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 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.

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.

scholarly journals Evaluation of the Efficiency of Neural Networks and Statistical Models to Determine Daily Traffic Volume of the Suburban Roads of Mazandaran Province

2015 ◽  
Vol 10 (Special-Issue1) ◽  
pp. 215-222
Author(s):  
Shariar Zargar ◽  
Sepideh Aldini ◽  
Seyed Hoseini
Keyword(s):  
Statistical Models ◽  
Regression Models ◽  
Traffic Volume ◽  
Road Maintenance ◽  
Mazandaran Province ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
The Future ◽  
Transportation Organization ◽  
Future Status

Realizing the traffic volume at the present time is frequently one of the concerns that occupies the planners’ minds in transportation. Knowing the current volume plays an important role in reflecting the performance of transportation system in the future. Traffic studies are based on observations and interpretations of the current circumstances .Since the present observations cannot be represented for the future status, it should be predicted by means of determined conditions. Annual Average Daily Traffic is one the measure to be used for the traffic volume, which has been mentioned in the codes. The fixed or non-fixed automated counters serve to count this volume. In Iran, Road Maintenance & Transportation Organization is responsible to count daily through different ways. In the present study, the data collected from the selected axes of Mazandaran Province was utilized to make a predictive model for traffic volume. It is fitted by data, linear and logarithmic regression models and also neural network model.

Conducting Environmental Assessments for Physical Activity: Determining Traffic Volume in Walkability Audits in Two West Virginia Communities

2008 ◽  
Vol 5 (6) ◽  
pp. 909-917 ◽  
Author(s):  
Karen K. Lee ◽  
Candace D. Rutt ◽  
Andrea Sharma ◽  
Michael Pratt ◽  
Judd Flesch ◽  
...  
Keyword(s):  
West Virginia ◽  
Weighted Kappa ◽  
Traffic Volume ◽  
Department Of Transportation ◽  
Annual Average ◽  
Traffic Counts ◽  
High Agreement ◽  
Suburban Areas ◽  
Annual Average Daily Traffic ◽  
Road Segments

Background:In this article, we examine the possibility of reducing time to conduct traffic volume audits through (1) reducing time for manual traffic counting and (2) using Department of Transportation (DOT) information.Methods:In audits of 824 road segments in 2 West Virginia (WV) communities, manual traffic counts were recorded for 1, 2, and 5 minutes in duration. Annual Average Daily Traffic (AADT) was calculated from counts. Available AADT from DOT was also collected. Percent agreement and a weighted kappa were calculated between 5-minute count and 1- and 2-minute count AADT categories and between 5-minute count and DOT AADT categories.Results:One- and 2-minute counts produced identical AADT categories as 5-minute counts in 93.4% and 95.0% of segments, respectively. Weighted kappa was 0.79 (95% CI = 0.74–0.85) and 0.85 (95% CI = 0.80–0.89), respectively. Forty-two segments (5.1%) had DOT data.Conclusions:DOT AADT was available for a small percentage of road segments assessed. The high agreement between AADT categories produced by 1- and 2-minute counts and 5-minute counts makes it reasonable to consider using 1- or 2-minute manual traffic counts if time or staffing constraints make it necessary. Possible generalizability of this methodology to other communities, particularly larger urban and suburban areas, will require further research.

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 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.

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.

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