journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

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Original Research Paper Traffic sign vandalism and demographics of local population: A case study in

* Majid Khalilikhah a, , Kevin Heaslip b, Kathleen Hancock c a Smart Urban Mobility Lab, Virginia Polytechnic Institute and State University, Falls Church, VA 22043, USA b Department of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA c Department of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Falls Church, VA 22043, USA article info abstract

Article history: Among the different types of traffic sign damage, vandalism is exclusively caused by Received 21 July 2015 humans. Traffic sign vandalism is a serious concern, since it can lead to an increase in Received in revised form unsafe driving behaviors. In addition, it results in increased costs to transportation 20 October 2015 agencies to replace, repair, or maintain the vandalized signs. This paper examines the Accepted 2 November 2015 association between the local population demographics and traffic sign vandalism rates in Available online 22 April 2016 the State of Utah. To accomplish this goal, sign data of over 97,000 traffic signs across Utah were digitally collected by an equipped vehicle. Sign damage data were obtained from the Keywords: inspection of daytime digital images taken of each individual sign. Demographic data of Traffic sign vandalism Utah's counties, including population density, ethnicity, age, income, education, and Mobile-based data collection gender, were obtained from the U.S. Census. The association between demographic groups Demographics and vandalism rates was tested using chi-square and trend tests. The results reveal that Statistical methods the most statistically significant variables comprise median household income, completion of at least an associate degree, and population density. According to the fitted linear regression model, a relationship exists between sign vandalism rate and local population demographic. The findings of this investigation can assist transportation agencies in identifying areas with a higher likelihood of sign vandalism, based on demographic characteristics. Such information can then be used to encourage scheduled sign in- spections and to implement various countermeasures to prevent sign vandalism. © 2016 Periodical Offices of Chang'an University. Production and hosting by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

safety improvement. Since the important task of traffic signs 1. Introduction is to convey critical information to drivers, the replacement of ineffective signs promotes safe driving and road navigation To reduce the number of vehicular crashes, transportation (Hugh and McGee, 2010). To better see and understand traffic agencies across the country continually make efforts for

