estimating annual average daily traffic (aadt) for local ...docs.trb.org/prp/13-3490.pdf · 3 for...

1

Estimating Annual Average Daily Traffic (AADT) 2

for Local Roads for Highway Safety Analysis 3

4

5

by 6

7

8

Tao Wang, Ph.D. 9 Research Associate 10

Lehman Center for Transportation Research 11

Florida International University 12

10555 West Flagler Street, EC 3680 13

Miami, FL 33174 14

Phone: (305) 510-3331 15

Email: [email protected] 16

17

Albert Gan, Ph.D.* 18 * Corresponding Author

19

Professor 20

Department of Civil and Environmental Engineering 21



Miami, FL 33174 24


26

Priyanka Alluri, Ph.D. 27 Research Associate 28

Lehman Center for Transportation Research 29



Miami, FL 33174 32


34

35

This paper contains 5,000 words, 7 figures, and 2 tables. 36

(7,250 equivalent words) 37

38

39

Submitted for: 40

Presentation and Publication 41

The 92nd Annual Meeting of the Transportation Research Board 42

43

44

November 15, 2012 45

46

TRB 2013 Annual Meeting Paper revised from original submittal.

mailto:[email protected]



Wang, Gan, and Alluri 1

ABSTRACT 1 2

Annual average daily traffic (AADT) is a required input to the newly released SafetyAnalyst 3

software application. Further, AADT is also required to calculate crash rates. Traditionally, 4

AADTs are estimated using a mix of permanent and temporary traffic counts collected in the 5

field. Because field collection of traffic counts is expensive, it is usually performed for only the 6

major roads. The mandate by the Federal Highway Administration (FHWA) to report the top 5% 7

of high crash locations on all public roads underscores, for the first time, the need for state 8

Departments of Transportation to acquire AADTs for also the non-state local roads. However, 9

many local jurisdictions either do not have any AADT data, or they do not have them in 10

sufficient quality. This paper presents a method to estimate AADTs for local roads using the 11

travel demand modeling method. A major component of the method involves a parcel-level trip 12

generation model that estimates the trips generated by each parcel. The generated trips are then 13

distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The 14

all-or-nothing trip assignment method is then applied to assign the trips between the parcels and 15

the traffic count sites onto local roadway network to yield estimates of AADTs. The estimated 16

AADTs were compared with those from an existing regression-based method using actual traffic 17

counts from Broward County, Florida. The results show that the proposed method produces 18

significantly lower mean absolute percentage errors. 19

20

Key words: Annual Average Daily Traffic, Tax Parcel Records, Trip Generation, Travel 21

Demand Modeling. 22

23 24



INTRODUCTION 1 2

Annual average daily traffic (AADT) is the average 24-hour traffic volume at a roadway location 3

over an entire year. AADT is required for many transportation analyses including economic 4

evaluation of highway safety projects, estimation of highway user revenues, computation of 5

highway statistics such as vehicle miles traveled (VMT), development of improvement and 6

maintenance programs, etc. 7

One well-known application of AADT is in the calculation of crash rates. As part of the 8

new Highway Safety Improvement Program (HSIP) of the Federal Highway Administration 9

(FHWA), states are required to submit an annual report describing no less than 5% of their 10

highway locations on all public roads that exhibit the most severe safety needs (1). To submit the 11

annual 5% reports (also known as Transparency Report), the Florida Department of 12

Transportation (FDOT) needs to have AADTs for all roads in Florida; however, FDOT estimated 13

AADTs for only its state roads but not the local roads. 14

In addition to the need for 5% reporting, AADT is also a required input to SafetyAnalyst 15

(2), a new safety analysis software released by the American Association of State Highway and 16

Transportation Officials (AASHTO). The software aims at providing state and local highway 17

agencies with a comprehensive set of tools to enhance their programming of site-specific 18

highway safety improvements. FDOT is interested in deploying SafetyAnalyst for all roads in 19

Florida. Because AADT is a required input to SafetyAnalyst, FDOT was, again, faced with the 20

problem of not having AADTs for local roads. 21

The most accurate method for obtaining the AADT of a roadway segment is to install an 22

