Annual Average Daily Traffic Research Articles

HSM calibration factor, calibration function, or jurisdiction-specific safety model – A comparative analysis

ABSTRACTThere are various ways in which a transportation agency can approach safety prediction. One could calibrate the Highway Safety Manual (HSM) using adjustment factors by ranges of exposure variable, calibrate it using a function, or develop jurisdiction-specific models. Various tradeoffs are involved in deciding on which approach to undertake, including minimum sample size, data required, data processing, modeling, statistical expertise, labor involved, and accuracy of estimate. A comprehensive case study involving urban freeway four-lane segments (FU4) in Missouri is presented along with a discussion of general tradeoffs. Highway Safety Manual calibration was performed over the entire range of predictor variables. For calibration functions, regression modeling was performed, and calibration function forms were explored using annual average daily traffic (AADT) and segment length as additional predictor variables. Missouri specific safety performance functions (SPF) were developed and cumulative residuals (CURE) plots were used for comparative analysis of all model approaches. For calibration functions and SPFs, the inverse overdispersion and log-likelihood were evaluated in addition to the CURE plots. The results showed that calibration by AADT ranges outperformed all other calibration factors and functions proposed. The jurisdiction-specific SPF had similar accuracy as fully loaded and calibrated HSM models while not requiring the extensive data collection and processing of freeway-related crash modifications factors.

Evaluation of Traffic Density Parameters as an Indicator of Vehicle Emission-Related Near-Road Air Pollution: A Case Study with NEXUS Measurement Data on Black Carbon.

An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively. We evaluated the potential of these indices as vehicle emission-specific near-road air pollutant indicators by analyzing their correlation with black carbon (BC), a marker for mobile source air pollutants, using measurement data obtained from the Near-road Exposures and Effects of Urban Air Pollutants Study (NEXUS). The average BC concentrations during a day showed variations consistent with changes in traffic volume which were classified into high, medium, and low for the morning rush hours, the evening rush hours, and the rest of the day, respectively. The average correlation coefficients between BC concentrations and MRD, ATD, and HTD, were 0.26, 0.18, and 0.48, respectively, as compared with −0.31 and 0.25 for two commonly used traffic indicators: nearest distance to a major road and total length of the major road. HTD, which includes only heavy-duty diesel vehicles in its traffic count, gives statistically significant correlation coefficients for all near-road distances (50, 100, 150, 200, 250, and 300 m) that were analyzed. Generalized linear model (GLM) analyses show that season, traffic volume, HTD, and distance from major roads are highly related to BC measurements. Our analyses indicate that traffic density parameters may be more specific indicators of near-road BC concentrations for health risk studies. HTD is the best index for reflecting near-road BC concentrations which are influenced mainly by the emissions of heavy-duty diesel engines.

Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity

Safety Performance Functions (SPFs) provide a basis for identifying locations where countermeasures can be effective. While SPFs in the Highway Safety Manual (HSM) were calibrated based on data from select states, calibration factors can be developed to localize SPFs to other states. Calibration factors typically provide a coarse adjustment—time and space stationarity of associations between crash frequencies and various factors is still assumed, implying that the SPF functional form is transferable. However, with increasing availability of statewide geo-referenced safety data, new spatial analysis methods, and increasing computational power, it is possible to relax the stationarity assumption. Specifically, to address spatial heterogeneity in SPFs, this study proposes relaxing SPFs (referring to them as Localized SPFs (L-SPFs)) that can be developed by using sophisticated geo-spatial modeling techniques that allow correlates of crash frequencies to vary in space. For demonstration, a 2013 geo-referenced freeway crash and traffic database from Virginia is used. As a potential methodological alternative, crash frequencies are predicted by estimating Geographically Weighted Negative Binomial Regressions. This model significantly outperforms the traditional negative binomial model in terms of model goodness-of-fit, providing a better and fuller understanding of spatial variations in modeled relationships. Our study results uncover significant spatial variations in parameter estimates for Annual Average Daily Traffic (AADT) and segment length. Ignoring such variations can result in prediction errors. The results indicate low transferability of a single statewide SPF highlighting the importance of developing L-SPFs. From a practical standpoint, L-SPFs can better predict crash frequencies and support prioritizing safety improvements in specific locations.