* Corresponding author. Tel.: þ1 435 554 8499. E-mail addresses: [email protected] (M. Khalilikhah), [email protected] (K. Heaslip), [email protected] (K. Hancock). Peer review under responsibility of Periodical Offices of Chang'an University. http://dx.doi.org/10.1016/j.jtte.2015.11.001 2095-7564/© 2016 Periodical Offices of Chang'an University. Production and hosting by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202 193 signs, both high legibility and visibility are key characteristics. 2.1. Traffic sign vandalism background A study concluded that sign damage causes a decline in the overall legibility of the sign (Boggs et al., 2013). The effects of Previously, multiple studies have focused on traffic sign con- damage on the legibility of traffic signs vary considerably ditions (Brimley and Carlson, 2013; Carlson and Lupes, 2007; with respect to the form of damage (Khalilikhah and Harris et al., 2009; Jalayer et al., 2014; Khalilikhah et al., 2015a; Heaslip, 2016a). Of all forms of sign damage, vandalism is Khalilikhah and Heaslip, 2016b; Kipp and Fitch, 2009; Mace the only one that is exclusively caused by humans. Types of et al., 1982; Rasdorf et al., 2006; Worsey et al., 1986). Also, human vandalism on signs include shooting paintballs and researchers have studied traffic signs based on road user bullets, throwing beer bottles, putting stickers, and painting characteristics. For example, the understandability and graffiti (Evans et al., 2012a). The overall legibility of the sign comprehensibility of traffic signs, as well as driver recognition is affected by vandalism during both daytime and nighttime of signs, have repeatedly been investigated (Drory and Shinar, conditions. In addition, up to hundreds of thousands of 1982; Kirmizioglu and Tuydes-Yaman, 2012; Liu et al., 2014; dollars are spent to repair or replace vandalized signs (Evans Vigano and Rovida, 2015). However, few studies have focused et al., 2012b; Harris, 1992). Previously, a study showed that on traffic sign vandalism. Over the past decades, traffic sign the vandalism rate for rural signs was greater than that of vandalism has become a serious problem for traffic agencies urban signs (Boggs et al., 2013). (Chadda and Carter, 1983; Harris, 1992). With special attention After focusing on the sign data collected throughout the given to its various forms of sign vandalism, some researchers State of Utah, the authors observed that a significant have discussed countermeasures against vandalism (Picha, number of measured signs (97,314) were damaged, inten- 1997; Perkins and Barton, 1997). Many have proposed the uti- tionally caused by humans. Due to a lack of detailed infor- lization of various countermeasures to reduce sign vandalism, mation about traffic sign vandalism, this paper aims to such as using more resistant materials to construct signs, answer the following question. How does the vandalism rate mounting signs higher, applying penalty notices to signs, and change with respect to the local population demographics using public information campaigns. Other studies have where the sign is placed? To answer this goal, creating con- estimated the costs of sign vandalism (Smith and Simodynes, tingency tables is necessary to yield the desired results. 2000), developed methods for sign vandalism detection The authors used U.S. Census Bureau data to obtain the - (Mueller, 1995), and examined the effects of releasing demographic data, such as population, ethnicity, age, information via the media to reduce sign vandalism (Ellison, income, education, and gender. To identify more 1996). Although the association between demographics and significant demographics associated with sign vandalism, the transportation infrastructure and facilities, such as road authors applied the chi-square and trend statistic tests improvements (Majumdar and Mitra, 2014), public transit with respect to the categorical variables. Research results (Mulley et al., 2014), pedestrian overpass (Wu et al., 2014), serve as a basis for agencies to schedule more frequent in- and car ownership (McGoldrick and Caulfield, 2015) have spections or conduct countermeasures in areas that are been studied by many researchers, the research regarding more capable to vandalism. The paper reviews recent the effects of demographics on traffic sign vandalism rate is research efforts regarding sign vandalism and examines the far from complete. This study aims to fill this gap. collected data. Then, the results of the data analysis are pre- sented, and key research findings and conclusions are 2.2. Traffic sign data collection methodology identified. In an effort to obtain comprehensive information about its road assets, the Utah Department of Transportation (UDOT) sponsored a mobile-based data collection effort to digitally 2. Data description capture transportation assets in 2012 (Ellsworth, 2013). This included data collection on many types of roadway assets, To decrease crash numbers, specifically fatality crashes, including traffic signs, pavements, guardrails, barrier walls, transportation agencies have no choice but to take into and markers across over 6000 center lane miles of state consideration all of the contributing factors, including routes and interstates. This comprehensive approach was drivers, vehicles, and roadway infrastructure (Baratian- deployed by an instrumented vehicle driving at freeway Ghorghi et al., 2015; Preston et al., 2015). Stop signs, yield speeds and collecting asset data on the roadway. The vehicle signs, and speed limits are a few examples of traffic signs sensors included a light detection and ranging (LiDAR) that increase safety by conveying essential information to sensor, laser road imaging system, laser rut measurement drivers (Prieto and Allen, 2009). Previous studies showed that system, laser crack measurement system, road surface driver behavior can be dramatically influenced by the profiler, and position orientation system. In addition, placement of yield to pedestrian at crosswalks signs (Ellis imaging technologies were integrated to automatically et al., 2007), work-zone warning signs (Strawderman et al., collect high-resolution detailed images from the assets. 2013), and school zone signs (Strawderman et al., 2015). In Fig. 1 shows a snapshot of the data collection process. addition, a study concluded that installing chevrons (W1- The first phase of the project, data gathering, was 10 traffic signs) in curves and using supplemental signs completed by driving one vehicle along state roads. The sec- when implementing the flashing yellow arrow signals ond phase of the project, post-processing, was conducted by significantly reduce crashes (Schattler et al., 2015; Zhao survey to derive the desired data. At project conclusion, data et al., 2015). on more than 97,000 traffic signs under the jurisdiction of 194 journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

Fig. 1 e Mobile-based data collection process. (a) Equipped vehicle (http://www.mandli.com/lidar/). (b) Taking image of traffic signs (http://168.178.125.102/roadview.asp?Route¼0080P&Mile¼61.6).

