Comparative Assessment of Geospatial and Statistical Methods to Estimate Local Road Annual Average Daily Traffic

2021 ◽  
Vol 147 (7) ◽  
pp. 04021035
Author(s):  
Sonu Mathew ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Statistical Methods ◽  
Comparative Assessment ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Local Road

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.

Enhancing Statewide Annual Average Daily Traffic Estimation with Ubiquitous Probe Vehicle Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 649-660
Author(s):  
Xu Zhang ◽  
Mei Chen
Keyword(s):  
Model Performance ◽  
Improvement Program ◽  
Annual Average ◽  
Great Opportunity ◽  
Safety Reporting ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Annual Average Daily Traffic ◽  
Functional Classes ◽  
Aadt Estimation

Annual average daily traffic (AADT) is a critical input into many transportation applications, particularly safety reporting. For example, the Highway Safety Improvement Program in the U.S. requires states to make AADT data for all public paved roadways accessible by 2026. Because collecting traffic counts on every network segment is prohibitively expensive, a method capable of accurately estimating AADT on unmonitored segments is of great value to state DOTs. The ubiquitous probe vehicle data present a great opportunity to this end. This paper presents an enhanced method for statewide AADT estimation by leveraging such data in Kentucky. The use of the probe data is explored in two ways. First, an annual average daily probes (AADP) variable is derived from hourly probe counts; second, a betweenness centrality (BC) variable is calculated using probe speeds. Including both variables and using the random forest model results in model performance that exceeds those previously reported for statewide applications. Incorporating AADP and BC improves the accuracy of AADT estimates by 30%–37% for all roads and 23%–43% for highways in functional classes 5–7, compared with only using sociodemographic and roadway characteristics. These results demonstrate the value of the probe data for enhancing AADT estimation. The analysis further shows that on roadways having more than 53 AADP or an average of 2.2 probe counts per hour, the median and the mean absolute percent errors are below 20% and 25%, respectively. These findings have practical implications for state DOTs wanting to maximize the utility of probe vehicle data.

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

Two Problems of Averaging Arising in the Estimation of the Relationship Between Accidents and Traffic Flow

1998 ◽  
Vol 1635 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Abraham Mensah ◽  
Ezra Hauer
Keyword(s):  
Traffic Flow ◽  
Annual Average ◽  
Performance Function ◽  
Safety Performance Function ◽  
Time Period ◽  
Annual Average Daily Traffic ◽  
Averaging Problem ◽  
Long Time ◽  
The Relationship ◽  
The Ideal

A function linking the expected accident frequency to traffic flow is called a safety performance function (SPF). SPFs are estimated from data for various facilities and accident types. Typically, accident counts over a period of a year or more, and estimates of average flow for such periods, serve as data. The ideal is for SPFs to represent cause-effect regularities. However, because accident counts are for a long time period and because average flows are used, two issues of averaging arise. First, the cause-effect relationship is between accidents and the flows prevailing near the time of accident occurrence. Therefore, ideally, these should be the argument of the SPF. In practice, however, either because of lack of detail or difficulties of estimation, average flows are used for estimation. The question is what problems arise when average flows, such as annual average daily traffic, instead of the flows at the time of the accident are used as the argument of the SPF. This is the argument averaging problem. Second, there are at least two (daytime and nighttime) and perhaps many more cause-effect SPFs that prevail in the course of a year. Ideally, each relationship should be estimated separately. The question is what problems arise if one joint SPF is estimated when two or more separate functions should have been used. This is the function averaging problem. After analysis, how to account and how to correct for the argument averaging problem are shown. At this time, avoiding the function averaging problem by estimating daytime and nighttime SPFs separately can be the only recommendation.

scholarly journals Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets

2008 ◽  
Vol 2 (1) ◽  
pp. 1-4 ◽  
Author(s):  
B. Téllez ◽  
T. Cernocky ◽  
E. Terradellas
Keyword(s):  
Outlier Detection ◽  
Statistical Methods ◽  
Air Temperature ◽  
Reference Values ◽  
Total Precipitation ◽  
Annual Average ◽  
Initial Dataset ◽  
Geostatistical Techniques

Abstract. The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation. The resulting maps are then used in the automatic outlier detection in meteorological datasets.

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.

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