Annual Average Daily Traffic Estimation Research Articles

New Efficient Regression Method for Local AADT Estimation via SCAD Variable Selection

This paper focuses on the estimation and variable selection for the local annual average daily traffic (AADT). The variable selection procedure by smoothly clipped absolute deviation penalty is proposed. It can simultaneously select significant variables and estimate unknown regression coefficients in one step. The estimation algorithm and the tuning parameters selection are presented. The data from Mecklenburg County, North Carolina, USA, in 2007 are used for demonstration with our proposed variable selection procedures. The results show that this penalized regression technology improves the local AADT estimation along with satellite information, and it outperforms some other benchmark models.

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

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.

Comparison of Clustering Methods for Road Group Identification in FHWA Traffic Monitoring Approach: Effects on AADT Estimates

Defining road groups is the first step in the Federal Highway Administration (FHWA) factor approach procedure for annual average daily traffic (AADT) estimation and is one of the main sources of errors in AADT estimates. This paper focuses on a comparative analysis of cluster analysis methods to identify road groups with similar traffic patterns according to different combinations of seasonal adjustment factors calculated for passenger vehicles and trucks. The aim is to highlight the differences among methods and input variables in the AADT estimation process, optimizing information commonly available to analysts. The analysis made use of traffic data from 50 automatic traffic recorder (ATR) sites in the Province of Venice, Italy. The estimation accuracy of the clustering methods was assessed and compared by considering the values of mean absolute percent error in AADT estimates. The performance of clustering methods was found to differ, depending on data sets and traffic patterns. Particularly significant for the accuracy of AADT estimates was the choice to use seasonal adjustment factors disaggregated by vehicle type as input variables.

Methodology to Characterize Ideal Short-Term Counting Conditions and Improve AADT Estimation Accuracy Using a Regression-Based Correcting Function

Transportation agencies’ motor vehicle count programs tend to be well established and robust with clear guidelines to collect short-term count data, to analyze data, develop annual average daily traffic (AADT) adjustment factors, and to estimate AADT volumes. In contrast, bicycle and pedestrian traffic monitoring is an area of work for most transportation agencies. In most agencies, there are a low numbers of counting sites and limited agency experience to manage a city-wide or state-wide system of collecting, processing, and using nonmotorized data. Short duration counts are used to estimate longer duration volumes such as AADT. Because bicycle or pedestrian short-term counts vary dramatically over time and significantly more than motorized vehicle counts, the direct application of motorized vehicle AADT estimation methods may be inadequate. The goal of this paper is to present a methodology that will enhance, if needed, existing AADT estimation methods widely employed for motorized vehicle counts. The proposed methodology is based on the analysis of AADT estimation errors using regression models to estimate a correcting function that accounts for weather and activity factors. The methodology can be applied to any type of traffic with high volume variability but in this research is applied to a permanent bicycle counting station in Portland, Oregon. The results indicate that the proposed methodology is simple and useful for finding ideal short-term counting conditions and improving AADT estimation accuracy.

Application of Seasonal Adjustment Factors to Subsequent Year Data

Seasonal adjustment factors are used by state highway agencies to convert short-term traffic counts into estimates of annual average daily traffic (AADT). These factors are typically calculated from continuous traffic counter data. The protocol for the process is described in FHWA's Traffic Monitoring Guide. This protocol specifies that temporary counts collected during a year should be adjusted by seasonal factors computed from continuous data collected during the same calendar year. The authors had suggested that, if the prior year seasonal factors could be used, AADT estimates would be available soon after temporary counts were collected. This study investigated whether the seasonal factors from sequential years could be applied without loss of statistical accuracy. No significant loss of accuracy was found in estimating AADT when prior-year seasonal adjustment factors were applied to current-year short-term counts of traffic volume. Likewise, no significant loss of accuracy was found when vehicle miles of travel were computed by using prior-year seasonal adjustment factors.