UDOT were recorded. The surveyed data included sign attri- the authors categorized the damaged signs into three butes, such as location, size, orientation, and mount height. In groups: aging/environmental, vandalism, and unknown. addition, during the post-processing analysis, the digital im- Traffic signs that were categorized as aging/environmental ages captured in daytime were examined by a trained oper- had faded colors or obstructed view, were bent by wind or ator, and traffic signs with any form of face damage were snow, were bent or knocked down by vehicles, or were dirty, identified throughout the entire data set. delaminated, or rusty. Essentially, aging consists of deterioration formed over time, whereas environmental 2.3. Categories of traffic sign damage forms were the result of weather or other natural factors. Damage unintentionally caused by humans is also included Traffic signs exhibited various forms of damage that, in this group. Vandalism, on the other hand, describes any depending on the damage form, could caused by humans or deliberate damage to the sign face, including stickers, paint, nature. For example, signs were bent, delaminated, dented, dents, gunshots, and graffiti. The data also identified a group dirty, faded, fallen, graffiti or paint, obstructed, rusted, and of signs with uncertain sources of damage, which are covered by stickers. Almost 7% of all measured signs were included in the unknown grouping. damaged. To ensure the data accuracy, the authors used a Table 1 summarizes the number of damaged signs for each sample data set collected by their research team in the field of the 29 counties across the State of Utah. Since the locations (details provided by Khalilikhah et al. (2015b)), including (latitude and longitude) of measured traffic signs were taking photos of about 1700 traffic signs (Fig. 2). Ultimately, recorded by the equipped vehicle, traffic sign data were

Fig. 2 e Traffic sign damage categories (photos taken by this research team). (a) Environmental. (b) Vandalism. (c) Aging. journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202 195

imported into ArcGIS software to identify the counties in 20,253 km2. In this study, geographic size and population which the signs were placed. As shown in Table 1, the rate data were obtained to examine whether a county'spopula- of damage and vandalism differed by county. After tion density and the number of people per unit of area examining the results more closely, different vandalism influenced the vandalism rate. In general, Utah is not a rates were found across counties. Piute County had, by far, high-density state. With an average population density of the highest rate of sign vandalism, whereas Davis County 33.31 persons per square mile, Utah is ranked 41st out of the experienced the lowest rate. Fig. 3 shows the locations of 50 U.S. states. To provide some perspective, the national traffic signs and identifies the vandalized signs. average population density is 95.66 persons per square mile (WorldAtlas, 2014). 2.4. Demographic characteristics in Utah The research considers people of all backgrounds, such as , Blacks, Indians, and Asians, who make up 20% of In order to assess the association between the vandalism rate the total population of Utah, and Caucasians, who make up and socio-economic characteristics of each county where 80% of the population (Perlich, 2011). The percentage of traffic signs are placed, U.S. Census Bureau data could be Caucasians in Utah is higher than in the in useful (Table 2). Online sources were used to obtain the general (63%). San Juan County is the only county in Utah various desired demographic data from U.S. Census data where Caucasians are not the majority group (43.9%). (Governor's Office of Planning and Budget, 2010; Howden and However, 74% of 's residents are Caucasians, Meyer, 2010; Matthews, 2010; Perlich, 2011; United States and Morgan County has the highest percentage of Census Bureau, 2010a, 2014). Based on the 2010 United Caucasians (96.1%). Compared to the United States, Utah is States Census Bureau data, Utah is composed of 29 counties, also the youngest state, with a median age of 29.2 years with an overall population of 2,763,885 people. old, while the national median age is 37.2 (Governor'sOffice In Utah, county populations range from about 1000 to of Planning and Budget, 2010). The state's youngest 1,000,000. Salt Lake County is the most populous county, population is centered in Utah County, with a median age wherein approximately 37% of Utah's inhabitants reside. of 24.6, followed closely by Cache County (25.5). Kane, The population of four counties, namely Daggett, Piute, Daggett, and Piute Counties have the highest median ages Rich, and Wayne, is less than 5000 residents. Moreover, in Utah. Davis County is the smallest county, with an area of According to the 5-year estimates of median household 774 km2, whereas San Juan County is the largest, with income obtained by the U.S. Census Bureau, the average

Table 1 e Damaged signs by county. County Number of signs Damage Percentage of Vandalism damage (%) rate (%) None Vandalism Aging/environmental Unknown Beaver 1585 1399 64 75 47 11.74 4.04 Box Elder 5152 4744 42 192 174 7.92 0.82 Cache 2821 2541 61 143 76 9.93 2.16 Carbon 2130 1932 61 79 58 9.30 2.86 Daggett 1180 1077 20 57 26 8.73 1.69 Davis 5279 5112 21 116 30 3.16 0.40 Duchesne 2038 1800 47 89 102 11.68 2.31 Emery 3071 2834 44 92 101 7.72 1.43 Garfield 3216 3009 39 67 101 6.44 1.21 Grand 2514 2217 81 117 99 11.81 3.22 Iron 3105 2751 66 118 170 11.40 2.13 Juab 1642 1570 25 24 23 4.38 1.52 Kane 1731 1656 13 46 16 4.33 0.75 Millard 3698 3429 97 104 68 7.27 2.62 Morgan 1123 996 14 73 40 11.31 1.25 Piute 921 789 63 41 28 14.33 6.84 Rich 942 866 7 36 33 8.07 0.74 Salt Lake 15,790 15,123 267 318 82 4.22 1.69 San Juan 3910 3566 67 159 118 8.80 1.71 Sanpete 2198 2105 27 22 44 4.23 1.23 Sevier 4198 3888 56 130 124 7.38 1.33 Summit 3715 3518 17 130 50 5.30 0.46 Tooele 3118 2902 22 95 99 6.93 0.71 Uintah 1838 1549 47 113 129 15.72 2.56 Utah 8711 8339 113 181 78 4.27 1.30 Wasatch 1665 1535 12 71 47 7.81 0.72 3481 3306 30 105 40 5.03 0.86 Wayne 1592 1460 39 37 56 8.29 2.45 Weber 4950 4740 38 121 51 4.24 0.77 196 journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