Automatic Traffic Recorder (ATR) to count the total volumes continuously throughout the entire 23

year. However, because the installation and maintenance of permanent counters are expensive, 24

the number of permanent counters is typically limited. Therefore, it is not economically feasible 25

to apply this method of AADT estimation on a widespread basis. 26

An alternative approach to estimating AADT is to use portable counts, also called short-27

term, seasonal, or coverage counts. The collected short-term volumes on the interested roads are 28

then used to calculate Average Daily Traffic (ADT) which is then converted to AADT by 29

applying some adjustment factors. This factor approach is more economically feasible than the 30

permanent count method, but is still too costly to cover the local roads, which number over two 31

million segments in the state of Florida. 32

In 2007, FDOT contracted with the University of South Florida to develop regression 33

models to estimate AADTs for the local roads (3). The project attempted to improve upon a set 34

of regression models developed earlier by Xia et al. (4). However, a preliminary evaluation 35

based on data from the Miami-Dade and Broward counties, Florida showed that the estimation 36

errors of the models exceeded 100% and 200%, respectively. 37

This paper presents an improved method of estimating AADTs for local roads in Florida. 38

The method applies travel demand modeling techniques at the tax parcel level. In this method, 39

trips are generated based on parcel data using the trip generation rates from the trip generation 40

report of the Institute of Transportation Engineers (ITE) (5). A gravity trip distribution model is 41

then developed to distribute the parcel trips to the traffic counts sites on the major roads. All the 42

trips are finally assigned to local roads by applying all-or-nothing assignments based on free-43

flow travel times. 44

45



LITERATURE REVIEW 1 2

Several existing AADT estimation methods have been reported in the literature. Among the 3

methods the regression analysis has been the most widely used. A non-exhaustive list of states 4

that has developed regression models to estimate AADTs for their highway networks would 5

include Kentucky (6), Alabama (7), Minnesota (8), and Indiana (9). In Florida, regression models 6

were first developed to estimate AADTs for local roads in Broward County (4). The models were 7

modified and improved in multiple follow-up studies (10, 11, 12). A more recent attempt to 8

estimate AADTs for local roads in Florida was performed by Lu et al. (3). As aforementioned, 9

they attempted to improve the regression models developed by Xia et al. (4). However, the 10

estimation errors from their models were found to be quite high. 11

The availability of high-resolution satellite images and aerial photos had encouraged 12

some researchers to explore the potential of estimating AADTs with image-based data. McCord 13

et al. (13) first proposed an image-based method for AADT estimation using satellite images and 14

aerial photos. Later, a related method combining image-based and ground-based estimations was 15

implemented for Ohio road segments (14, 15). One problem with image-based methods is that it 16

is difficult to estimate traffic volumes on local roads as traffic on such facilities is usually sparse 17

and infrequent. 18

Another method attempted for AADT estimation was based on machine learning 19

algorithms. For example, a multi-layered, feed-forward, and back-propagation neural network 20

with supervised learning was developed to compare the Artificial Neural Network (ANN) 21

approach for AADT estimation to the traditional factor approach with 48-hour short-term count 22

data (16). The ANN approach was later applied by the same researchers to estimate AADTs on 23

low-volume rural roads (17, 18). Another effort that involved ANN models for AADT estimation 24

was conducted by Lam and Xu (19). A more recent study was performed by Li et al. (20) who 25

combined a K-Nearest Neighbor (K-NN) algorithm with Geographic Information System (GIS) 26

technology to aid in AADT estimation. In another recent study, a modified version of support 27

vector regression machine (SVR) named SVR-DP (SVR with Data-dependent Parameters) was 28

used to forecast AADT one year into the future based on historical AADTs (21). 29

Studies using the travel demand modeling approach for AADT estimation are relatively 30

rare. One known study that used this approach was conducted by Zhong and Hanson (22). The 31

study developed travel demand models to estimate AADTs on low-class roads for two regions in 32

the province of New Brunswick in Canada (22). While the study showed that the method has the 33

potential to improve AADT estimation, the results from the study were negatively impacted by 34

the available census geographic unit data, called dissemination area (DA), which was the 35

smallest census unit available in Canada. However, a DA by definition is a geographic unit 36

composed of one or more neighboring dissemination blocks with a population of 400 to 700 37

persons. Accordingly, data based on DAs are clearly too coarse for applications involving local 38

roads, which typically attract most trips generated from the abutting land uses. In this research, 39

this travel demand modeling approach is applied based on data at the parcel level. 40

