The Experts below are selected from a list of 8973 Experts worldwide ranked by ideXlab platform
Ioannis Tsapakis - One of the best experts on this subject based on the ideXlab platform.
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improving stratification procedures and accuracy of annual Average Daily Traffic aadt estimates for non federal aid system nfas roads
Transportation Research Record, 2021Co-Authors: Ioannis Tsapakis, Steven Jessberger, Subasish Das, Paul T Anderson, William HolikAbstract: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 approx...
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determining the optimal number of seasonal adjustment factor groupings when estimating annual Average Daily Traffic and investigating their characteristics
Transportation Planning and Technology, 2015Co-Authors: Ioannis Tsapakis, William H SchneiderAbstract:Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate ‘practical and sensible’ groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the ma...
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a bayesian analysis of the effect of estimating annual Average Daily Traffic for heavy duty trucks using training and validation data sets
Transportation Planning and Technology, 2013Co-Authors: Ioannis Tsapakis, William H Schneider, Andrew P NicholsAbstract:The precise estimation of annual Average Daily Traffic (AADT) is of significant importance worldwide for transportation agencies. This paper uses three modeling frameworks to predict the AADT for heavy-duty trucks. In total, 12 models are developed based on regression and Bayesian analysis using a training data-set. A separate validation data-set is used to compare the results from the 12 models, spanning the years 2005 through 2007 and taken from 67 continuous data recorders. Parameters of significance include roadway functional class, population density, and spatial location; five regional areas – northeast, northwest, central, southeast, and southwest – of the state of Ohio in the USA; and Average Daily truck Traffic. The results show that a full Bayesian negative binomial model with a coefficient offset is the most efficient model framework for all four seasons of the year. This model is able to account for between 87% and 92% of the variability within the data-set.
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improving the estimation of total and direction based heavy duty vehicle annual Average Daily Traffic
Transportation Planning and Technology, 2011Co-Authors: Ioannis Tsapakis, William H Schneider, Andrew P NicholsAbstract:Abstract The estimation of annual Average Daily Traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4–13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings – which are consistent with previous total volume studies – further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of Traffic...
William Holik - One of the best experts on this subject based on the ideXlab platform.
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improving stratification procedures and accuracy of annual Average Daily Traffic aadt estimates for non federal aid system nfas roads
Transportation Research Record, 2021Co-Authors: Ioannis Tsapakis, Steven Jessberger, Subasish Das, Paul T Anderson, William HolikAbstract: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 approx...
Aemal J Khattak - One of the best experts on this subject based on the ideXlab platform.
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updating annual Average Daily Traffic estimates at highway rail grade crossings with geographically weighted poisson regression
Transportation Research Record, 2019Co-Authors: Huiyuan Liu, Myungwoo Lee, Aemal J KhattakAbstract:Highway-rail grade crossings (HRGCs) are unique nodes in the transportation system that facilitate the movement of rail and highway Traffic. Various mathematical models are available that provide s...
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implications of using annual Average Daily Traffic in highway rail grade crossing safety models
Transportation Research Board 91st Annual MeetingTransportation Research Board, 2012Co-Authors: Aemal J Khattak, Anuj Sharma, Zheng LuoAbstract:Highway-rail grade crossing safety models have been around since at least 1940s. A staple of these models is the annual Average Daily Traffic (AADT), which is a measure of vehicular use of roadways. The main reason for the presence of AADT in safety models is because it is the main contributor to what is termed as “exposure.” This exposure accounts for the state of being subjected to the likelihood of a crash. However, in place of AADT a more relevant measure of exposure at highway-rail grade crossings is the portion of AADT that encounters train Traffic— termed (AADT)TC in this paper. This is more suitable because the exposure of motorists to train-involved crashes in the absence of trains is zero. Consideration of the non-relevant portion of AADT (i.e., vehicular Traffic when no trains are present) can mask the true picture. Therefore, it is prudent to use (AADT)TC, which amounts to stripping AADT of the non-relevant portion. A case of three crossings is presented that are rank ordered based on predicted crashes calculated by using (AADT)TC and AADT in a crash prediction model that was estimated for this study. Results showed that the use of AADT in place of the more relevant (AADT)TC yielded different rank order results. The recommendation is to use (AADT)TC to minimize the possibility of missing crossings that are deserving of attention and perhaps expenditure of safety-related resources. The paper concludes with comments on how transportation agencies may incorporate (AADT)TC in their comparative assessments and future research needs.