income value for Utah ($56,330) is higher than the national people living in Summit. The percentage of Americans median income of $51,914 (United States Census Bureau, between the ages of 25 and 64 with at least a two-year 2010a). As defined by the American Community Survey college degree is 38.7%, whereas 40.3% of the working- (ACS): “Income of household includes the income of the age adults in the State of Utah hold at least an associate householder and all other individuals 15 years old and degree (Matthews, 2010). In addition, residents of Beaver over in the household, whether they are related to the and Summit Counties have respectively the least and householder or not” (United States Census Bureau, 2010a). highest rates of educated people. By considering Summit is the richest county in Utah, with a household gender composition data, the gender ratio (male to median income of $79,461. This is followed by Morgan female) for the State of Utah is 1.009, which is higher than County ($70,152). The median income for Daggett, the U.S. gender ratio of 0.967 (United States Census Piute, and San Juan Counties are less than half of that of Bureau, 2014).

Fig. 3 e Locations of traffic signs. journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202 197

Table 2 e Demographic characteristics by county in Utah. County Area (km2) Population Percentage of Median Median Percentage of Gender ratio ethnic majority (%) age household adults with (male to female) income (USD) associate degree, or higher (%) Beaver 6708 6629 86.0 31.9 41,514 19.7 1.059 Box Elder 14,880 49,975 88.3 30.6 55,135 32.2 1.016 Cache 3017 112,656 85.5 25.5 47,013 44.5 0.988 Carbon 3829 21,403 84.1 34.4 41,967 28.4 0.984 Daggett 1805 1059 94.4 42.8 36,389 32.8 1.292 Davis 774 306,479 85.8 29.2 66,866 45.7 1.008 Duchesne 8394 18,607 87.1 29.7 52,895 24.6 1.033 Emery 11,557 10,976 92.1 32.8 49,237 27.3 1.037 Garfield 13,403 5172 91.6 39.0 44,745 31.1 1.071 Grand 9509 9225 84.1 39.9 41,396 31.4 1.015 Iron 8538 46,163 87.1 26.8 42,247 37.5 0.989 Juab 8786 10,246 94.0 29.3 53,225 24.3 1.042 Kane 10,334 7125 93.2 44.5 43,540 34.3 0.977 Millard 17,022 12,503 84.7 33.7 44,594 30.7 1.038 Morgan 1578 9469 96.1 32.0 70,152 39.3 1.016 Piute 1963 1556 91.2 40.5 37,708 23.6 1.047 Rich 2664 2264 94.1 34.7 54,737 32.9 1.069 Salt Lake 1922 1,029,655 74.0 30.8 58,004 40.1 1.012 San Juan 20,253 14,746 43.9 29.9 38,076 31.9 1.008 Sanpete 4118 27,822 86.7 28.4 42,395 31.4 1.098 Sevier 4948 20,802 92.9 32.8 45,622 28.4 1.018 Summit 4848 36,324 85.4 37.1 79,461 59.5 1.064 Tooele 17,977 58,218 84.5 29.6 60,590 31.0 1.015 Uintah 11,602 32,588 82.8 29.1 59,730 21.1 1.034 Utah 5189 516,564 84.2 24.6 56,927 47.9 1.004 Wasatch 3044 23,530 84.2 31.6 65,204 42.2 1.034 Washington 6284 138,115 85.6 32.5 50,050 33.7 0.978 Wayne 6373 2778 93.4 37.1 49,414 33.7 1.022 Weber 1492 231,236 78.1 30.7 54,086 33.2 1.008