Day-of-Year Scaling Factors and Design Considerations for Nonmotorized Traffic Monitoring Programs

General procedures, including the estimation of annual average daily traffic (AADT) from short-duration counts, have not been established for nonmotorized traffic monitoring programs. Continuous counts of nonmotorized traffic were collected at six locations on the off-street trail network in Minneapolis, Minnesota, in 2011. A new approach for estimating AADT values from short-duration counts, the use of day-of-year factors, is demonstrated. Analyses of variability in count data can be used to design a monitoring program that uses both continuous and short-duration counts of nonmotorized traffic. Five core conclusions may be useful for developing nonmotorized monitoring programs: ( a) day-of-year scaling factors have smaller error than does the standard (day-of-week and month-of-year) method of AADT estimation, especially from short-duration counts (<1 week); ( b) extrapolation error decreases with short-duration-count length, with only marginal gains in accuracy for counts longer than 1 week; ( c) errors in estimating AADT values are lowest when short-duration counts are taken in summer (or spring, summer, and fall) months (April through October); ( d) the impact of sampling on consecutive (successive) versus nonconsecutive (separate) days on AADT estimation is minimal but may reduce labor requirements; and ( e) the design of a traffic monitoring program depends on the acceptable error, equipment availability, and monitoring period duration. Trade-offs in short-duration-count lengths and estimate accuracy will depend on resource constraints. Analysts can use day-of-year factors to improve the accuracy of AADT estimation. Analyses of variability in traffic counts can strengthen the design of monitoring programs.

Annual Average Daily Traffic Estimation from Seasonal Traffic Counts

This paper presents an approach to estimation of the Annual Average Daily Traffic (AADT) from a one-week seasonal traffic count (STC) of a road section, with the aim of improving the interpretability of results with measures of non-specificity and discord. The proposed method uses fuzzy set theory to represent the fuzzy boundaries of road groups and measures of uncertainty. Neural networks are used to assign a road segment to one or more predefined road groups. The approach was tested with data obtained in the Province of Venice, Italy, for the period of the year in which STCs are taken. The method was found to produce accurate results.

Annual Average Daily Traffic Estimation using Co-kriging

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it`s difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year`s traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

Annual Average Daily Traffic Estimation using Co-kriging

Annual Average Daily Traffic Estimation using Co-kriging

Use of Reduction-Effectiveness Ratios to Evaluate Reduced Traffic Data Collection Plans

Because of budget shortfalls in recent years, state departments of transportation (DOTs) must adjust their traffic data collection plans by reducing data collection locations or extending data collection cycles; however, few studies have been performed to evaluate the cost-effectiveness of various efforts to reduce data collection. This study developed a quantitative method for evaluating the impact of various reduced plans for traffic data collection on the overall accuracy of the annual average daily traffic (AADT) estimation. The mean absolute percentage error is calculated to compare the accuracy of 10 reduced data collection plans with a base plan. In addition, a reduction-effectiveness ratio (i.e., the percentage of reduced data collection cost to the percentage of increased AADT estimation error) is proposed. Results from this study show that the current practice, which randomly selects data collection sites on the basis of different cycles, performs well in maintaining AADT estimation accuracy but may not be the most cost-effective approach. Results also show that certain types of sites, such as rural sites, lower-AADT sites, and sites with higher AADT variation, tend to produce larger errors if they are not counted. The results imply that the proposed method both provides a quantitative means with which to evaluate plans for reduced data collection and suggests ways to enhance current data collection and traffic estimation practices. The method also enriches the information provided to state departments of transportation for effective and informed decision making with limited resources.

Improving Group Assignment and AADT Estimation Accuracy of Short-term Traffic Counts using Historical Seasonal Patterns & Bayesian Statistics

