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An Assessment of the Highway Inventory Data Collection Method Using Photo/Video Logging

Mohammad Jalayer* Ph.D. Graduate Student, Dept. of Civil Engineering

Auburn University, Auburn, AL 36849-5337 Phone: +1-312-351-4730

[email protected]

Shunfu Hu Professor, Dept. of Geography

Southern Illinois University Edwardsville, Edwardsville, IL 62026-1800 Phone: +1-618-650-2281

[email protected]

Huaguo Zhou Associate Professor, Dept. of Civil Engineering

Auburn University, Auburn, AL 36849-5337 Phone: +1-334-844-1239, Fax: +1-334-844-6290

[email protected]

Rod E. Turochy Associate Professor, Dept. of Civil Engineering

Auburn University, Auburn, AL 36849-5337 Phone: +1-334-844-6271, Fax: +1-334-844-6290

[email protected]

*Corresponding Author

Word Count = 3,690 (Text) + 250×11 (Tables and Figures) =6,440

A Paper Submitted for Presentation at the 94th Transportation Research Board Annual Meeting

Jalayer, Hu, Zhou, and Turochy 2

ABSTRACT 1 For many years, state departments of transportation (DOTs) and local agencies have collected 2

and maintained highway inventory data (HID) in order to assist the decision-makers at different 3

levels. In light of the implementation of the Highway Safety Manual (HSM) in 2010, many state 4

DOTs have sought to tailor the various safety measures and functions to evaluate the safety in 5

their jurisdictions; however, insufficient HSM-required HID in many current DOT’s databases 6

necessitating the collection of the absent features. In order to obtain these data, various 7

techniques for different purposes have been utilized, including field inventory, photo/video log, 8

integrated GPS/GIS mapping systems, aerial photography, satellite imagery, terrestrial laser 9

scanners, airborne LiDAR, and mobile LiDAR. Among many data collection methods, the 10

photo/video log is widely employed by DOTs due to its simplicity and low cost; therefore, the 11

focus of this manuscript, which is a timely and needed research, is to evaluate the capability of 12

the photo/video logging method to collect HID for supporting HSM implementation through a 13

comprehensive literature review, a nationwide survey, and a field trial. The results of this study 14

demonstrated that the photo/video log can provide worthy and relevant HSM datasets with 15

acceptable accuracy. 16

17

Keywords: Highway Inventory Data (HID), Highway Safety Manual (HSM), Photo/Video Log 18

Method, Data Collection 19


INTRODUCTION 1 The Highway Safety Manual (HSM), released in 2010, assists state department of transportations 2

(DOTs) and local agencies to evaluate roadway safety performance at planning, design, 3

construction, and operation stages. The manual provides predictive models for three types of 4

facilities, including [1] rural two-lane, two-way roads, [2] rural multilane highways, and [3] 5

urban and suburban arterials (1). In order to implement these models, collection of all the 6

necessary information along the roadways, which is time-consuming and often requires 7

significant cost, is imperative. As of today, many different data collection techniques are being 8

used by state DOTs and local agencies including, but not limited to, field inventory, photo/video 9

log, integrated Global Positioning System (GPS)/Geographic Information Systems (GIS) 10

mapping systems, aerial photography, satellite imagery, airborne Light Detection and Ranging 11

(LiDAR), terrestrial laser scanners, and mobile LiDAR. The difference between these methods is 12

associated with the demanded efforts for data collection as well as data reduction. The major task 13

is to match the data collection techniques to highway inventory applications. It is not clear which 14

of these methods or any combination of them is capable of efficiently collecting required data 15

while minimizing costs and safety concerns. Presently, however, among aforementioned data 16

collection techniques, the photo/video log is rapidly employed by state DOTs due to less data 17

collection effort and associated cost than other methods. The video log process moves the 18

inventorying practice from the field into the office. 19

The focus of this study is to evaluate the capability of the photo/video logging method to 20

collect highway inventory data (HID) for supporting HSM implementation. In doing so, a 21

nationwide web-based survey was developed and sent to all the state DOTs to gather their 22

opinions toward the photo/video log method and their perception of which features are collected 23

via this method. Field trials were also conducted to recognize the pros and cons of the 24

photo/video log to gather related HSM-required datasets. By virtue of the fact that many state 25

