Traffic Count Estimates for Short-Term Traffic Monitoring Sites: Simulation Study

1998 ◽  
Vol 1625 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Patricia S. Hu ◽  
Tommy Wright ◽  
Tony Esteve
Keyword(s):  
Simulation Study ◽  
Traffic Monitoring ◽  
Highway Maintenance ◽  
Short Term ◽  
Annual Average ◽  
Vehicle Class ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Term Monitoring ◽  
Road Segments

Traffic characteristics, such as the annual average daily traffic (AADT) and the AADT for each vehicle class, are essential for highway maintenance and planning. In practice, selected road segments are monitored continuously every day of the year to identify their traffic characteristics. A sample of the remaining road segments is monitored for 1 or 2 d each year, and the resulting data are adjusted (by using factors based on data collected from the continuously monitored road segments) to produce estimates of annual average daily traffic characteristics. A simulation study empirically considered how the precision of an estimate from a continuously monitored site compares with the precision of an estimate from a short-term monitored site. The original estimates of traffic characteristics (i.e., AADT and AADT by vehicle class) treating the site as a continuously monitored site are on average quite close to, but smaller than, the simulated estimates treating the site as a short-term monitored site. The original estimates (continuous monitoring) appear to be more precise, on average, than the simulated estimates (short-term monitoring). This decrease in precision typically occurs for vehicle classes that account for less than 1 percent of the daily traffic volume, suggesting that these less-common vehicle classes could be combined to achieve reliable AADT estimates.

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

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 about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

Conducting Environmental Assessments for Physical Activity: Determining Traffic Volume in Walkability Audits in Two West Virginia Communities

2008 ◽  
Vol 5 (6) ◽  
pp. 909-917 ◽  
Author(s):  
Karen K. Lee ◽  
Candace D. Rutt ◽  
Andrea Sharma ◽  
Michael Pratt ◽  
Judd Flesch ◽  
...  
Keyword(s):  
West Virginia ◽  
Weighted Kappa ◽  
Traffic Volume ◽  
Department Of Transportation ◽  
Annual Average ◽  
Traffic Counts ◽  
High Agreement ◽  
Suburban Areas ◽  
Annual Average Daily Traffic ◽  
Road Segments

Background:In this article, we examine the possibility of reducing time to conduct traffic volume audits through (1) reducing time for manual traffic counting and (2) using Department of Transportation (DOT) information.Methods:In audits of 824 road segments in 2 West Virginia (WV) communities, manual traffic counts were recorded for 1, 2, and 5 minutes in duration. Annual Average Daily Traffic (AADT) was calculated from counts. Available AADT from DOT was also collected. Percent agreement and a weighted kappa were calculated between 5-minute count and 1- and 2-minute count AADT categories and between 5-minute count and DOT AADT categories.Results:One- and 2-minute counts produced identical AADT categories as 5-minute counts in 93.4% and 95.0% of segments, respectively. Weighted kappa was 0.79 (95% CI = 0.74–0.85) and 0.85 (95% CI = 0.80–0.89), respectively. Forty-two segments (5.1%) had DOT data.Conclusions:DOT AADT was available for a small percentage of road segments assessed. The high agreement between AADT categories produced by 1- and 2-minute counts and 5-minute counts makes it reasonable to consider using 1- or 2-minute manual traffic counts if time or staffing constraints make it necessary. Possible generalizability of this methodology to other communities, particularly larger urban and suburban areas, will require further research.

A statistical practical methodology of statewide traffic pattern grouping and precision analysis

2009 ◽  
Vol 36 (3) ◽  
pp. 427-438 ◽  
Author(s):  
Shy Bassan
Keyword(s):  
Traffic Volume ◽  
Seasonal Adjustment ◽  
Traffic Data ◽  
Annual Average ◽  
Traffic Pattern ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors ◽  
And Performance ◽  
Day By Day

Traffic data in general and traffic volume in particular are collected to determine the use and performance of the roadway system. Due to budget limitations, traffic volume cannot be counted day by day for every roadway within the state. Therefore, the volume on roadways without automatic traffic recorders (ATRs) can be determined by taking portable short-duration counts and using adjustment factors to produce annual average daily traffic (AADT) at a specific location. This study presents a statistical practical methodology that develops traffic pattern groups (TPGs) by combining roadways with similar traffic characteristics such as volume, seasonal variation, and land use in Delaware, USA. Monthly seasonal adjustment factors and their coefficient of variance (FCV) are analyzed for each group. To meet the desired confidence level and precision intervals, the TPGs’ ATR inventory is examined such that the required sample size is determined by the critical month.

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

2017 ◽  
Vol 2644 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Giuseppe Grande ◽  
Steven Wood ◽  
Auja Ominski ◽  
Jonathan D. Regehr
Keyword(s):  
Count Data ◽  
Weighted Average ◽  
Traffic Monitoring ◽  
Local Context ◽  
Traffic Data ◽  
Data Loss ◽  
Truck Traffic ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Accuracy And Precision

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.