41

METHODOLOGY 42 43

A parcel-level travel demand analysis model was implemented in this study. Similar to the 44

traditional travel demand forecasting model, the parcel-level model also involves a series of 45

mathematical models to simulate human travel behavior. The model consists of the following 46



four steps: network modeling, parcel-level trip generation, parcel-level trip distribution, and 1

parcel-level trip assignment. 2

Different from the traditional travel demand model, which attempts to simulate the 3

choices that the travelers may make during the entire trip from the origination to the destination, 4

the parcel-level model attempts to simulate choices that travelers may make in response to the 5

given local street system. The model does not include the mode choice step as the transit trips 6

and other modes are insignificant on local roads. The functionalities of each step involved in the 7

parcel-level travel demand analysis model are as follows: 8

Network Modeling defines the boundaries of the study area, prepares and preprocesses 9

the roadway network, parcel, and traffic counts data, and sets up the network 10

representation of the roadway linked with parcels and traffic count sites. 11

Parcel-level Trip Generation estimates the number of vehicle trips generated by each 12

parcel in the study area. The estimation is calculated based on the land use type of each 13

parcel and its corresponding ITE trip generation rate. 14

Parcel-level Trip Distribution determines where the trips generated by each parcel will 15

go. It determines the number of trips between a parcel and a traffic count site based on 16

traffic count data (or AADT estimated from the count data) and the shortest travel time 17

between them. 18

Parcel-level Trip Assignment predicts the routes the travelers will take to reach the traffic 19

count sites on major roads, resulting in the estimated AADTs of local roads in the study 20

area. 21

In the network modeling step, the study area boundary, commonly called the cordon line, 22

is defined. Unabridged roadway network data, detailed parcel data, and traffic count sites data 23

were used in the analysis. Similar to the traditional travel demand model, centroids and centroid 24

connectors were created. Each parcel was assigned a centroid and a centroid connector to 25

represent access to a neighboring road segment. An example of a subarea with the added centroid 26

connectors connecting the parcels and the closest roads is shown in Figure 1. The green polygons 27

represent the parcel boundaries; the blue lines represent the roadway; and the gray lines are the 28

added centroid connectors. 29

Parcel-level trip generation is the process to estimate and quantify the number of trips 30

each parcel will generate. In this application, this step was implemented using both the 31

Department of Revenue (DOR) parcel data and the trip generation rates and regression equations 32

provided by ITE Trip Generation Report (5). 33

The DOR parcel data has 100 land use types for the parcels, and the ITE Trip Generation 34

Report provides trip generation rates and/or equations for 10 main land use categories and 162 35

sub-categories. The two types of land use categories were matched to estimate the parcel trips. In 36

the case that a parcel land use type encompasses multiple ITE land use categories, the results 37

were averaged and weighted by an estimate of the relative presence of each ITE land use 38

category in the study area. The calculation can be expressed as follows: 39

40

ii Pn

i

XFT

)

1

( (1) 41

where, 42

T = number of vehicle trips generated by a parcel; 43



Fi = ITE trip generation function (either regression equation or average trip rate) for 1

ITE land use type i; 2

X = independent variable such as dwelling units, gross floor area, etc.; 3

n = number of ITE land use types; and 4

Pi = proportion of ITE land use type i in the study area. 5

6

7 8

FIGURE 1 Example of study area with parcels (green lines), centroid connectors (gray 9

lines), and existing roads (blue lines). 10 11

For most of the land use types, the ITE trip generation rates/equations are based 12

on dwelling units or areas, which are also the attributes of a parcel in the parcel database. Hence, 13

for a majority of parcels, the trip generation can be calculated directly by using the parcel data. 14

However, for land use types of which the corresponding independent variable used by ITE rates 15



is a size attribute that is different from the parcel data, they are adjusted by the ratio between the 1