Annual Average Daily Traffic (AADT) is one of the most important pieces of data being used widely in planning, design, operation and management of roads and facilities. A reliable estimate of AADT has always been one of the main interests of transportation and highway agencies. Traditionally, AADTs are estimated for the majority of road segments of a network from short-term traffic counts (STTCs or coverage counts) by applying a set of expansion factors derived from permanent traffic counts (PTCs). Literature indicates that the FHWA (Federal Highway Administration) method may be most widely used. In this method, roads in the same functional class are assumed to have similar traffic patterns, and the factors derived from PTC data collected from the class is used to convert STTCs to AADT estimates. However, it should be noted that, because roads from a functional class do not necessarily have similar seasonal traffic variations, this method may sometimes produce large estimation errors. In this regard, this paper proposes a novel pattern-matching method, which constructs a seasonal traffic variation profile for a short-term counting site using all historical counts available and then use this profile to assign the site to a PTC or a PTC group. In addition, a Bayesian approach is developed to explicitly consider and show the “risk or uncertainty” associated with assigning each short-term counting site to different PTC or PTC groups. Study results based on the simulated STTCs from a permanent counter on a winter recreational road in Alberta, Canada show that the new method proposed can limit the 95th percentile (P95) AADT estimation error to less than 13%, in contrast to 21.7% from the FHWA method. Moreover, it should be noted that the proposed method will not impose any additional monitoring cost, or make any change to existing traffic monitoring programs, and therefore it will be easy for highway agencies to incorporate the proposed method in their practice.

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

This paper proposes an approach that estimates annual average daily traffic (AADT) of a road section from incomplete data. This approach improves the accuracy and interpretability of the results while preserving the concept of the current FHWA procedure. When the short-period traffic count (SPTC) of a road section is given, a road group with a known AADT and a similar traffic pattern is identified. The AADT of the road section in question is estimated by adjusting the AADT associated with the road group by the degree of similarity. The uncertainty associated with the similarity is measured by nonspecificity and discord. The model was tested with data obtained in the Province of Venice, Italy. The analysis considered the characteristics of SPTC, including duration and day of the week. Estimates obtained with the proposed method and two existing methods were compared. The proposed method was found to produce more accurate results than the previous methods. Weekday 48-h short counts were found to be the best sample SPTC for AADT estimation. Furthermore, the measures of uncertainty helped to interpret the quality of the estimates. SPTCs with a lower value of discord were found to yield better AADT estimates, and a high value of nonspecificity indicated uncertainty with respect to matching with the groups. These measures can also indicate the need for additional data collection.

Modeling and Algorithms of VMT and AADT Estimation for Community Area Traffic Networks

AbstractThis paper presents new modeling methods and their algorithms for the VMT (Vehicle Miles Traveled) and AADT (Annual Average Daily Traffic) estimation of community traffic networks which are lack of monitoring systems. It is found that the total traffic amount at the entrances of a community has strong relationship with its household number. Three models are proposed to estimate and predict AADT and VMT in the community based on its household number, the road network topology, and drivers’ common behavior in statistical sense. The automatic algorithm and software are developed for the new models. The models and methods are verified by the field sample measurement data.

Data Mining Methods for Traffic Monitoring Data Analysis: A case study

Presented in this paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads, aimed at the estimation of Annual Average Daily Traffic (AADT). The analysis was carried out using data available from fifty-four Automatic Traffic Recorder (ATR) sites in the Province of Venice (Italy) and separated adjustment factors for passenger and truck vehicles in the grouping process. Errors in AADT estimation from 24-h sample counts indicate that model-based clustering methods give slightly better results compared to other tested methods, identifying a significant ATRs classification.

Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings

The assignment of short-term counts to groupings of seasonal adjustment factors is the most critical step in the annual average daily traffic estimation process; this step is also extremely sensitive to error resulting from engineering judgment. In this study, discriminant analysis is examined, and several variable selection criteria are investigated to develop 12 assignment models. Continuous traffic volume data, obtained in the state of Ohio during 2005 and 2006, are used in the analysis. Seasonal adjustment factors are calculated with individual volumes of the two directions of travel as well as the total volume of a roadway segment. The results reveal that the best-performing directional volume–based model, which employs the Rao's V algorithm, produces a mean absolute error (MAE) of 4.2%, which can be compared with errors reported in previous studies. An average decline in the MAE by 58% and in the standard deviation of the absolute error by 70% is estimated over the traditional roadway functional classification. In addition, time-of-day factors are slightly more effective in identifying similar patterns of short-term counts than when they are combined with the average daily traffic. When directional-specific factors are used instead of total volume–based seasonal adjustment factors, the improvement in the average MAE is approximately 41%. This conclusion is consistent with previous research findings and may result from the division of the data set by direction essentially doubling the sample size, which in turn increases the number of assignment options for a short-term count.