Spatial models of active travel in small communities: Merging the goals of traffic monitoring and direct-demand modeling

Abstract A number of recent studies have made progress on specific components of monitoring and modeling bicycle and pedestrian traffic. However, few efforts merge the goals of collecting traffic counts and developing spatial models to meet multiple objectives, e.g., tracking performance measures and spatial modeling for use in exposure assessment. We used estimates of bicycle and pedestrian Annual Average Daily Traffic (AADT) from a comprehensive traffic monitoring campaign in a small community to develop direct-demand models of bicycle and pedestrian AADT. Our traffic monitoring campaign (101 locations) was designed specifically to capture spatial variability in traffic patterns while controlling for temporal bias. Lacking existing counts of cyclists and pedestrians, we chose count sites based on street functional class and centrality (a measure of trip potential). Our direct-demand models had reasonable goodness-of-fit (bicycle R 2 : 0.52; pedestrian R 2 : 0.71). We found that aspects of the transportation network (bicycle facilities, bus stops, centrality) and land use (population density) were correlated with bicycle and pedestrian AADT. Furthermore, spatial patterns of bicycle and pedestrian traffic were different, justifying separate monitoring and modeling of these modes. A strength of our analysis is that we conducted counts at a representative sample of all street and trail segments in our study area (Blacksburg, Virginia; ~5.5% of segments) – an advantage of monitoring in a small community. We demonstrated that it is possible to design traffic monitoring campaigns with multiple goals (e.g., estimating performance measures and developing spatial models). Outputs from our approach could be used to (1) assess land use patterns that are correlated with high rates of active travel and (2) provide inputs for exposure assessment (e.g., calculating crash rates or exposure to other hazards). Our work serves as a proof-of-concept on a relatively small transportation network and could potentially be extended to larger urban areas.

Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

ABSTRACTShort period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.

Designing a bicycle and pedestrian traffic monitoring program to estimate annual average daily traffic in a small rural college town

Cycling and walking are commonly recognized as energy-efficient alternatives to motorized transport. Research and practice lack a comprehensive set of methods to assess spatiotemporal patterns of traffic volumes across an entire transportation network. Current non-motorized traffic monitoring programs are primarily implemented in urban areas and for singular components of the network (e.g., off-street trails, specific corridors). Our approach synthesizes ongoing efforts in non-motorized traffic monitoring to estimate Annual Average Daily Traffic (AADT), across an entire network in Blacksburg, VA – a small, rural college town. We selected count sites across the network, stratified by street functional class (e.g., major roads, local roads), centrality of the link relative to origins and destinations, and planned bicycle facilities. We collected 45,456h of pedestrian and cyclist counts using three types of automated counters: pneumatic tube (n=12), passive infrared (n=10), and radio beam (n=3) at both reference locations (n=4; 1-year) and short-duration locations (n=97; 1-week) during 2015. We found a strong correlation between manual validation counts and automated counts. We used day-of-year scaling factors to estimate AADT for bicycles and pedestrians and found that temporal and spatial patterns differed between modes. Pedestrian volumes were higher and more variable than bicycle volumes (median [interquartile range] AADT for pedestrians: 135 [89–292]; bicycles: 23 [11–43]); both modes were positively correlated with street functional class, presence of facilities, and proximity to campus. Our approach provides insight for planners or policymakers interested in comprehensive monitoring programs to track performance measures or for use in environmental and health impact studies.

AIDS TO NAVIGATION SYSTEMS ON INLAND WATERWAYS AS AN ELEMENT OF COMPETITIVENESS IN ULCV TRAFFIC

Estimation of Annual Average Daily Traffic (AADT) is vital for departments of transportation (DOTs) work because AADT provides the basic information for planning new road construction, determination of roadway geometry, traffic control needs, congestion management strategies and safety considerations. AADT is used to determine state wide vehicle miles travelled on all roads and are used by transportation agencies to determine compliance with federal and state rules and regulations. DOTs spend heavily to collect traffic counts on state roads, but mostly traffic counts are not available for off-system, or low volume roads. Often estimates rely on a comparison with roads that are subjectively considered to be similar. Such comparisons are inherently subject to large errors, and also may not be repeated often enough to remain current. Therefore, a better method is needed for estimating AADT for off-system roads. This research developed a technique to estimate AADT for local roads in Alabama incorporating various facets from previous studies. A model has been developed using linear regression using known AADTs and collection of socio-economic and location variables as a means to estimate the AADT. The model relied upon five independent variables: nearby population, number of households in the area, employment in the area, population to job ratio and access to major roads. The model was used to generate AADT estimates on low-volume rural, local roads for 12 counties in Alabama. The model was developed using 70 percent of the collected data and validate to the remaining 30 percent of the data. Consistent with the recent literature on AADT estimation, a log transformation was attempted to determine if any improvements were determined. The paper concludes that a straight linear regression model can be used to predict the AADT for low-volumes roadways in Alabama for future applications.

