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.

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 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

scholarly journals Estimation of annual average daily traffic with optimal adjustment factors

2015 ◽  
Vol 168 (5) ◽  
pp. 406-414 ◽  
Author(s):  
Borja Alonso ◽  
José Luis Moura ◽  
Angel Ibeas ◽  
Juan Pablo Romero
Keyword(s):  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors ◽  
Optimal Adjustment

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.

scholarly journals A Traffic Prediction Model for Self-Adapting Routing Overlay Network in Publish/Subscribe System

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Meng Chi ◽  
Jianhua Yang ◽  
Yabo Liu ◽  
Zhenhui Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Large Scale ◽  
Overlay Network ◽  
Experimental Results ◽  
Traffic Prediction ◽  
Model Integration ◽  
Traffic Data ◽  
Location Based Service ◽  
Ideal Situation

In large-scale location-based service, an ideal situation is that self-adapting routing strategies use future traffic data as input to generate a topology which could adapt to the changing traffic well. In the paper, we propose a traffic prediction model for the broker in publish/subscribe system, which can predict the traffic of the link in future by neural network. We first introduced our traffic prediction model and then described the model integration. Finally, the experimental results show that our traffic prediction model could predict the traffic of link well.

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.

scholarly journals Estimation of annual average daily traffic with optimal adjustment factors

2015 ◽  
Vol 168 (5) ◽  
pp. 406-414
Author(s):  
Borja Alonso ◽  
Angel Ibeas ◽  
José Luis Moura ◽  
Juan Pablo Romero
Keyword(s):  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors ◽  
Optimal Adjustment
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