Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

2015 ◽  
Vol 38 (2) ◽  
pp. 181-199 ◽  
Author(s):  
Ioannis Tsapakis ◽  
William H. Schneider IV
Keyword(s):  
Optimal Number ◽  
Seasonal Adjustment ◽  
Adjustment Factor ◽  
Annual Average ◽  
Annual Average Daily Traffic

A Comparison of Short Duration Traffic Counts Methodologies for Estimating Annual Average Daily Traffic

2014 ◽  
Vol 607 ◽  
pp. 657-663
Author(s):  
Jung Ah Ha
Keyword(s):  
Short Duration ◽  
Estimation Error ◽  
Traffic Volume ◽  
Adjustment Factor ◽  
Annual Average ◽  
Traffic Counts ◽  
Transportation Sector ◽  
Annual Average Daily Traffic ◽  
Transportation Plan ◽  
Aadt Estimation

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Future level of service is forecasted, based on design traffic volume. AADT is used as design traffic which is the basic traffic volume in transportation plan. But AADT is estimated using short duration traffic counts at most sites because permanent traffic counts are installed at limited sites. A various of methodologis about short duration traffic counts are used to estimate AADT. This study compared with typical short duration traffic counts methodologies in USA and Korea. Short duration traffic counts in USA typically are defined as stations where 24-hour, 48-hour of data is collected. In Korea, short duration traffic counts are collected at one day (24-hour) or two days (not two consecutive days). So this study compared among each short duration traffic counts methodology: one day (24-hour), two consecutive days (48-hour), not two consecutive days (twice per year). Short duration traffic counts surveyed twice per year is the best method to reduce AADT estimation error among analyzed methodologies. The analysis found that in case adjustment factor is applied to estimate AADT, AADT estimation error is further lowered.

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.

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.

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.

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