increase the power of analysis by using a test statistic with a 3. Data analysis more specific alternative, such as trend tests. The authors began the analysis by comparing traffic sign This section of the study discusses the rates of the surveyed vandalism rates with demographic characteristics of vandalism, with special attention given to the demographics counties. Fig. 4 visualizes the association between county of the State of Utah, including population, ethnicity, age, in- demographics and vandalism rates. In this figure, the gender come, education, and gender composition. The authors used ratio is not included due to the uniformity of counties' sex the chi-square test to examine the association between the ratio. The study also excluded ethnic majority because no two categorical variables. To do so, they defined the level of clear trend. Counties are shaded based on vandalism rates, e variables as same as the levels shown in Tables 3 8. In addi- while the size of the symbols reflects the variation in each tion, they used trend test to increase the power of our anal- demographic characteristic. As seen in Fig. 4, counties ysis. According to Agresti (2007), having a 2 J (or I 2) table located in Northern Utah show a lower vandalism rate than with categorical column (row) variable Y that fairly shows a in Sothern Utah counties. This finding is likely a reflection of trend (increase or decrease) in the studied feature, we can variation in demographics of counties. Focusing on counties with the highest vandalism rate, in comparison with the others, Beaver, Piute, and Grand Counties have lower population densities, poorer household incomes, roughly Table 3 e Vandalism rates by population density. Density Number of Vandalism Vandalism (people per km2) signs rate (%) Yes No e <1 24,360 534 23,826 2.19 Table 4 Vandalism rates by type of area. 1e5 17,986 239 17,747 1.33 Area Number of signs Vandalism Vandalism 5e10 13,936 197 13,739 1.41 rate (%) Yes No 10e100 15,013 204 14,809 1.36 >100 26,019 326 25,693 1.25 Urban 46,611 410 46,201 0.88 Rural 50,703 1090 49,613 2.15 Chi-square test statistic ¼ 92.39 (p-value < 0.0001). Trend test statistic ¼7.65 (p-value < 0.0001). Chi-square test statistic ¼ 257.32 (p-value < 0.0001). 198 journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

Table 5 e Vandalism rates by percentage of ethnic Table 7 e Vandalism rates by income. majority. Median household Number of Vandalism Vandalism Percentage of Number Vandalism Vandalism income, 2006e2010 signs rate (%) Yes No majority group (%) of signs rate (%) Yes No (USD) <42,000 12,240 356 11,884 2.91 <85 48,324 805 47,519 1.67 42,000e45,000 13,948 242 13,706 1.74 >85 48,990 695 48,295 1.42 45,000e50,000 11,682 200 11,482 1.71 ¼ ¼ Chi-square test statistic 9.63 (p-value 0.002). 50,000e55,000 13,053 147 12,906 1.13 55,000e60,000 31,491 469 31,022 1.49 >60,000 14,900 86 14,814 0.58 higher median ages, and lower rate of education levels. With Chi-square test statistic ¼ 263.09 (p-value < 0.0001). the exception of Washington County, it can also be stated Trend test statistic ¼13.83 (p-value < 0.0001). that counties with fewer number of vandalism signs are not very populous. Except Cache County, Utah's counties with a population density greater than ten persons per square mile Table 4 summarizes the traffic sign vandalism rate by area exhibit traffic sign vandalism rates less than two percent. type. Rural signs exhibit a higher rate of vandalism in The existence of Utah State University in Cache County may comparison with urban signs. The chi-square value was also contribute to this