DOTs are currently redesigning their asset management plans to meet Moving Ahead for 26

Progress in the 21st Century (MAP-2) requirements, which proactively address safety concerns, 27

the outcomes of this research effort may provide a resource for saving money and time. 28

29

RESEARCH BACKGROUND 30

HSM-related Highway Inventory Data 31

In the HSM, highway safety performance functions (SPFs) estimate the average crash frequency 32

for roadway segments and intersections based on roadway characteristics (i.e., length) and traffic 33

conditions (i.e., Average Annual Daily Traffic [AADT]). This estimation is accomplished by 34

categorizing all the facilities into three groups including rural two-lane roadways, rural multi-35

lane highways, and urban and suburban arterials. Each of these facilities has their own uique 36

inputs. Tables 1 and 2 summarize the required input data for the predictive models in the HSM. 37

The check marks indicate the required variables for roadway segments and intersections. 38

39


TABLE 1 Highway Inventory Data Required for Road Segments in the HSM (1) 1

Variables Rural Two-lane, Two-way Roads

Rural Multilane Highways

Urban and Suburban Arterials

Number of through lanes Lane width Shoulder width Shoulder type Presence of median Median width Presence of passing lane Presence of rumble strips Presence of two-way left-turn lane (TWLTL) Driveway density Number of major/minor commercial driveways Number of major/minor residential driveways Number of major/minor industrial/institutional driveways

Number of other driveways Horizontal curve length Horizontal curve radius Horizontal curve superelevation Presence of spiral transition Grade Roadside hazard rating (RHR) Roadside slope Roadside fixed object density/offset Percent of length with on-street parking Type of on-street parking Presence of lighting Presence of auto speed enforcement

2 TABLE 2 Highway Inventory Data Required for Intersections in the HSM (1) 3

Variables Rural Two-lane, Two-way Roads

Rural Multilane Highways

Urban and Suburban Arterials

Number of intersection legs Number of approaches with left-turn lane(s) Number of approaches with right-turn lane(s) Intersection skew angle Presence of lighting Pedestrian volume/lane Number of bus stop within 1000 ft. Number of alcohol sales within 1000 ft. Presence of schools within 1000 ft.

4

To date, many of these variables have already been or are routinely collected by state 5

DOTs and are available in their highway inventory databases. Nevertheless, in many states, there 6

is a lack of worthy highway databases that include all the required variables as inputs for the 7

HSM predictive models. The current Illinois Department of Transportation (IDOT) databases 8

contain a number of roadside information collection requirements, such as roadside fixed objects, 9

their density and offset to the edge of travel, and RHR. The latter is a subjective measure, which 10

is categorized in seven different categories, to characterize the potential hazard related to 11

roadside environment. The main contribution of this study is the evaluation of the capabilities/ 12


incapabilities of the photo/video log method to collect the absent information in the most 1

economical and effective way. Given that these features are also missing in many state DOTs 2

databases, the findings of this study are valuable in providing guidance for other states. 3

4

Review of Photo/Video Logging Method 5

HID collection methods can be broadly categorized in two groups: land-based and air- or space-6

based methods. The photo/video logging method almost falls more closely under the first group, 7

land-based, while taking advantages of GPS and imaging technology. In this method, a vehicle 8

drives along the roadway while automatically recording photos/videos that can be examined later 9

to extract required information. Short field data collection time and less exposure to traffic are 10

two major advantages; however, the inability to measure feature dimensions and the need for 11

large data reduction efforts are some disadvantages of the photo/video logging method. Maerz 12

and McKenna (1999) conducted a study to collect data on objects, features, structures, and 13

landmarks along the highways using a high-speed multifunction vehicle equipped with a video 14

system (2). The results revealed that this technology not only was safe for the survey crew but 15

also was fast in data collection. A relative comprehensive dataset, including signs and their 16

positions, guiderails, bridges, cables, and slopes, can be collected using this method and then 17

stored in a database or even in GIS database. 18

Another study, sponsored by the Iowa Department of Transportation (3), concentrated on 19

collecting roadside features using mobile digital cameras. Through this research, three-20

dimensional locations of roadside features were captured without the necessity of ground control. 21

