Improving Stratification Procedures and Accuracy of Annual Average Daily Traffic (AADT) Estimates for Non-Federal Aid-System (NFAS) Roads

The 2016 safety Final Rule requires states to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid-system (NFAS) roads. The latter account approximately for 75% of the total roadway mileage in the country making it difficult for agencies to collect traffic data on these roads. Many agencies use stratified sampling procedures to develop default AADT estimates for uncounted segments; however, there is limited guidance and information about how to stratify the network effectively. The goal of this paper is to enhance transportation agencies’ ability to improve existing stratification schemes, design new schemes, and ultimately develop more accurate AADT estimates for NFAS roads. The paper presents the results from five pilot studies that validated and compared the performance of current, updated, and new (traditional and decision-tree-based) schemes using readily available data. According to the results, the median absolute percent error of existing AADT estimates, developed by state agencies, ranged between 71% and 120%. Updating these schemes using recent counts resulted in an AADT accuracy improvement of 25%. The best-performing schemes were developed using DTs that improved the AADT accuracy of existing schemes by 41%. Overall, having more strata and very homogenous strata is better than having fewer strata and more samples within each stratum. The analysis revealed that a key to selecting an effective scheme is to determine a critical point, beyond which creating more strata improves the AADT accuracy marginally but increases the required sample size exponentially.

Queue Analysis at Work Zones from Field-Collected Data

Estimation of the capacity of work zones is vital to manage the possibility of traffic flows exceeding capacity and resulting in unbearable queues during work zone lane closures. A plethora of research papers have studied several ways to estimate work zone capacity, with the Highway Capacity Manual (HCM) having its own methodology to estimate capacity based on various site characteristics. However, HCM always recommends validating its model with local data to reflect the actual driving behavior of the region. This study considered work zone capacity as a function of queue discharge rate (QDR), defined as the 15-min average flow rate immediately after breakdown, also known as postbreakdown flow rate. By collecting data from 10 different work zones within the state of Louisiana, the study estimated QDR and its corresponding duration at breakdowns. An average QDR of 1,664 pcphpl and an associated queue of 120 min average duration was found. Analysis of variance showed that average QDRs across all sites were not significantly different. The QDR prediction model revealed that a closed right lane and a work zone on linear roadways significantly increased the discharge rate. However, the presence of nearby exit ramps, daytime scenarios, and an increase in the speed ratio and truck percentages were found to decrease the discharge rate. A separate model for the duration of queue or breakdown found the time of day, change in the speed ratio, presence of entry ramp, location of work zones, and annual average daily traffic of the roadway to be significant variables.

Exploring Vehicle Probe Data as a Resource to Enhance Network-Wide Traffic Volume Estimates

Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations.

Seasonal Signals Observed in Non-Contact Long-Term Road Texture Measurements

Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The “rate of change” is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning. In the summer, this signal is evident as a transient peak in the “rate of change” of texture greater than 0.04 mm, and in the winter as a reduction. The transient changes in texture corresponded to above average rainfall occurring in the week prior to SMTD measurement. The signal observed demonstrates an inverse pattern to the classically understood seasonal variation of skid resistance in the UK, where values are low in the summer and high in the winter. The findings demonstrate for the first time that texture measurements experience a seasonal signal, and provide compelling evidence pointing toward surface processes (such as polishing and the wetting and drying of surface contaminants) causing changes to texture that are affecting seasonal variation in skid resistance.

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

Modelling the Safety Performance of Roadways for Specific Collision Types

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

Modelling the Safety Performance of Roadways for Specific Collision Types

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

Traffic Noise Annoyance in the Population of North Mexico: Case Study on the Daytime Period in the City of Matamoros

Aim: The presence of noise in urban environments is rarely considered a factor that causes damage to the environment. The primary generating source is transportation means, with vehicles being the ones that affect cities the most. Traffic noise has a particular influence on the quality of life of those who are exposed to it and can cause health alterations ranging from annoyance to cardiovascular diseases. This study aims to describe the relationship between the traffic noise level and the perceived annoyance in the inhabitants of a city on the Northern Border of Mexico. The work carried out in a city represents the vulnerability characteristics: economic, social, and migratory of its sizable portion of the inhabitants. Due to that, it is impossible to identify precisely the number of residents as the number of vehicles in circulation.Methods: The streets and avenues with an annual average daily traffic of more than 1,000 vehicles were considered for the measurement of traffic noise. The equipment used was a vehicle gauge with non-invasive speed radar; type I integrating sound level meters, with their respective gauges and tripods. A questionnaire was applied to people living within 250 m of the streets and avenues in which the noise was measured.Results: The noise measurement found a parameter of LAeq estimated for 12 h during the day, exceeding 70 dBA. The data received from the questionnaire were statistically tested by using Pearson's correlation tests. A total number of 2,350 people were participated, of whom 1,378 were women (58.6%) and 972 were men (41.4%). The age of participants is ranged from 18 to 75 years. The overall perception of traffic noise annoyance identified that 1,131 participants (48.1%) responded “Yes” as they considered the noise annoying. Participants who responded “No” as well as those who responded “Do not know” resulted in a total of 1,219 people (51.9%).Conclusion: The results show that the population is desensitized to traffic noise and does not perceive it as an annoyance. The flow of vehicles and the type of vehicles are the significant factors for the propagation and increase in the traffic noise levels. Women present a considerable appreciation of traffic noise perception instead of younger people who demonstrate a higher tolerance to high-level exposure. This reflects the lack of information of the population around the noise problem and its effects.