finding where the area is heavily statistically significant. This finding is similar to those of populated but the vandalism rate of signs is more than two Black et al. (1992), and Boggs et al. (2013), who reported that percent. In addition, except Kane County, all counties with vandalism was more frequent in rural canyon areas. Since vandalism rate less than one percent have median the definition of areas is mostly corresponded to population household incomes higher than 45,000 dollars. density, the association between population density and number of vandalized signs is strongly evident. 3.1. Population density 3.2. Ethnicity By considering the counties' population density, the associa- tion between the vandalism rate and population can be This section examines whether or not traffic sign vandalism determined. To do this, population and area data were ob- rates in Utah correspond to a specific community in Utah. tained from the 2010 United States Census Bureau data. The Previously, a number of studies found that race plays a role in effects of counties' population density (people per km2) on the the road user behavior, probably due to the impact on drinking vandalism rate are summarized in Table 3. As seen in Table 3, (Harper et al., 2000; Romano et al., 2006). Almost 20% of the the more densely populated counties were less likely to people living in Utah are minorities, including Hispanics, experience sign vandalism. According to the results of the Blacks, Indians, Asians, and various other races (Perlich, 2011). chi-square test, there is strong evidence of an association Table 5 demonstrates the percentage of sign vandalism with between the vandalism rate and population density. The respect to the majority ethnic group (Caucasians). According trend test illustrated that this association could be linear. to the results, there is no evidence of a strong association Thus, in densely populated counties, the vandalism rate will between vandalism rate and ethnicity. The authors excluded decrease. trend test results since it was not statistically significant. To enable more in-depth analysis, the authors used the Thus, race does not play a significant role in traffic sign Geographic Information Database of Utah Automated vandalism in Utah. Geographic Reference Center website (2008). After employing ArcGIS, traffic signs were categorized into two groups: urban 3.3. Age versus rural. Urban areas represent densely developed territory, encompassing residential, commercial, and other Previously, a study addressed the effects of road users' age on nonresidential urban land uses. In general, this territory the understanding of traffic signs (Ng and Chan, 2008). consists of areas of high population density and urban land use resulting in a representation of the urban footprint. Table 8 e Vandalism rates by education level. Percentage of adults Number of Vandalism Vandalism Table 6 e Vandalism rates by median age. (25e64) with at signs rate (%) Yes No Median age Number of Vandalism Vandalism least an associate (years) signs rate (%) degree (%) Yes No <30 17,423 407 17,016 2.34 <29 16,835 267 16,568 1.59 30e32 18,654 333 18,321 1.79 29e31 43,717 576 43,141 1.32 32e34 17,297 176 17,121 1.02 31e34 18,821 317 18,504 1.68 34e42 21,749 360 21,389 1.66 >34 17,941 340 17,601 1.90 >42 22,191 224 21,967 1.01 Chi-square test statistic ¼ 31.97 (p-value < 0.0001). Chi-square test statistic ¼ 154.30 (p-value < 0.0001). Trend test statistic ¼ 3.90 (p-value < 0.0001). Trend test statistic ¼9.76 (p-value < 0.0001). journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202 199