ITE and parcel based mean values using the following equation: 2

3

)( RXFT (2) 4

5

where, 6

T = number of vehicle trips generated by a parcel; 7

F = the ITE trip generation function (either regression equation or average trip rate, 8

which can be regarded as a special linear regression); 9

X = independent variable such as dwelling units, gross floor area, etc.; and 10

R = the ratio of mean values between the ITE independent variable and the parcel 11

size attribute. 12

13

Due to the lack of other demographic and land use data for a few uncommon land use 14

types, the proportion of ITE land use type Pi in Equation (1) and the ratio of mean values 15

between the ITE independent variable and the parcel size attribute R in Equation (2) were 16

determined by human judgment. 17

Due to the different travel patterns among the weekday, Saturday, and Sunday, the 18

number of trips for weekdays and weekend were averaged using the following equation: 19

20

7

5 SundaySaturdayweekday

average

TTTT

(3) 21

where, 22

Taverage = the final estimated number of daily trips generated by a parcel, 23

Tweekday = average weekday trips generated by a parcel, 24

TSaturday = Saturday trips generated by a parcel, and 25

TSunday = Sunday trips generated by a parcel. 26

27

Similar to the traditional travel demand model, parcel-level trip distribution is also 28

derived from Newton's law of gravity. However, the trips are distributed between the parcels and 29

the traffic count sites instead of among the zones. To distribute trips, all the parcels have only 30

productions and no attractions, and all the traffic count sites have only attractions and no 31

productions. Productions of a parcel are the trips generated in the parcel-level trip generation 32

step, and the attractions of a traffic count site are either the traffic count data or AADT estimated 33

from the traffic count data. In addition, while traditional travel demand model usually includes 34

the effects of multiple travel impedance factors, such as travel time, cost, etc., parcel-level trip 35

distribution considers only the shortest free-flow travel time. This is expedient because travel 36

time is the major factor that determines trips on local roads, and travelers will most likely choose 37

the fastest path to access major roads to reach their destinations. The parcel-level trip distribution 38

can be expressed as follows: 39

40

i

ikk

ijj

ij Tn

k

DA

DAT

1

)/(

/ (4) 41



where, 1

Tij = daily vehicle trips between parcel i and traffic count site j, 2

Ti = total daily vehicle trips generated by parcel i, 3

Aj = AADT estimated from traffic count volume at traffic count site j, 4

Dij = the shortest free flow travel time between parcel i and traffic count site j, and 5

n = number of traffic count sites. 6

7

It should be noted that, since the parcel-level trip distribution step distributes the trips 8

from the parcels to the nearby traffic count sites, enough traffic count data are required to evenly 9

cover the entire study area. Uneven coverage of traffic count sites may cause inaccurate 10

distribution of trips. 11

For traffic assignment, the traditional travel demand model commonly assigns trips using 12

an all-or-nothing with capacity restraint method, also known as the equilibrium assignment 13

method. In this application, only the simple all-or-nothing assignment method is needed because 14

congestion seldom happens on local roads. In other words, for the same reason that affects the 15

trip distribution step, the travelers’ route selection is based mainly on the free-flow travel time to 16

the nearby major roads. The simple all-or-nothing assignment also improved the efficiency of the 17

model as it involves only one iteration. 18

After the trips for all of the parcels were assigned to the local roads, the AADT is 19

estimated as the sum of trips in both the directions for a roadway segment. Finally, data post-20

processing was performed to calculate the final AADT values for all local roads. 21

22

MODEL DEVELOPMENT 23 24

Two development tools were applied to implement this model: ArcGIS 10.0 from ESRI and 25

Cube 5.1.3 from Citilabs. An application of ArcGIS called ModelBuilder was used to perform 26

the data preprocessing and post-processing. Scripts were then developed in Cube Voyager to call 27

and customize the library programs to implement the model. Figure 2 shows the system 28

components and procedure used to estimate AADT. The procedure includes the following sub-29

steps: 30

ArcGIS is used to preprocess the input data for the model including the DOR parcel data, 31

unabridged highway network data, and traffic count sites data. 32

The preprocessed input data are imported into Cube, and the highway network is built 33

from the unabridged roadway shape file. 34

The built highway network is used by the network modeling step to calculate the free 35

flow travel time skim matrix. 36

The parcel-level trip generation step is performed by using the merged DOR parcel data 37

and traffic count site data as well as the trip generation rates and regression equations 38

provided by the ITE Trip Generation Report. 39

A parcel-level trip distribution gravity model is used to distribute the generated trips 40