The locations of features could be extracted by relative cameras’ exposure locations via GPS. 22

The results demonstrated the capability of this method to collect features, such as road edges and 23

utility poles with a relative accuracy of two inches. The National Cooperative Highway Research 24

Program (NCHRP) conducted research to develop an image processing algorithm to enhance the 25

roadside sign data collection process (4). Since collecting data on roadside signs is time-26

consuming, costly, and in some ways unsafe, using video log images equipped with this 27

recognition algorithm can provide significant assistance. In an attempt to create an automated 28

road sign inventory system using stereo and tracking, Wang et al. (2010) collected and analyzed 29

a research site in Fayetteville, Arkansas. The detailed investigation selected 26 out of 52 signs on 30

the site for the road test. The results indicated that the required dataset could be gathered with 31

acceptable accuracy (5). 32

The Institute of Critical Technologies and Applied Science (ICTAS) at Virginia Tech. 33

sponsored a study to evaluate a novel video-based recognition technology to collect DOT-34

required asset inventories (6). Using the videos recorded from the mounted camera on the 35

vehicle, an algorithm was developed to automatically recognize the roadside features. Like other 36

studies, the results of research demonstrated the capability of this method in collecting roadside 37

features with reasonable accuracy. 38

Based upon a comprehensive literature review (2-19), it can be noted that although there 39

is a considerable number of studies on the photo/video log method, none of them have solely 40

focused on supporting HSM implementation. Moreover, it is not obvious to what extent this 41

method has been employed by various state DOTs. Such valuable information might provide 42

other state DOTs and local agencies a guideline to better understand what kinds of information 43

can be gathered through photo/video logging to prepare adequate HSM-required road inventory 44

datasets. 45


SURVEY DATA COLLECTION AND ANALYSIS 1 In many states, there is a lack of statewide that include all the required variables as inputs for the 2

HSM predictive models. On the other hand, many state DOTs do have road inventory databases 3

that provide some data elements that can be used in the HSM predictive models. In order to gain 4

understanding of the implementation status of various HID collection methods and their pros and 5

cons, a web-based survey was developed and sent to 50 state and seven Canadian provinces. The 6

respondents were asked to mark their primary data collection methods and their views in terms 7

of time, cost, safety, data storage requirements, and accuracy. Particularly, roadside objects and 8

roadside slopes, for the purpose of this study, were addressed in the proposed survey. The 9

roadside features included: bridge rails, fence, fire hydrants, glare screen, guardrails, impact 10

attenuators, jersey barriers, junction boxes, lighting, luminaries, milepost, paddles, signs, rock 11

outcropping, sign support, signals, trees, tree groupings, utility poles, and walls. 12

Based on 30 respondent states, more than 40% of them collect roadside feature data 13

through a video logging method. Additionally, photo logging has been gradually replaced by 14

video logging. The major difference between photo and video log is that the latter records photos 15

continuously. The results revealed that due to difficulty in recognition of small vertical objects, 16

the satellite imagery and airborne LiDAR are less popular methods. Although mobile LiDAR is 17

not a common method among state DOTs, it is becoming more widespread. Figure 1 illustrates 18

the percentage of states using each type of HID collection methods. It should be noted that some 19

states use multiple methods that account for the total being more than 100%. 20

21

22 FIGURE 1 Technology Adoption Percentage in Respondent States 23

24

The respondents were also asked to identify their primary highway inventory data 25

platform technology including GIS, Oracle, SQL, Excel, Access, and others. According to the 26

obtained results, Oracle was the predominant data storage platform; however, many agencies 27

used Oracle in combination with other systems such as GIS. 28 29


1

FIGURE 2 Percentage Use of Each Data Storage Platform in Respondent States 2 3

To further investigate the capability of each data collection method to gather roadside 4

features, the respondents were requested to specify those features collected using each applied 5

method, indicating that guardrails, shoulders, and mileposts signs are the most predominant 6

objects collected. Among the roadside features, the photo/video log has the ability to collect all 7

the roadside features except side slope and curvatures characteristics. Notably, less than 10 8

percent of states collected roadside slope and curvature alignments. Along with data collection 9

methods, data storage platforms, and types of data collected, the survey respondents were asked 10

to indicate their level of satisfaction with their primary collection method using a scale of 1 to 5 11

