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.

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.

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

2020 ◽  
Vol 2674 (10) ◽  
pp. 583-598 ◽  
Author(s):  
Md Mehedi Hasan ◽  
Jun-Seok Oh
Keyword(s):  
Pattern Recognition ◽  
Travel Behavior ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Seasonal Adjustment ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Adjustment Factors ◽  
Clustering Approach

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.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume

2012 ◽  
Vol 2308 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Gregorio Gecchele ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Volume ◽  
Annual Average ◽  
Annual Average Daily Traffic

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.

scholarly journals Development and Evaluation of Recurrent Neural Network-Based Models for Hourly Traffic Volume and Annual Average Daily Traffic Prediction

2019 ◽  
Vol 2673 (7) ◽  
pp. 489-503 ◽  
Author(s):  
Zadid Khan ◽  
Sakib Mahmud Khan ◽  
Kakan Dey ◽  
Mashrur Chowdhury
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Traffic Volume ◽  
Series Data ◽  
Annual Average ◽  
Forecasting Models ◽  
Annual Average Daily Traffic ◽  
Better Than ◽  
Temporal Dependencies

The prediction of high-resolution hourly traffic volumes of a given roadway is essential for transportation planning. Traditionally, automatic traffic recorders (ATR) are used to collect these hourly volume data. These large datasets are time-series data characterized by long-term temporal dependencies and missing values. Regarding the temporal dependencies, all roadways are characterized by seasonal variations that can be weekly, monthly or yearly, depending on the cause of the variation. Traditional time-series forecasting models perform poorly when they encounter missing data in the dataset. To address this limitation, robust, recurrent neural network (RNN)-based, multi-step-ahead forecasting models are developed for time-series in this study. The simple RNN, the gated recurrent unit (GRU) and the long short-term memory (LSTM) units are used to develop the forecasting models and evaluate their performance. Two approaches are used to address the missing value issue: masking and imputation, in conjunction with the RNN models. Six different imputation algorithms are then used to identify the best model. The analysis indicates that the LSTM model performs better than simple RNN and GRU models, and imputation performs better than masking to predict future traffic volume. Based on analysis using 92 ATRs, the LSTM-Median model is deemed the best model in all scenarios for hourly traffic volume and annual average daily traffic (AADT) prediction, with an average root mean squared error (RMSE) of 274 and mean absolute percentage error (MAPE) of 18.91% for hourly traffic volume prediction and average RMSE of 824 and MAPE of 2.10% for AADT prediction.

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
Sign in / Sign up

Export Citation Format

Share Document