between the parcels and the nearby traffic count sites. 41

The distributed trips are assigned to the network using all-or-nothing assignment method 42

in the parcel-level trip assignment step. 43

The traffic volume data of the loaded network are exported, and ArcGIS is used again to 44

calculate the final AADTs, which are then joined to the original roadway network to 45

obtain the roadway network with AADTs. 46



1

2 FIGURE 2 System components and procedure. 3

4

ArcGIS ModelBuilder, which provides a visual programming environment allowing users 5

to graphically link geoprocessing tools into models, was used to implement the ArcGIS 6

component. While the models built with ModelBuilder can be executed directly in ArcGIS, they 7

can also be exported to scripting language such as Python. The Python scripts can be called with 8

the Cube Voyager Pilot program, so theoretically all the steps of the ArcGIS part can be 9

integrated into Cube to simplify the execution of the entire model. However, because this part of 10

the procedure requires some geo-processing tools that are supported only by ArcGIS 10.0, which 11

is not compatible with the current version of Cube (5.1.3), integration of ArcGIS into Cube could 12

not be realized. Nevertheless, this incompatibility would not affect the results of the entire 13

model. 14

Figure 3 shows the model steps and the input and output files for each steps implemented 15

in Cube. When the model is run, the four steps are executed in sequence, and the output files of a 16

preceding step become the input files of a later step. It is noted that if there were no compatibility 17

problems as mentioned above, the ArcGIS part of the procedure could have been combined with 18

Cube, and the steps shown in Figure 3 would be all the steps involved in the entire model. 19

Table 1 summarizes the input and output files for each step. There are two input files for 20

Cube: the network file preprocessed by ArcGIS, and the DBF file for the merged parcels and 21

traffic count sites shape file which is also generated by ArcGIS. There is one output file 22

generated by the Cube part, i.e., the DBF link file with the traffic volume information exported 23

from the loaded network assigned in the parcel-level trip assignment step. Among the steps, the 24

output files of a preceding step become the input files of a later step. 25



1 FIGURE 3 Model steps in Cube. 2

3

TABLE 1 Input and Output Files 4

Model Step Input File Output File

Network Modeling Preprocessed Network File Free Flow Time Skim Matrix File

Modified Network File

Parcel-level Trip

Generation Merged Parcels and Counts DBF File Vehicle Trips DBF File

Parcel-level Trip

Distribution Free Flow Time Skim Matrix File

Distributed Trips Matrix File Vehicle Trips DBF File

Parcel-level Trip

Assignment Distributed Trips Matrix File Link DBF File with Volume Exported

from Loaded Network Modified Network File

5

MODEL EVALUATION 6 7

Because the actual AADT values for local roads were not available, the AADTs estimated from 8

short-term traffic count data had to be used as the ground truths for model evaluation in this 9

study. To quantify the estimation error, the mean absolute percentage error (MAPE), calculated 10

as follows, was used: 11 12

n

i iG

iGiF

nMAPE

1 )(

)()(1

(5)

13

14 where, 15

G(i) = ground truth AADT at location i, 16

F(i) = estimated AADT at location i, and 17

n = total number of locations. 18



As aforementioned, regression modeling has been the most widely used AADT 1

estimation method. Based on the method’s popularity and also the availability of the 2

experimental results, the regression method proposed by Lu et al. (3) was compared with the 3

proposed parcel-level travel demand model. Lu et al. developed multiple regression models that 4

estimate AADTs as functions of socio-economic and roadway characteristics. 5

Broward County in Florida was selected as the study area for evaluation. The County 6

includes over 4,000,000 parcels. However, Cube 5.1.3 can only process a maximum of 32,000 7

zones (or parcels in this application) at a time. A total of 10 subareas with a total of 78 evaluating 8

count sites were selected to cover diverse areas in this evaluation. An example map of the 9

subareas is shown in Figure 4. Two types of count sites are shown in this map. One is the 10

estimation count sites on the major roads (indicated by circular dots in the map) which are used 11

to estimate the AADTs, and the other is evaluation count sites on the minor roads (indicated by 12

the triangles in the map) which are used to evaluate the estimation results. 13

14

15

FIGURE 4 An example of the subareas. 16

Figures 5 and 6 compare the overall performance of the regression method and the 17

proposed method, respectively. As shown in the figures, the maximum AADT is lower than 18