Pavement performance evaluation models for South Carolina

This paper develops pavement performance evaluation models using data from primary and interstate highway systems in the state of South Carolina, USA. Twenty pavement sections are selected from across the state, and historical pavement performance data of those sections are collected. A total of 8 models were developed based on regression techniques, which include 4 for Asphalt Concrete (AC) pavements and 4 for Jointed Plain Concrete Pavements (JPCP). Four different performance indicators are considered as response variables in the statistical analysis: Present Serviceability Index (PSI), Pavement Distress Index (PDI), Pavement Quality Index (PQI), and International Roughness Index (IRI). Annual Average Daily Traffic (AADT), Free Flow Speed (FFS), precipitation, temperature, and soil type (soil Type A from Blue Ridge and Piedmont Region, and soil Type B from Coastal Plain and Sediment Region) are considered as predictor variables. Results showed that AADT, FFS, and precipitation have statistically significant effects on PSI and IRI for both JPCP and AC pavements. Temperature showed significant effect only on PDI and PQI (p < 0.01) for AC pavements. Considering soil type, Type B soil produced statistically higher PDI and PQI (p < 0.01) compared to Type A soil on AC pavements; whereas, Type B soil produced statistically higher IRI and PSI (p < 0.001) compared to Type A soil on JPCP pavements. Using the developed models, local transportation agencies could estimate future corrective actions, such as maintenance and rehabilitation, as well as future pavement performances.

Evaluation of Safety Performance Functions Based on Experimental Design Using a Cross-Validation Method

The Highway Safety Manual (HSM) Part C provides a series of safety performance functions (SPFs) for different roadway conditions. The SPFs suggested in the HSM are formulated on the basis of exposure variables: the logarithms of the annual average daily traffic (AADT) on the major road and on the minor road under the base condition. In this research, data from 7,802 intersections in Florida were collected and processed. These intersections were categorized into seven types based on area type (rural or urban), number of legs (three or four), and number of approaches controlled by stop signs. Twenty-two SPF formulations, including the one suggested by the HSM, were developed for each intersection type for examination of the goodness-of-fit measures of the SPFs. In addition, the goodness of fit of each model of the 22 SPFs in each category was examined with 10-fold leave-one-out cross-validation (LOOCV). With a comparison of the delta values generated with the LOOCV method, it is suggested that the SPF with the logarithm of the total entering vehicle volume and the ratio of the AADT on the minor road and the AADT on the major road are important. In addition, the SPFs with the AADT on the major road and the AADT on the minor road and their logarithmic transformations are also important. Therefore, it is suggested that the future HSM compare these two SPF formulations—as suggested in the current research, along with the original SPF formulation in the manual—and select the one with the best model fit on the basis of the delta value using LOOCV.

Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data

Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.

Road Fatality Model Based on Over-Dispersion Data Along Federal Route F0050

According to The World Health Ranking 2011 has ranked Malaysia as 20 th in its list of countries with the most deaths caused by road accidents. Road accidents also have been identified as the prime cause of death in Malaysia after the heart disease, stroke, influenza and pneumonia. To date, previous researches from Malaysian Institute of Road Safety (MIROS) have reported that averages of 18 people were killed on Malaysian road daily. There are many kinds of models that have been developed in modelling the circumstance of accidents. The most widely applied was Poisson and Negative Binomial regression models while Zeroinflated Poisson and Zero-inflated Negative Binomial are the modification of Poisson and Negative Binomials regression models. This study interested to focus on road F0050 as statistic data from Royal Malaysian Police 2014 list F0050 as one of the high accident road in Malaysia from kilometre 0 until kilometre 58. R programming was chose to analyse the relationship between road fatality and its factor (annual average daily traffic (AADT), speed, shoulder width, lane width). Negative binomial and Zero-inflated negative binomial (ZINB) were shown to be preferred modelling methods for this study. Significant positive relationships were also identified between road fatality and annual average daily traffic (AADT) and lane width. This relationship can be a helpful support to the decision making of accident management for road F0050.

Statistical estimation and prediction of Annual Average Daily Traffic (AADT) on the first/last mile road sections in the Port of Szczecin

Deepening the fairway Świnoujście-Szczecin to a depth of 12.5 m will improve access to the port of Szczecin from the sea. Larger vessels will be able to call at the port of Szczecin and thus the current trends in cargo turnover will probably change. To make it possible, it is also necessary to invest in improving port access from the mainland. In the article the authors present estimates and forecasts of the annual average daily traffic of vehicles (AADT) on national road no. 10 (DK 10) and access roads to the port of Szczecin (so called the last mile sections). Estimation was based on the author's own traffic research carried out in September-October 2016 as part of Feasibility Study for the project Modernization of the access roads to the port o/Szczecin: reconstruction of the transport system in the area of Miedzyodrze implemented by the city of Szczecin.