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effects of work zone presence on injury and non injury crashes
Accident Analysis & Prevention, 2002Co-Authors: Asad J Khattak, Aemal J KhattakAbstract:Abstract Work zones in the United States have approximately 700 Traffic-related fatalities, 24 000 injury crashes, and 52 000 non-injury crashes every year. Due to future highway reconstruction needs, work zones are likely to increase in number, duration, and length. This study focuses on analyzing the effect of work zone duration mainly due to its policy-sensitivity. To do so, we created a unique dataset of California freeway work zones that included crash data (crash frequency and injury severity), road inventory data (Average Daily Traffic (ADT) and urban/rural character), and work zone related data (duration, length, and location). Then, we investigated crash rates and crash frequencies in the pre-work zone and during-work zone periods. For the freeway work zones investigated in this study, the total crash rate in the during-work zone period was 21.5% higher (0.79 crashes per million vehicle kilometer (MVKM)) than the pre-work zone period (0.65 crashes per MVKM). Compared with the pre-work zone period, the increase in non-injury and injury crash rates in the during-work zone period was 23.8% and 17.3%, respectively. Next, crash frequencies were investigated using negative binomial models, which showed that frequencies increased with increasing work zone duration, length, and Average Daily Traffic. The important finding is that after controlling for various factors, longer work zone duration significantly increases both injury and non-injury crash frequencies. The implications of the study findings are discussed in the paper.
Guo X Liu - One of the best experts on this subject based on the ideXlab platform.
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estimation of annual Average Daily Traffic on low volume roads factor approach versus neural networks
Transportation Research Record, 2000Co-Authors: Satish Sharma, Pawan Lingras, Guo X LiuAbstract:Estimation of the annual Average Daily Traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period Traffic counts. Fifty-five automatic Traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of ...
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neural networks as alternative to traditional factor approach of annual Average Daily Traffic estimation from Traffic counts
Transportation Research Record, 1999Co-Authors: Satish Sharma, Pawan Lingras, Guo X LiuAbstract:Presented in this paper is a comparison of the neural network approach and the traditional factor approach for estimating annual Average Daily Traffic (AADT) from 48-h sample Traffic counts. Minnesota's automatic Traffic recorder (ATR) sites are investigated. The traditional AADT estimation approach involves application of volume adjustment factors to sample counts. The neural network model used in this study is based on a multilayered, feed-forward, and back-propagation design for supervised learning. The results of AADT estimation from a single short-period Traffic count indicate that as compared with the neural network approach, the estimation errors for the factor approach can be lower under a scenario in which ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary for obtaining reliable AADT estimates from sample counts. The advantage of the neural network approach is that classification of ATR sites and sample site assignments are not required. The neural network approach can be particularly suitable for estimating AADT from two or more short-period Traffic counts taken at different times during the counting season.
Steven Jessberger - One of the best experts on this subject based on the ideXlab platform.
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improving stratification procedures and accuracy of annual Average Daily Traffic aadt estimates for non federal aid system nfas roads
Transportation Research Record, 2021Co-Authors: Ioannis Tsapakis, Steven Jessberger, Subasish Das, Paul T Anderson, William HolikAbstract: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 approx...
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improved annual Average Daily Traffic estimation processes
Transportation Research Record, 2016Co-Authors: Steven Jessberger, Robert Krile, Jeremy Schroeder, Frederick Todt, Jingyu FengAbstract:Annual Average Daily Traffic (AADT) volume is estimated for federal, state, and local uses for roadway segments throughout the United States. The AADT estimates derived from permanent or portable counts are critical to roadway planning, system operations, and distribution of funding. The AASHTO AADT estimation formula is the most commonly used method, in part because it can be used under many circumstances when hourly Traffic volume observations are missing, which is a common measurement issue with permanent Traffic-counting sites. Despite its utility, the AASHTO AADT method has limitations in that it requires complete hourly data for any day that is included, and it does not account for variations in the numbers of each day of the week within a month or variations in the number of days within a month. This research evaluated new AADT estimation methods that incorporate days in which some, but not all, hourly observations are available and adjusts volume for the number of times each day of the week occurs...
