than 350 residents in six locations in the state of North Carolina were surveyed about how those residents perceived and prioritized the roadway assets managed by the North Carolina Department of Transportation. Using a method termed “roadway reviews,” researchers asked participants about state-maintained roads as the participants were driven on those roads in real time; these surveys were supplemented with focus groups. In addition to identifying how well asset conditions on roadway segments aligned with the basic expectations of roadway users, researchers determined how these ratings compared with field measurements for the segments and captured how participants ranked the importance of specific roadway assets for overall condition, safety, and appearance. The findings will be used to improve asset management practice and will be integrated into the North Carolina Department of Transportation’s annual Highway Performance Monitoring System reports.

Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

SummaryThe recent increase in demand for performance‐driven and outcome‐based transportation planning makes accurate and reliable performance measures essential. Vehicle miles traveled (VMT), the total miles traveled by all vehicles on roadways, has been utilized widely as a proxy for traffic impact assessment, vehicle emissions, gasoline consumption, and crashes. Accordingly, a number of studies estimate VMT using diverse data sources. This study estimates VMT in the urban area of Bucheon, South Korea, by predicting the annual average daily traffic for unmeasured locations using spatial interpolation techniques (i.e., regression kriging and linear regression). The predictive performance of this method is compared with that of the existing Highway Performance Monitoring System (HPMS) method. The results show that regression kriging could provide more accurate VMT estimates than the HPMS method and linear regression, especially with a small sample size. Copyright © 2016 John Wiley & Sons, Ltd.

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

There are ongoing efforts to leverage the large volume of data collected to support real-time traffic operations for various non-real-time uses. However, adapting data streams to a different purpose can be challenging, especially if different applications use the data in different ways. Although traffic operations and traffic monitoring programs often use similar technology for vehicle detection, they may have different sensitivities to potential errors. Although small errors in traffic counts may not be critical in traffic operations, for which data are refreshed every few seconds, the same error levels could become much more significant in traffic monitoring, for which data, and the corresponding errors, are typically aggregated over longer periods of time. This study investigates if and how traffic volume data from detectors at signalized intersections could be appropriately used in a traffic monitoring program that supports the Highway Performance Monitoring System (HPMS). For this purpose, this study evaluates both the accuracy and representativeness of these traffic signal detector data in comparison to standard HPMS-type traffic count data obtained by portable pneumatic counters under different traffic flow conditions and intersection geometries. The results, although varying by site, indicate that these traffic signal detectors can produce 15-min aggregate traffic counts of comparable quality to results from portable pneumatic tube counters and provide 90% accuracy at a 95% level of confidence for 15-min aggregate counts. Through an examination of the representativeness of the data under different conditions, the study develops a set of eligibility criteria that can be used to identify intersections that are suitable for performance monitoring data collection.

Network level pavement evaluation with 1 mm 3D survey system

The latest iteration of PaveVision3D Ultra can obtain true 1 mm resolution 3D data at full-lane coverage in all 3 directions at highway speed up to 60 mph. This paper introduces the PaveVision3D Ultra technology for rapid network level pavement survey on approximately 1280 center miles of Oklahoma interstate highways. With sophisticated automated distress analyzer (ADA) software interface, the collected 1 mm 3D data provide Oklahoma Department of Transportation (ODOT) with comprehensive solutions for automated evaluation of pavement surface including longitudinal profile for roughness, transverse profile for rutting, predicted hydroplaning speed for safety analysis, and cracking and various surface defects for distresses. The pruned exact linear time (PELT) method, an optimal partitioning algorithm, is implemented to identify change points and dynamically determine homogeneous segments so as to assist ODOT effectively using the available 1 mm 3D pavement surface condition data for decision-making. The application of 1 mm 3D laser imaging technology for network survey is unprecedented. This innovative technology allows highway agencies to access its options in using the 1 mm 3D system for its design and management purposes, particularly to meet the data needs for pavement management system (PMS), pavement ME design and highway performance monitoring system (HPMS).

Evaluation of Traffic Data Collection Method

Traffic counts are one of the fundamental data sources for the Highway Performance Monitoring System (HPMS). Automatic Traffic Recorders (ATRs) are used to provide continuous traffic count coverage at selected locations to estimate annual average daily traffic (AADT). However, ATR data is often unavailable. This paper investigated the feasibility of using Video Detection System (VDS) technology when ATR data is not available. An Android Tablet-based manual traffic counting application was developed to acquire manual count based ground truth data. The performance of VDS was evaluated under various conditions including mounting styles, heights, and roadway offsets. The results indicated that VDS data presents reasonably accurate data, although the data exhibits more variability compared to ATR data.

Investigating Consistency in Highway Performance Monitoring System and Pavement Management Reporting

An NCHRP study was conducted to evaluate the comparability of pavement condition data submitted to FHWA under the Highway Performance Monitoring System (HPMS) and the information reported by state highway agencies as part of their pavement management program. As part of the study, a statewide survey of both HPMS and pavement management practitioners was conducted to identify issues associated with HPMS submittal requirements and to evaluate the impact these issues have on the quality of the data submittal. After the survey, eight states were invited to participate in a more detailed comparison of HPMS and pavement management data. The analysis compared the data in a number of ways, including the availability of comparable information in the pavement management and HPMS data sets, differences in pavement conditions reported using HPMS and pavement management data, and the impact of the reporting length (e.g., segment or route) on pavement condition statistics. The results indicate fairly good consistency in the international roughness index values reported to HPMS and used in pavement management. However, there are much greater inconsistencies in the pavement distress information reported to HPMS and used in pavement management. This paper summarizes the findings from the analysis and presents the implications of these findings on the performance management requirements outlined in the current law, Moving Ahead for Progress in the 21st Century Act, or MAP-21.

Greenhouse Gas Emissions and Urban Congestion

The Texas A&M Transportation Institute's Urban Mobility Report (UMR) is acknowledged to be the most authoritative source of information about traffic congestion and its possible solutions. As policy makers from the local to national levels devise strategies to reduce greenhouse gas (GHG) emissions, the level of interest in the environmental impact of urban congestion has increased. To this end, the researchers developed and applied a methodology to determine carbon dioxide (CO2) emissions caused by congestion for inclusion in the UMR. The methodology also estimated fuel consumption on the basis of the CO2 emissions estimates. The researchers developed a five-step methodology with data from three primary data sources: (a) FHWA's Highway Performance Monitoring System, (b) INRIX traffic speed data, and (c) the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator model. Results were intuitive and reasonable when emission rates (pounds of CO2 per mile) were compared with the emissions inventories in selected cities. The researchers incorporated the new methodology for all urban areas into the 2012 UMR and plan to include the same measures in future releases of the report. The researchers reported that, in 2011, 56 billion pounds of additional CO2 were produced in all 498 urban areas during congestion only; this amount equated to 2.9 billion gallons of wasted fuel. The amount of CO2 produced under free-flow conditions (i.e., absent congestion) was 1.8 trillion pounds in 2011 in all 498 urban areas.