Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Tsapakis, Ioannis; Schneider, William H.; Nichols, Andrew P

doi:10.1080/03081060.2011.554709

Cited by 5 publications

(15 citation statements)

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“…Also, when k increases by one, the cluster variability increases more in the total volume than in the directional volume analysis. These results are consistent with the past research findings (Wright et al 1997;Hallenbeck et al 1997;Tsapakis et al 2011) and may be due to the fact that the two directional distributions of a station are in general more similar than the total volume patterns of two randomly selected ATRs; although the ANOVA showed that the directional patterns are statistically different at a 95% confidence level. The previous argument may also explain the finding that when k increases, the directional volume analysis results on average in more factor groupings than the total volume analysis.…”

Section: Discussionsupporting

confidence: 93%

“…According to analysis of variance (ANOVA) tests, conducted on the entire data-set, it was found that statistical differences exist in the traffic patterns of the two directions of flow; therefore, it is appropriate to evaluate directional-specific SAFGs independently. This finding is in line with past research findings, according to which traffic differs dramatically by direction (Wright et al 1997), temporal analyses may be affected by directional patterns (Hallenbeck et al 1997), and the use of directional volume-based SAFs decreases the within-cluster variability (Tsapakis et al 2011). The advantages and disadvantages of the two analyses are examined within the sixth objective of this paper.…”

Section: Study Datasupporting

confidence: 82%

“…To address the six objectives of the study, the ATRs were grouped geographically into north and south Ohio based upon the criteria and the results of the study by Tsapakis et al (2011). The geographical classification relies on previous findings according to which the geographical location plays more important role in seasonal grouping than the roadway functional class of an ATR (Flaherty 1993;Zhao et al 2004).…”

Section: Examined Methodsmentioning

confidence: 99%

“…Taking into account that new SAFGs need to be determined on a yearly basis (if not more frequently as recommended by the FHWA 2001) due to instability and within-group variations over time (Ritchie 1986;Flaherty 1993;Faghri and Hua 1995;FHWA 2001;Zhao et al 2004Zhao et al , 2008Tsapakis, Schneider, and Nichols 2011), this study examines a sound method of developing SAFGs within short computational time. Moreover, the proposed approach minimizes engineering judgment to a certain extent, without completely eliminating it.…”

Section: Purpose and Objectivesmentioning

confidence: 99%

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Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Tsapakis¹,

Schneider²

2015

Transportation Planning and Technology

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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 main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.

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Section: Discussionsupporting

confidence: 93%

Section: Study Datasupporting

confidence: 82%

Section: Examined Methodsmentioning

confidence: 99%

Section: Purpose and Objectivesmentioning

confidence: 99%

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Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Tsapakis¹,

Schneider²

2015

Transportation Planning and Technology

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“…The Traffic Monitoring Guide (TMG) recommends the use of cluster analysis in conjunction with traditional methods for the creation of factor groups. Several studies have investigated different ways of grouping traffic stations; however, limited research efforts have been devoted to assigning short‐term counts to groups of stations .…”

Section: Introductionmentioning

confidence: 99%

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Tsapakis

Schneider

Nichols

et al. 2012

J of Advced Transportation

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SUMMARY The precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

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GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

Hasan

2020

Transportation Research Record

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Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.

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Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Cited by 5 publications

References 10 publications

Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

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