(representing unacceptable, fair, good, very good, and excellent, respectively). Based on the nine 12

satisfaction indicators used in this study as shown in Table 3, most states expressed their level of 13

satisfaction as “good” for the primary data collection methods. More specifically, Figure 3 14

illustrates the detailed information toward the different levels of satisfaction for the primary data 15

collection method among respondent states. 16

Based on Figure 3, the photo log, the satellite imagery, and the aerial imagery scored 17

highest among all the HID collection methods. Looking at the results in detail, the lowest scores 18

were assigned to data reduction time, data collection time, and data collection cost. The results 19

indicated that data collection, reduction efforts, and associated cost are the most common 20

concerns for state DOTs. Given this fact, the photo/video logging method appeared more 21

desirable to state DOTs. 22


TABLE 3 Levels of Satisfaction for Primary Data Collection Method of State DOTs 1

Satisfaction Factors Unacceptable (%)

Fair (%)

Good (%)

Very Good (%)

Excellent (%)

Sum (%)

Equipment cost rating 0 21 58 21 0 100 Data accuracy rating 0 7 41 45 7 100 Data completeness rating 7 17 34 34 7 100 Crew hazard exposure rating 4 29 39 21 7 100 Data collection cost rating 3 24 55 17 0 100 Data collection time rating 3 34 48 14 0 100 Data reduction time rating 11 26 30 26 7 100 Data reduction cost rating 4 39 29 21 7 100 Data storage requirement rating 0 14 52 31 3 100

2

3 FIGURE 3 Level of Satisfaction of Various Inventory Data Collection Methods among 4

Respondent States 5

6

FIELD TRIAL AND RESULTS 7 To evaluate the photo/video logging method’s capability/incapability of collecting the required 8

dataset, this study used three different types of roadway segments including rural two-lane, two-9

way roads, rural multilane highways, and urban and suburban arterial segments as the test sites. 10

For all selected segments, which varied in length but all were longer than one mile, the data 11

collection time and data reduction time were recorded separately: the former during the field trip 12

and the latter in-office. Manual review and photogrammetry, making measurements from 13

photographs, are the proposed data reduction methods for photo/video logging. 14

As for data collection, the geo-tagged digital videos and photos were gathered through 15

the use of Red Hen video mapping system (www.redhensystems.com). Notably, this video 16

mapping system, which uses a video camcorder and a GPS antenna, has the capability to collect 17


geo-tagged digital video with essential locational information. Figure 4 depicts a configuration of 1

a video logging system implemented in this study. The researchers collected video with a total 2

data volume of slightly over 5 GB, for a total of 28 miles for selected roadways during two 3

hours. The GPS enabled photo/video logging requires a relatively short time of data collection 4

with an extensive feature extraction effort in the office. In this study, the average time for data 5

collection using this method was 4.3 minutes per mile. 6

7

8 FIGURE 4 A Video Logging System Configurations in Use for Data Collection 9

10

After data collection, the video files were imported into ArcGIS 9.3 software (with an 11

ArcView 9.3 or Arc Editor 9.3 license) as inputs for an ArcGIS extension (or GeoVideo). These 12

files contained both digital motion pictures and GPS locations for the roadways. This GeoVideo 13

program creates a point feature class that correlates with the GPS locations where each video 14

was taken. The GeoVideo allows the user to click on any point to start playing the video file, 15

allowing the system operator to identify roadside objects (Figures 5 and 6). 16

17

18 FIGURE 5 Guardrail as a Roadside Object Showed in Video 19


1 FIGURE 6 Light Pole as a Roadside Object Showed in Video 2

3 The data reduction effort requires the user to recognize roadside features from among 4

these points, lines, and polygons through on-screen digitizing with the help of both high-5

resolution imagery (e.g., 1-ft digital orthophotos or satellite imagery) and video files. The 6