Predicting Day and Night Traffic Volumes on Rural Roads for Statistical Road Safety Modeling

Statistical road safety modelers have commonly used some combination of segment length and traffic volume as measures of exposure. Traffic volume is usually represented in statistical road safety models with annual average daily traffic (AADT), which turns out to be a highly influential right-hand-side variable for regression models of expected crash frequency. Models that use AADT alone do not explicitly capture differences in traffic volume patterns throughout the 24-h day; this factor can have significant effects on safety performance. This study adds to the existing literature by developing more disaggregated estimates of traffic volumes for day and night conditions in rural areas and modeling road safety using those estimates. The proposed approach is demonstrated with the data from all automatic traffic recorder stations in Utah, with subsequent safety analysis focused on rural two-lane horizontal curve segments. Universal kriging, along with multiple covariates, proved to be an effective spatial technique for predicting day and night traffic volumes at unmeasured locations using data from permanent traffic-recording stations. Predicted day and night traffic volume estimates were incorporated into statistical road safety models of the expected number of crashes on rural two-lane horizontal curves to determine how this new information affected safety model estimation results. The parameter estimate for the predicted ratio of night-to-day traffic volume was positive and statistically significant and verified the hypothesis that horizontal curves with higher proportions of traffic at night were expected to experience more crashes than similar curves with higher proportions of traffic during the day.

Multivariate poisson lognormal modeling of crashes by type and severity on rural two lane highways

In an effort to improve traffic safety, there has been considerable interest in estimating crash prediction models and identifying factors contributing to crashes. To account for crash frequency variations among crash types and severities, crash prediction models have been estimated by type and severity. The univariate crash count models have been used by researchers to estimate crashes by crash type or severity, in which the crash counts by type or severity are assumed to be independent of one another and modelled separately. When considering crash types and severities simultaneously, this may neglect the potential correlations between crash counts due to the presence of shared unobserved factors across crash types or severities for a specific roadway intersection or segment, and might lead to biased parameter estimation and reduce model accuracy. The focus on this study is to estimate crashes by both crash type and crash severity using the Integrated Nested Laplace Approximation (INLA) Multivariate Poisson Lognormal (MVPLN) model, and identify the different effects of contributing factors on different crash type and severity counts on rural two-lane highways. The INLA MVPLN model can simultaneously model crash counts by crash type and crash severity by accounting for the potential correlations among them and significantly decreases the computational time compared with a fully Bayesian fitting of the MVPLN model using Markov Chain Monte Carlo (MCMC) method. This paper describes estimation of MVPLN models for three-way stop controlled (3ST) intersections, four-way stop controlled (4ST) intersections, four-way signalized (4SG) intersections, and roadway segments on rural two-lane highways. Annual Average Daily traffic (AADT) and variables describing roadway conditions (including presence of lighting, presence of left-turn/right-turn lane, lane width and shoulder width) were used as predictors. A Univariate Poisson Lognormal (UPLN) was estimated by crash type and severity for each highway facility, and their prediction results are compared with the MVPLN model based on the Average Predicted Mean Absolute Error (APMAE) statistic. A UPLN model for total crashes was also estimated to compare the coefficients of contributing factors with the models that estimate crashes by crash type and severity. The model coefficient estimates show that the signs of coefficients for presence of left-turn lane, presence of right-turn lane, land width and speed limit are different across crash type or severity counts, which suggest that estimating crashes by crash type or severity might be more helpful in identifying crash contributing factors. The standard errors of covariates in the MVPLN model are slightly lower than the UPLN model when the covariates are statistically significant, and the crash counts by crash type and severity are significantly correlated. The model prediction comparisons illustrate that the MVPLN model outperforms the UPLN model in prediction accuracy. Therefore, when predicting crash counts by crash type and crash severity for rural two-lane highways, the MVPLN model should be considered to avoid estimation error and to account for the potential correlations among crash type counts and crash severity counts.

The effect of traffic data source on deterioration rates of heavy-duty flexible pavements

The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.

