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.

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.

Estimating Annual Average Daily Bicycle Traffic without Permanent Counter Stations

2018 ◽  
Vol 2672 (43) ◽  
pp. 145-153
Author(s):  
Josh F. Roll ◽  
Frank R. Proulx
Keyword(s):  
Short Duration ◽  
Count Data ◽  
Statistical Models ◽  
Traffic Monitoring ◽  
Basic Unit ◽  
Seasonal Adjustment ◽  
Annual Average ◽  
Bicycle Infrastructure ◽  
Traffic Volumes ◽  
Paper Advance

Traffic volumes are a basic unit of measurement for understanding the transportation system. As investments in bicycle infrastructure are made, similar measures are necessary for understanding this non-motorized mode of travel. Methods for estimating annual average daily bicycle traffic (AADBT) are still developing, but generally employ techniques used in the motorized traffic monitoring field whereby data from permanent counters are used to construct expansion factors that are then applied to short-duration counts. This approach requires a network of permanent counters and knowledge about how to group factors into appropriate categories based on patterns observed in both the short-duration and permanent counter data. The methods presented in this paper advance a new approach to estimating AADBT solely using short-duration counts. The Seasonal Adjustment Regression Model uses statistical models that relate the daily bicycle volume to daily conditions and weather variables at a given count location. These models are then used to predict daily volumes for the remaining days of the year. To verify this approach and determine the resulting error, levels of available short-duration counts using varying amounts of permanent count data were simulated. This method was then applied to short-duration bicycle counts from Eugene, Oregon. With sufficient short-duration count data, this method can produce AADBT estimates with minimal error and without requiring a network of permanent counters. This approach also circumvents the need to determine which expansion factors should be applied to different short-term count locations by using statistical models in place of expansion factors.

scholarly journals ANNUAL AVERAGE DAILY TRAFFIC FORECASTING USING DIFFERENT TECHNIQUES

Transport ◽  
2006 ◽  
Vol 21 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Tomas Šliupas
Keyword(s):  
Growth Factor ◽  
Linear Regression ◽  
Multiple Regression ◽  
Traffic Data ◽  
Annual Average ◽  
Traffic Forecasting ◽  
Annual Average Daily Traffic ◽  
Forecasting Method ◽  
The Future

This paper describes annual average daily traffic (AADT) forecasting for the Lithuanian highways using a forecasting method used by Idaho Department for Transportation, growth factor, linear regression and multiple regression. AADT forecasts obtained using these methods are compared with the forecasts evaluated by traffic experts and given in references. The results show that the best Lithuanian traffic data are obtained using Idaho forecasting method. It is assumed that the curve of AADT change should be exponential in the future.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

2019 ◽  
Vol 11 (3) ◽  
pp. 158-170 ◽  
Author(s):  
Xiaolei Ma ◽  
Sen Luan ◽  
Chuan Ding ◽  
Haode Liu ◽  
Yunpeng Wang
Keyword(s):  
Spatial Interpolation ◽  
Traffic Data ◽  
Annual Average ◽  
Annual Average Daily Traffic

Annual Average Daily Traffic Prediction Model for County Roads

1998 ◽  
Vol 1617 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Dadang Mohamad ◽  
Kumares C. Sinha ◽  
Thomas Kuczek ◽  
Charles F. Scholer
Keyword(s):  
Prediction Model ◽  
Model Development ◽  
Demographic Variables ◽  
Traffic Prediction ◽  
Traffic Data ◽  
Annual Average ◽  
Independent Variables ◽  
State Highway ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors

A traffic prediction model that incorporates relevant demographic variables for county roads was developed. Field traffic data were collected from 40 out of 92 counties in Indiana. The selection of a county was based on population, state highway mileage, per capita income, and the presence of interstate highways. Three to four automatic traffic counters were installed in each selected county. Most counters installed on the selected road sections were based on the standard 48-hour traffic counts. Then, the obtained average daily traffic was converted to annual average daily traffic by means of adjustment factors. Multiple regression analysis was conducted to develop the model. There were quantitative and qualitative predictor variables used in the model development. To validate the developed model, additional field traffic data were collected from eight randomly selected counties. The accuracy measures of the validation showed the high accuracy of the model. The statistical analyses also found that the independent variables employed in the model were statistically significant. The number of independent variables included in the model was kept to a minimum.

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.

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.

Indirect Validation of Probe Speed Data on Arterial Corridors

2017 ◽  
Vol 2643 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Sepideh Eshragh ◽  
Stanley E. Young ◽  
Elham Sharifi ◽  
Masoud Hamedi ◽  
Kaveh Farokhi Sadabadi
Keyword(s):  
Linear Models ◽  
Weighted Average ◽  
Access Point ◽  
Functional Class ◽  
Traffic Data ◽  
Strong Correlations ◽  
Signal Density ◽  
Annual Average Daily Traffic ◽  
Speed Error ◽  
Road Characteristics

This study aimed to estimate the accuracy of probe speed data on arterial corridors on the basis of roadway geometric attributes and functional classification. It was assumed that functional class (medium and low) along with other road characteristics (such as weighted average of the annual average daily traffic, average signal density, average access point density, and average speed) were available as correlation factors to estimate the accuracy of probe traffic data. This study tested these factors as predictors of the fidelity of probe traffic data by using the results of an extensive validation exercise. This study showed strong correlations between these geometric attributes and the accuracy of probe data when they were assessed by using average absolute speed error. Linear models were regressed to existing data to estimate appropriate models for medium- and low-type arterial corridors. The proposed models for medium- and low-type arterials were validated further on the basis of the results of a slowdown analysis. These models can be used to predict the accuracy of probe data indirectly in medium and low types of arterial corridors.

Evaluation of Traffic Data Collection Method

2015 ◽  
Vol 764-765 ◽  
pp. 905-909
Author(s):  
Won Ho Suh ◽  
James Anderson ◽  
Angshuman Guin ◽  
Michael Hunter
Keyword(s):  
Performance Monitoring ◽  
Detection System ◽  
Ground Truth ◽  
Traffic Data ◽  
Accurate Data ◽  
Annual Average ◽  
Collection Method ◽  
Ground Truth Data ◽  
Traffic Data Collection ◽  
Annual Average Daily Traffic

Traffic counts are one of the fundamental data sources for the Highway Performance Monitoring System (HPMS). Automatic Traffic Recorders (ATRs) are used to provide continuous traffic count coverage at selected locations to estimate annual average daily traffic (AADT). However, ATR data is often unavailable. This paper investigated the feasibility of using Video Detection System (VDS) technology when ATR data is not available. An Android Tablet-based manual traffic counting application was developed to acquire manual count based ground truth data. The performance of VDS was evaluated under various conditions including mounting styles, heights, and roadway offsets. The results indicated that VDS data presents reasonably accurate data, although the data exhibits more variability compared to ATR data.

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