30,000 vehicles/day and within a reasonable range, since all the testing locations were on local 19

roads. From Figure 5, it can be seen that the regression models tend to overestimate AADT. The 20

plotted dots in Figure 6 show that the results of the proposed method are distributed more evenly 21

on both sides of the 45-degree sloping line than in Figure 5. 22



1 2

FIGURE 5 Comparison of estimated AADTs from regression models and ground truth 3

AADTs. 4

5

6

7 8

FIGURE 6 Comparison of estimated AADTs from parcel-level demand model with ground 9

truth AADTs. 10 11



Table 2 quantifies the accuracy of the two methods using the MAPE measure. It can be 1

seen that the proposed method has consistently lower estimation error than the regression models 2

for all study areas. The overall estimation errors for all the 78 evaluating count sites in the 10 3

study areas were also calculated. The results show that the proposed model generated 52% 4

MAPE, which is 159% lower than the corresponding value of 211% from the regression models. 5

6

TABLE 2 Errors Comparison of Regression and Demand Models 7

Subarea # MAPE from Regression Models (%) MAPE from Demand Model (%)

1 217 48 2 314 52 3 177 48 4 345 66 5 1756 49 6 405 60 7 114 62 8 186 49 9 181 56

10 157 39 All Subareas 211 52

8

Depending on the availability of traffic count data, most of the traffic count sites used for 9

this evaluation are located on local roads that are directly connected to the state roads. The 10

lower-level local roads such as the community roads were not used in this evaluation because of 11

the lack of traffic count data. However, the proposed method is expected to perform better even 12

for lower-level local roads as its trip generation is based on detailed parcel level data. To verify 13

this assumption, the AADTs estimated using the two methods for the available lower-level local 14

roads were checked and compared. Figure 7 shows the estimation results for the roads in a 15

community of approximately 160 houses. In this figure, the AADTs estimated from the proposed 16

method and the regression method are displayed in red and green, respectively. It can be seen 17

that the AADTs estimated by the regression method were unreasonably high. One reason that the 18

regression method significantly overestimated the AADTs in this case was due to their inability 19

to recognize that the layout of the local roads was designed such that through traffic, if any, was 20

minimal. 21

22

CONCLUSIONS 23 24

Annual average daily traffic (AADT) data are needed to identify high crash locations on all 25

public roads for 5% reporting as mandated by FHWA. They are also required for input to the 26

newly released SafetyAnalyst software application. The lack of AADT data, especially for the 27

vast local road networks, prevents such application from being deployed to improve safety on 28

local roads. This paper described a parcel-level travel demand model that was developed to 29

estimate AADTs for local roads. Compared to results from a set of existing regression models, 30

the parcel-level demand model improved the accuracy of AADT estimation for local roads by 31

159% based on the MAPE measure. A major component of the proposed model involves a 32

parcel-level trip generation model that estimates the trips generated by each parcel. The model 33

makes use of the tax parcel data together with trip generation rates from the Institute of 34

Transportation Engineers (ITE) Trip Generation Report. One advantage of using tax parcel data 35



is that the data is updated at least annually, making it possible to update the AADTs in response 1

to lane use changes. One disadvantage of the proposed model is the large number of parcels that 2

have to be preprocessed to improve the model’s efficiency. For applications involving large 3

areas, the study area has to be divided into multiple subareas and run the model separately. In 4

addition, the model also requires that there not only be sufficient traffic count data to cover the 5

entire study area, but they should be relatively evenly spaced, as uneven coverage of traffic count 6

sites may result in inaccurate distribution of trips. 7

8

9 10

FIGURE 7 Example of AADT estimation for local roads in a community. 11 (Note: AADTs estimated by the proposed method and the regression method are shown in red and green, respectively.) 12

13

ACKNOWLEDGEMENTS 14 15

This research was funded by the Research Center of the Florida Department of Transportation 16

(FDOT). The authors are thankful to the Project Manager, Mr. Joseph Santos, of the FDOT 17

Safety Office for his guidance and support. 18

19



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http://safety.fhwa.dot.gov/hsip/fivepercent/

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http://144.171.11.39/view/798908


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