Using Seasonal and Day-of-Week Factoring to Improve Estimates of Truck Vehicle Miles Traveled

1996 ◽  
Vol 1522 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Herbert Weinblatt
Keyword(s):  
Short Duration ◽  
Single Unit ◽  
Vehicle Miles Traveled ◽  
Annual Average ◽  
Vehicle Class ◽  
Traffic Counts ◽  
Annual Average Daily Traffic ◽  
Traffic Volumes ◽  
Total Vehicle ◽  
Unbiased Estimates

Procedures developed by FHWA for “factoring” short-duration traffic counts for seasonal and day-of-week variations in traffic volumes are capable of producing estimates of annual average daily traffic (AADT) that are quite accurate. Moreover, there is virtually no bias in these estimates, so AADT estimates for a set of road sections can be used to produce unbiased estimates of total vehicle miles traveled (VMT) for systems of roads. Unfortunately, corresponding procedures are not generally used for estimating AADT by vehicle class, and the less sophisticated procedures that are commonly used contribute to substantial overestimates of truck AADT and VMT. Current procedures apparently overestimate VMT by 25 to 40 percent for combination trucks and possibly more for single-unit trucks. Modified versions of the FHWA factoring procedure that are capable of producing substantially improved estimates of truck VMT and of AADT of combination trucks are presented. These procedures use seasonal and day-of-week factoring to reduce the errors in truck AADT estimates and to eliminate the upward bias in truck VMT estimates that result from the use of unfactored weekday classification counts.

scholarly journals Development of Statewide Annual Average Daily Traffic Estimation Model from Short-Term Counts: A Comparative Study for South Carolina

2018 ◽  
Vol 2672 (43) ◽  
pp. 55-64 ◽  
Author(s):  
Sakib Mahmud Khan ◽  
Sababa Islam ◽  
MD Zadid Khan ◽  
Kakan Dey ◽  
Mashrur Chowdhury ◽  
...  
Keyword(s):  
South Carolina ◽  
Traffic Engineering ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Short Term ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Functional Classes ◽  
Estimation Models ◽  
Aadt Estimation

Annual Average Daily Traffic (AADT) is an important parameter for traffic engineering analysis. Departments of Transportation continually collect traffic count using both permanent count stations (i.e., Automatic Traffic Recorders or ATRs) and temporary short-term count stations. In South Carolina, 87% of the ATRs are located on interstates and arterial highways. For most secondary highways (i.e., collectors and local roads), AADT is estimated based on short-term counts. This paper develops AADT estimation models for different roadway functional classes with two machine learning techniques: Support Vector Regression (SVR) and Artificial Neural Network (ANN). The models predict AADT from short-term counts. The results are first compared against each other, using the 2011 ATR data, to identify the best models. Then, the results of the best models are compared against both the regression-based model and factor-based model. The comparison reveals the superiority of the SVR model for AADT estimation for different roadway functional classes over all other methods. Among models for different roadway functional classes, developed with the 2011 ATR data, the SVR-based models show minimum errors in estimating AADT compared to the ANN-based, regression-based, and factor-based models, depicting the superiority of the SVR-based model for all roadway functional classes over other models in terms of AADT estimation accuracy. SVR models are validated for each roadway functional class using the 2016 ATR data and short-term count data collected by the South Carolina Department of Transportation (SCDOT). The validation results show that the SVR-based AADT estimation models can be used by the SCDOT as a reliable option to predict AADT from the short-term counts.

An Empirical Analysis of the Obtrusiveness of and Participants' Compliance with the Electronically Activated Recorder (EAR)

2007 ◽  
Vol 23 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Matthias R. Mehl ◽  
Shannon E. Holleran
Keyword(s):  
Empirical Analysis ◽  
Sampling Method ◽  
Daily Life ◽  
Assessment Method ◽  
Data Confidentiality ◽  
Short Term ◽  
Naturally Occurring ◽  
Two Samples ◽  
Audio Recorder ◽  
Term Monitoring

Abstract. In this article, the authors provide an empirical analysis of the obtrusiveness of and participants' compliance with a relatively new psychological ambulatory assessment method, called the electronically activated recorder or EAR. The EAR is a modified portable audio-recorder that periodically records snippets of ambient sounds from participants' daily environments. In tracking moment-to-moment ambient sounds, the EAR yields an acoustic log of a person's day as it unfolds. As a naturalistic observation sampling method, it provides an observer's account of daily life and is optimized for the assessment of audible aspects of participants' naturally-occurring social behaviors and interactions. Measures of self-reported and behaviorally-assessed EAR obtrusiveness and compliance were analyzed in two samples. After an initial 2-h period of relative obtrusiveness, participants habituated to wearing the EAR and perceived it as fairly unobtrusive both in a short-term (2 days, N = 96) and a longer-term (10-11 days, N = 11) monitoring. Compliance with the method was high both during the short-term and longer-term monitoring. Somewhat reduced compliance was identified over the weekend; this effect appears to be specific to student populations. Important privacy and data confidentiality considerations around the EAR method are discussed.