Road safety performance measures and AADT uncertainty from short-term counts

ObjectiveThe objective of this paper is to enable better risk analysis of road safety performance measures by creating the first knowledge base on uncertainty surrounding annual average daily traffic (AADT) estimates when the estimates are derived by expanding short-term counts with the individual permanent counter method. BackgroundMany road safety performance measures and performance models use AADT as an input. While there is an awareness that the input suffers from uncertainty, the uncertainty is not well known or accounted for. MethodThe paper samples data from a set of 69 permanent automatic traffic recorders in Manitoba, Canada, to simulate almost 2 million short-term counts over a five year period. These short-term counts are expanded to AADT estimates by transferring temporal information from a directly linked nearby permanent count control station, and the resulting AADT values are compared to a known reference AADT to compute errors. The impacts of five factors on AADT error are considered: length of short-term count, number of short-term counts, use of weekday versus weekend counts, distance from a count to its expansion control station, and the AADT at the count site. ResultsThe mean absolute transfer error for expanded AADT estimates is 6.7%, and this value varied by traffic pattern group from 5% to 10.5%. Reference percentiles of the error distribution show that almost all errors are between −20% and +30%. Error decreases substantially by using a 48-h count instead of a 24-h count, and only slightly by using two counts instead of one. Weekday counts are superior to weekend counts, especially if the count is only 24h. Mean absolute transfer error increases with distance to control station (elasticity 0.121, p=0.001), and increases with AADT (elasticity 0.857, p<0.001). ImplicationsThese results can support evidence-based risk analysis of road safety performance measures that use AADT as inputs. Analytical frameworks for such analysis exist but are infrequently used in road safety because the evidence base on AADT uncertainty is not well developed.

Prediction of secondary crash frequency on highway networks

Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact.

Backpropagation Neural Network to estimate pavement performance: dealing with measurement errors

The objective of this study is to apply the Backpropagation Neural (BPN) network with Generalised Delta Rule learning algorithm for reducing the measurement errors of pavement performance modelling. The Multi-Layer Perceptron network and sigmoid activation function are applied to build the BPN network of Pavement Condition Index (PCI). Collector and arterial roads of both flexible and rigid pavements in Montreal City are taken as a case study. The input variables of the PCI are Average Annual Daily Traffic (AADT), Equivalent Single Axle Loads (ESALs), Structural Number (SN), pavement age, slab thickness and difference in the PCI between the current and preceding year (ΔPCI). The BPN networks estimate that the PCI has inverse relationships with AADT, ESALs and pavement age. The PCI has positive relationships with these variables for roads that have recent treatment operations. The PCI has positive relationships with SN and slab thickness that imply the increase in pavement condition with increasing structural strength and slab thickness. The ΔPCI significantly influences the estimation of PCI values. The AADT and ESALs have considerable importance; however, pavement age and structural characteristics of the pavement have an insignificant influence in determining the PCI values, except in the case of flexible arterial roads.

Methods to improve traffic flow and noise exposure estimation on minor roads

Address-level estimates of exposure to road traffic noise for epidemiological studies are dependent on obtaining data on annual average daily traffic (AADT) flows that is both accurate and with good geographical coverage. National agencies often have reliable traffic count data for major roads, but for residential areas served by minor roads, especially at national scale, such information is often not available or incomplete. Here we present a method to predict AADT at the national scale for minor roads, using a routing algorithm within a geographical information system (GIS) to rank roads by importance based on simulated journeys through the road network. From a training set of known minor road AADT, routing importance is used to predict AADT on all UK minor roads in a regression model along with the road class, urban or rural location and AADT on the nearest major road. Validation with both independent traffic counts and noise measurements show that this method gives a considerable improvement in noise prediction capability when compared to models that do not give adequate consideration to minor road variability (Spearman’s rho. increases from 0.46 to 0.72). This has significance for epidemiological cohort studies attempting to link noise exposure to adverse health outcomes.

International Roughness Index specifications around the world

The International Roughness Index (IRI) is an indicator used worldwide for the characterisation of longitudinal road roughness. This study summarised IRI limit values for new, reconstructed, or rehabilitated roads; for in-service (existing) roads; and road classification schemes used around the world. An overview of practices in 35 US states and 29 non-US states was provided. Limit values are a function of road surface type, road functional category, road speed limit, road construction type, or average annual daily traffic (AADT). IRI specifications are defined for a broad range of evaluation lengths from several metres to the entire length of a section. Large differences in IRI limit values were observed for the same segment length among various countries. The IRI-based methodology used in US states was compared with that used in non-US countries. Non-US countries used more often specifications as a function of road functional category and AADT, and are based on percentile of IRI observations. US states used more often pay adjustment and specifications as a function of road construction type and road speed limit.