ArcGIS software creates a point feature class that includes points, lines, and polygons. Figure 7 7

illustrates an example of object extractions using both video logging and high-resolution 8

imagery. 9

10

11 FIGURE 7 Fire Hydrant as a Roadside Object Showed with High-resolution Imagery in 12

Background and Video File in Forefront 13 14

Using the inventory number given in the existing IDOT GIS database, the extracted 15

roadside objects can then be assigned to each roadway segment. This was accomplished through 16

a spatial join process in the ArcGIS software. In doing so, a snap tool was first utilized to assign 17

each extracted object to the nearest road segment. The number of objects for each segment was 18


tallied using 30 feet as a buffer or clear zone, and objects beyond this zone were not tallied. 1

Table 4 lists the total count of the number of objects, Total_Count, assigned to each roadway 2

segment that is designated by the road inventory number. As discussed in the previous section, 3

half of the state DOTs have GIS highway inventory data platforms. Consequently, the 4

methodology set forth by this study can be followed by other DOTs. 5

6

TABLE 4 Total Number of Objects Assigned to Each Roadway Segment Using Inventory 7

Number 8 Total_Count Segment Inventory Number Name 1 06070138 Highway Sign-Bike 0 06098875 4 06098875 Highway Sign 0 06098875 6 06080929 Tree 6 06080926 Light Pole 7 06080902 Highway Sign 0 06080931 1 06080935 Light Pole 0 06080916

9

It should be noted that work or effort is simply measured in some units like man-days, 10

person-years, person-hours, and so forth (1820); therefore, for the purpose of this study, person-11

hours was selected. In this research, a total of 1,141 objects were extracted in 23 person-hours of 12

work in-office for the total 28 miles of roadway segments, averaging approximately 41 objects 13

per mile or 1.25 minutes per object. Regarding cost analysis, two unit labor costs were assumed: 14

$35 per hour for a person trained at an introductory level and $65 per hour for an expert-level 15

person. The photo/video logging required 0.7 person-hour work per mile at an introductory level 16

and 0.3 person-hour work per mile at an expert level with the total cost including data collection 17

and data reduction efforts at $44 per mile. 18

Taking advantage of a vehicular platform, the photo/video logging method collects 19

roadside data without exposing the data collection crew to traffic flow, which not only increases 20

the safety of the crew but also eliminates the necessary roadway closures. Moreover, checking 21

with the field values, this method can provide all HSM-required inventory data except RHR with 22

an acceptable accuracy using high-resolution aerial photographs or satellite imagery. A 23

locational accuracy of six inches for all roadside objects is achievable with 1-foot spatial 24

resolution images. Additionally, although the RHR cannot be acquired directly via this method, it 25

is still possible to estimate it at some level. Further study is needed to explore how to estimate 26

the RHR based on the data collected by the photo/video logging method. 27

28

CONCLUSION 29 Since the release of the HSM in 2010, many state DOTs and local agencies have been 30

implementing the manual to improve the safety in their jurisdictions. One of the challenges faced 31

by state and local transportation professionals is to collect all HSM-required highway inventory 32

data. Although some of these data have already or are routinely collected by state DOTs and are 33

available in their databases, there is a lack of worthy highway databases that include all the 34

required variables as inputs for the HSM predictive models. This study showed that the 35

photo/video logging method has been recognized as one of the most common data collection 36


techniques among state DOTs due to its simplicity and low associated costs. It also provides 1

timely and needed research on identifying the capability of the photo/video logging method to 2

collect HSM-required datasets. Given this information, a comprehensive literature review, a 3

nationwide survey, a field trial, and a cost analysis has been conducted to demonstrate the pros 4

and cons of this method. The results revealed that, unlike many other HID collection methods, 5

photo/video logging with high-resolution imagery is less costly and less time-consuming. 6

Additionally, this method can be utilized to gather required data with an acceptable accuracy 7

required by the HSM for large-scale statewide data collection in a short time period. 8

9

ACKNOWLEDGMENT 10 This study was made possible through funding from the Illinois Department of Transportation 11

(IDOT) and the Illinois Center for Transportation (ICT). The authors thank the Technical Review 12

Panel (TRP) members for their inputs on this project. 13

14

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