scholarly journals AB1082 INFLUENCE OF THE VARIATION OF THE OPERATOR, PATIENT POSITION AND DEVICE ON THE MEASUREMENT PERFORMANCE OF RADIOFREQUENCY ECHOGRAPHIC MULTI SPECTROMETRY (REMS)

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1830.1-1830
Author(s):  
C. Caffarelli ◽  
G. Adami ◽  
G. Arioli ◽  
G. Bianchi ◽  
M. L. Brandi ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Femoral Neck ◽  
High Performance ◽  
Mineral Density ◽  
Research Support ◽  
Short Term ◽  
Alphabetical Order ◽  
Patient Position ◽  
Term Monitoring

Background:The monitoring of bone mineral density (BMD) is a key aspect for patients undergoing pharmacological treatments that might cause BMD changes at non-physiological rates. At present, the short-term follow-up of patients under treatment in terms of BMD change with time remains an unmet clinical need, since the current techniques, including the gold standard dual X-ray absorptiometry (DXA), require at least 1 year between two consecutive measurements [1]. Therefore, an effective strategy for the assessment of BMD should guarantee high accuracy, precision and repeatability of the measurements.Objectives:The aim is to assess the influence of the variation 1) in patient position, 2) operator (both intra- and inter-) and 3) device on the REMS performance at lumbar spine and femoral neck.Methods:210 women were enrolled, divided in 7 groups of 30-patient each for the assessment of the parameters of interest, i.e. inter-device, intra- and inter-operator repeatability for lumbar spine scans and inter-patient position, inter-device, intra- and inter-operator repeatability for femoral neck scans.All patients underwent 2 REMS scans at lumbar spine or femoral neck, performed by the same operator or by 2 different operators or by the same operator using 2 different devices or in different patient position (i.e. supine without constraints or with a constrained 25°-rotation of the leg). The percentage coefficient of variation (CV%) with 95% confidence interval and least significant change for a 95% confidence level (LSC) have been calculated.Results:For lumbar spine, intra-operator repeatability resulted in CV%=0.37% (95%CI: 0.26%-0.48%), with LSC=1.02%, inter-operator repeatability resulted in CV%=0.55% (95% CI: 0.42%-0.68%), with LSC=1.52%, inter-device repeatability resulted in CV%=0.53% (95% CI: 0.40%-0.66%), with LSC=1.47%.For femoral neck, intra-operator repeatability resulted in CV%=0.33% (95%CI: 0.23%-0.43%), with LSC=0.91%, inter-operator repeatability resulted in CV%=0.47% (95% CI: 0.35%-0.59%), with LSC=1.30%, inter-device repeatability resulted in CV%=0.42% (95% CI: 0.30%-0.51%), with LSC=1.16%, inter-patient position repeatability resulted in CV%=0.24% (95% CI: 0.18%-0.30%), with LSC=0.66%.Conclusion:REMS densitometry is highly precise for both anatomical sites, showing high performance in repeatability. These results suggest that REMS might be a suitable technology for short-term monitoring. Moreover, thanks to its ionizing radiation-free approach, it might be applied for population mass investigations and prevention programs also in paediatric patients and pregnant women.References:Note:Carla Caffarelli, Giovanni Adami§, Giovanni Arioli§, Gerolamo Bianchi§, Maria Luisa Brandi§, Sergio Casciaro§, Luisella Cianferotti§, Delia Ciardo§, Francesco Conversano§, Davide Gatti§, Giuseppe Girasole§, Monica Manfredini§, Maurizio Muratore§, Paola Pisani§, Eugenio Quarta§, Laura Quarta§, Stefano Gonnelli§Equal contributors listed in alphabetical orderDisclosure of Interests:Carla Caffarelli: None declared, Giovanni Adami: None declared, Giovanni Arioli *: None declared, Gerolamo Bianchi Grant/research support from: Celgene, Consultant of: Amgen, Janssen, Merck Sharp & Dohme, Novartis, UCB, Speakers bureau: Abbvie, Abiogen, Alfa-Sigma, Amgen, BMS, Celgene, Chiesi, Eli Lilly, GSK, Janssen, Medac, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Sanofi Genzyme, Servier, UCB, Maria Luisa Brandi: None declared, Sergio Casciaro: None declared, Luisella Cianferotti: None declared, Delia Ciardo: None declared, Francesco Conversano: None declared, Davide Gatti Speakers bureau: Davide Gatti reports personal fees from Abiogen, Amgen, Janssen-Cilag, Mundipharma, outside the submitted work., Giuseppe Girasole: None declared, Monica Manfedini: None declared, Maurizio Muratore: None declared, Paola Pisani: None declared, Eugenio Quarta: None declared, Laura Quarta: None declared, Stefano Gonnelli: None declared

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