Fig. 4 e Utah's sign vandalism rate vs. demographics by county. (a) Population density. (b) Median age. (c) Household income. (d) Education level.

However, research that assesses the association between age Utah's counties is narrow, both chi-square and trend test and vandalism rates is not well-developed. To fill the gap, this values are statistically significant. study derived the median age data for each county from the U.S. Census (Governor's Office of Planning and Budget, 2010). 3.4. Income Utah has a median age of 29.2 years old, which is less than the national median age of 37.6 years. Table 6 summarizes There have been studies that evaluated the correlation be- the effects of each county's median age on its vandalism tween income and driving safety in the past (Traynor, 2008). In rate. Although the range of variability of median age in order to examine the association between income and 200 journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

vandalism rate, this study uses the Utah median household Table 9 e Regression models of sign vandalism rate. income levels collected in the American Community Survey (ACS). Generally, 10.8% of people living in Utah are Variable All variables Significant variables considered to be living in poverty. “In the U.S., poverty Estimate p-value Estimate p-value status is determined by comparing annual income to a set of Intercept 6.56 0.168 5.16* <0.001 dollar values called poverty thresholds that vary by family Population 0.000848 0.742 ee size, number of children and age of householder” (United density States Census Bureau, 2010b). For this study, income data Ethnicity 0.00396 0.885 ee ee between 2006 and 2010 were collected. Recorded income Median age 0.0354 0.516 Income 0.0000431 0.154 0.000066* <0.001 levels include “the income of the householder and all other Associate 0.0478 0.173 ee individuals 15 years old and over in the household, whether degree ” they are related to the householder or not (United States Gender 2.41 0.582 ee Census Bureau, 2010a). A summary of the vandalism rates F-statistic 2.006 0.110 10.3* <0.001 based on income is presented in Table 7. The result of the R2 0.3536 0.2761 chi-square test shows evidence of a very strong association Note: * means p-value <0.05, statistically significant at level of 0.05. between the vandalism rate and median income. Based on the trend test, their association is linear. In other words, there is statistically evidence that higher-income counties agreed for the models developed by income and education are less likely to experience traffic sign vandalism. variables. Thus, the linear association between sign vandalism rate and these variables is evident. In other 3.5. Education words, the plots suggest that only income and education level are good predictors of sign vandalism in the context of Education level is another factor that may affect the the data. vandalism rate. According to the 2011 census data, by focusing After considering the scatter plot of predictors, a majority ' e on Utah s working-age adults (25 64 years old), 40.3% of of variables display a high correlation with other predictors. people across the state hold at least an associate degree After creating a correlation matrix, a high correlation between (Matthews, 2010). The effects of education level on the predictors was discovered. Thus, it was necessary to remove vandalism rate are summarized in Table 8, wherein counties highly correlated variables to eliminate confounding from the with a higher percentage of educated people are less likely fitted model. The model development began by considering all to experience sign vandalism. According to the results of the predictors. Table 9 shows the fitted model, which shows that chi-square test, there is evidence of a strong association no predictor is statistically significant. By taking into between the vandalism rate and education level. Trend tests consideration the backward elimination procedure, illustrate that there is statistical evidence of a linear insignificant variables were dropped from the model one association. Based on the trend test results, by increasing after another. In other words, the variables without enough the percentage of people with at least an associate degree, statistically significant value were eliminated from the the rate of vandalism will be decreased. This finding was model. Based on the t-test, final variables were significant at also reported by Lewis, who stated that education plays a a level of 5%. As seen in Table 9, income is the only included vital role in effectively addressing the sign vandalism issue factor in the final model. Upon completing the linear (Lewis, 1998). regression analysis, by increasing the percentage of people who have families with a higher income in the county, the 3.6. Gender vandalism rate will decrease. With just one explanatory variable, the final model yields a good value of R2, 0.276. In ' In order to investigate if a county s gender ratio influences the addition, the F-test, for lack of fit, is equal to 10.3 for a p- traffic sign vandalism rate, gender composition data from the value less than 0.001, which shows the developed model 2010 census data were analyzed. The ratio of males to females provides a good fit. in Utah is 1.009, which is higher than the national gender ratio (0.967) (Howden and Meyer, 2010). However, drawing conclusions based on Utah's gender composition is not reliable, due to the uniformity of counties' gender ratio, with 4. Conclusions the exception of Daggett County, which holds a ratio of 1.29. In general, the gender ratio ranges from 0.977 to 1.098 in Utah. Regardless of the form, traffic sign vandalism has been a serious problem for transportation agencies. Vandalized 3.7. Regression model traffic signs can lead to safety issues, since the overall legi- bility of the sign is affected by vandalism during both day and To provide more robust evidence for the association between nighttime conditions. In addition, vandalism results in the vandalism rate and demographics of Utah's local popula- increased cost to replace, repair, or maintain damaged signs. tion, the authors also developed linear and polynomial This study makes an effort to find an answer for the question: regression models using R software (R Development Core “How is the sign vandalism rate affected by the local de- Team, 2014). From the fitted models for response variable, mographics?” To examine this proposal, the demographics of vandalism rate, linear and smoother models are virtually each Utah county were derived from U.S. Census Bureau data. journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202 201