Estimation for Seasonal Factors of Similarity-Based Traffic for Urban Roads in Florida

The Florida Department of Transportation (FDOT) estimates seasonal factors (SFs) by using data obtained from approximately 300 telemetry traffic monitoring sites, which are used in the calculation of annual average daily traffic (AADT) at portable traffic monitoring sites (PTMSs). Both the estimation and the application of seasonal factors are based on the consideration of similarities in the traffic characteristics of roads and on engineering judgment. FDOT desires a more objective and data-driven method to improve the accuracy of SF estimation for PTMSs. A previous study investigated traffic and land use data in Southeast and North Florida and demonstrated the possibility of identifying the link between land use variables and seasonal factors. This paper describes a statewide investigation to identify potentially influential variables that contribute to seasonal fluctuations in traffic volumes in urban areas in Florida. A method is also developed for assigning seasonal factors to PTMSs. The test results show that the errors of the estimated seasonal factors are on average 5%. This method is promising for application in both urban and rural areas to improve accuracy in AADT estimation.

Forecasting Network Data

Annual average daily traffic (AADT) values have long played an important role in transportation design, operations, planning, and policy making. However, AADT values are almost always rough estimates that are based on the closest short-period traffic counts and are factored by using permanent automatic traffic recorder data. This study develops Kriging-based methods for mining network and count data over time and space. With the use of Texas highway count data, the method forecasts AADT values at locations where no traffic detectors are present. While low-volume road counts remain difficult to predict, available explanatory variables are few, and extremely high-count outlier sites skew predictions in the data set used here, overall AADT-weighted median prediction error is 31% (across all Texas network sites). Here, Kriging performed far better than other options for spatial extrapolation, such as assigning AADT on the basis of a point's nearest sampling site, which yields errors of 80%. Beyond AADT estimation, Kriging is a promising way to explore spatial relationships across a wide variety of data sets, including, for example, pavement conditions, traffic speeds, population densities, land values, household incomes, and trip generation rates. Further refinements, including spatial autocorrelation functions based on network (rather than Euclidean) distances and inclusion of far more explanatory variables are possible, and will further enhance estimation.

Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.

Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree

Seasonal factor is an important parameter for converting coverage counts to annual average daily traffic (AADT). There have been many studies on establishing seasonal factor (SF) categories, but there is a limited understanding of how to assign SF groups to short-term count sites. In the current practice, established factor groups are typically assigned based on function class, the physical proximity of short count sites to a permanent traffic count site, and other engineering judgment. There is no sound theoretical basis or rules to guide the seasonal factor assignment process, which results in inaccurate AADT estimates for coverage count sites. This paper describes a data-driven procedure for assigning a seasonal factor category to a given portable count site by taking into consideration the similarities between the characteristics of permanent count sites in the seasonal factor group and the portable count site. A fuzzy decision tree was constructed based on the known SF groupings of permanent count sites and their land use attributes. The decision tree was then applied to determine the SF category for a given portable count site in Broward, Miami-Dade, and Palm Beach counties in Florida.

Improved AADT Estimation by Combining Information in Image- and Ground-Based Traffic Data

On most segments in a state highway system, annual average daily traffic (AADT) in the current year is estimated by applying an applicable growth factor to coverage count data obtained in an earlier year. The accuracy of AADT estimates could conceivably be improved by exploiting existing imagery of highway segments containing vehicles. When both earlier year coverage counts and a current year image containing traffic information are available, a weighted combination of both pieces of information is proposed for AADT estimation. The weights can be easily estimated from available information and data regularly collected by state departments of transportation. An empirical study was conducted using 122 highway segments to evaluate the improved accuracy in estimating AADT that would have resulted from using this weighted method with a single contemporary image for a 10-year period from 1994 and 2003. The accuracy was markedly improved and stable over a large range of important input values. The demonstrated improvements in accuracy and the ease of using this method with existing data are great enough that field testing should now be considered.