In addition, the study uses mobile-based sign data collected Boggs, W., Heaslip, K., Louisell, C., 2013. Analysis of sign damage across the State of Utah. The chi-square and trend tests apply and failure: Utah case study. Transportation Research Record e to analyze the association between the two categorical 2337, 83 89. Brimley, B., Carlson, P.J., 2013. The current state of research on variables. the long-term deterioration of traffic signs. In: The initial analysis of the data showed that almost 7% of Transportation Research Board 92nd Annual Meeting, 97,314 measured signs were damaged, of which at least 22% Washington DC, 2013. of the damages were intentionally caused by humans. The Carlson, P.J., Lupes, M.S., 2007. Methods for Maintaining Traffic data analysis shows that there are strong associations be- Sign Retroreflectivity. FHWA-HRT-08-026. Texas Transpor- tween the sign vandalism rate and median household in- tation Institute, College Station. come, completion of at least an associate degree, and Chadda, H., Carter, E., 1983. Sign vandalism: time for action-now. ITE Journal 53 (8), 16e19. population density. Furthermore, the results trend test re- Drory, A., Shinar, D., 1982. The effects of roadway environment sults demonstrate that, in more densely populated counties, and fatigue on sign perception. Journal of Safety Research 13 the vandalism rate is decreased. Also, higher-income (1), 25e32. counties are less likely to experience traffic sign vandalism, Ellis, R., Houten, R., Kim, J.L., 2007. In-roadway “yield to and by increasing the percentage of people with at least an pedestrians” signs: placement distance and motorist e associate degree can reduce the vandalism rate. Drawing yielding. Transportation Research Record 2002, 84 89. Ellison, J.W., 1996. “Stop and think”: a campaign against traffic conclusions based on gender composition, age, and ethnicity vandalism. In: Compendium of Technical Papers for the 66th was not reliable. The majority of Utah counties have a uni- ITE Annual Meeting, Minneapolis, 1996. form gender ratio, and Caucasians make up the majority Ellsworth, P., 2013. Utah DOT leveraging LiDAR for leap to asset racial group in most Utah counties. In addition, it is observed management. LiDAR Art of the Scan 3 (1), 38e43. that the range of variability of median age in Utah's counties Evans, T., Heaslip, K., Boggs, W., et al., 2012a. Assessment of sign is also narrow. These limitations may contribute to the re- retroreflectivity compliance for development of a sults of no overall impact of these variables on sign management plan. Transportation Research Record 2272, 103e112. vandalism rate in Utah. Evans, T., Heaslip, K., Louisell, W., et al., 2012b. Development of a After examining the correlation between variables, it is sign asset management plan for retoreflectivity compliance. determined that the majority of predictors are highly corre- In: Transportation Research Board 91st Annual Meeting, lated. By removing confounding variables from the regression Washington DC, 2012. model, median household income emerges as the only sig- Governor's Office Planning and Budget, 2010. Age Distribution in nificant predictor included in the final model. Therefore, the Utah. http://governor.utah.gov/DEA/Census/2010/AgeDistrib- strong association between the vandalism rate and median ution.pdf (accessed 01.06.14.). Harper, J.S., Marine, W.M., Garrett, C.J., et al., 2000. Motor vehicle income of counties proves reliable. Due to a lack of detailed crash fatalities: a comparison of and non-Hispanic information about traffic sign vandals, the findings of this motorists in . Annals of Emergency Medicine 36 (6), study will be beneficial to help agencies identifying areas with 89e596. a higher likelihood of sign vandalism. In this way, agencies Harris, E.A., Rasdorf, W., Hummer, J.E., 2009. A control sign may consider various countermeasures in the areas that are facility design to meet the new FHWA minimum sign & more vulnerable to sign vandalism. The next step is to retroreflectivity standards. Public Works Management Policy 14 (2), 174e194. establish a cost-benefit analysis to assess the cost of con- Harris, G., 1992. Engineering Study for Reducing Sign Vandalism. ducting countermeasures versus the cost of sign vandalism Project HR-246. Department of Transportation, Ames. itself. Howden, L.M., Meyer, J.A., 2010. Age and Sex Composition: 2010. U.S. Census Bureau, Washington DC. Hugh, W., McGee, P.E., 2010. Maintenance of Signs and Sign Supports: a Guide for Local Highway and Street Maintenance Acknowledgments Personnel. FHWA-SA-09e025. Federal Highway Administration, Washington DC. We would like to convey our gratitude to UDOT for sponsoring Jalayer,M.,Zhou,H.,Gong,J.,etal.,2014.Acomprehensive assessment of highway inventory data collection data collection, and to Mandli Communications, Inc., for col- methods. Journal of the Transportation Research Forum lecting data. We would also like to express our thanks to 53 (2), 73e92. Wesley Boggs, Travis Evans, and Kevin Gardiner, who took Khalilikhah, M., Heaslip, K., 2016a. Important environmental photos of traffic signs in the field. factors contributing to the temporary obstruction of the sign messages. In: Transportation Research Board 95th Annual Meeting, Washington DC, 2016. references Khalilikhah, M., Heaslip, K., 2016b. GIS-based study of impacts of air pollutants on traffic sign deterioration. In: Transportation Research Board 95th Annual Meeting, Washington DC, 2016. Khalilikhah, M., Heaslip, K., Louisell, C., 2015a. Analysis of the Agresti, A., 2007. An Introduction to Categorical Data Analysis, effects of coarse particulate matter (PM10) on traffic sign second ed. Wiley-Interscience, New York. retroreflectivity. In: Transportation Research Board 94th Baratian-Ghorghi, F., Zhou, H., Wasilefsky, I., 2015. Effect of red- Annual Meeting, Washington DC, 2015. light cameras on capacity of signalized intersections. Journal Khalilikhah, M., Heaslip, K., Song, Z., 2015b. Can daytime digital of Transportation Engineering 142 (1), 04015035. imaging be used for traffic sign retroreflectivity compliance? Black, K.L., Hussain, S.F., Paniati, J.F., 1992. Deterioration of Measurement 75, 147e160. retroreflective traffic signs. ITE Journal 62 (7), 16e22. 202 journal of traffic and transportation engineering (english edition) 2016; 3 (3): 192e202

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