Optimum Size Solution for Roadside Structures Based on FAHP Evaluation

Xia Zhao1; Zhonghua Wei2; Zhixia Li3; Wei Wang4; and Tongyang Zhang5 1Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100, Pingleyuan District, Chaoyang District, Beijing 100124, China. E-mail: xiabjut@163.com 2Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100, Pingleyuan District, Chaoyang District, Beijing 100124, China. E-mail: weizhonghua@bjut.edu.cn 3Traffic Operations and Safety Laboratory, Department of Civil and Environment Engineering, University of Wisconsin-Madison, 1249A Engineering Hall, 1415 Engineering Drive, Madison WI 53706. E-mail: zli262@wisc.edu 4Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100, Pingleyuan District, Chaoyang District, Beijing 100124, China. E-mail: wangwei2011@bjut.edu.cn 5Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100, Pingleyuan District, Chaoyang District, Beijing 100124, China. E-mail: zty0905@163.com Abstract

Roadside landscape can be for a restorative function on ecology or for appreciation, but it may also bring clutter and traffic accidents. A literature review has identified that a lack of size regulation on roadside structures is the direct reason for this phenomenon. This paper aimed to investigate the optimum size solution. A Fuzzy Analytic Hierarchy Process method was used to evaluate the multi-criteria decision making problem. Evaluation indices focused on harmony with the environment, conspicuity, excitement, or depression degrees to viewers. Ten roadside structures in varied sizes formed the evaluation alternatives set. Experts gave their perceptual judgments on the alternative sets. The results revealed that roadside structures that are 25 m –49 m have an excellent visual quality in the evaluation process and were considered to be the optimum size for roadside structures. The results could provide a theoretical basis for flexible landscape design in transportation corridors in China.

Keywords: Landscape; Roadside safety; FAHP; Optimum size solution; MCDM. INTRODUCTION

In recent decades, Americans travel approximately 61,000 miles per day on urban freeways and highways, with the ever-increasing experiences of roadside landscapes (United States Department of Transportation, 1998). Roadside

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landscape can have a restorative function on ecology (e.g., manage stormwater, enhance air quality, serve as wildlife habitat), traffic security (e.g., reduce crashes, increase traffic calming), and scenic beauty (e.g. provide visual amenities) during the complex driving process (Amekudzi, 2013). Its aesthetical function has been broadened during the last several decades. Several studies have examined the relative safety effects associated with aesthetic landscape treatments along the road (Dumbaugh, 2006). For example, Naderi (2003) examined the safety effects of aesthetic streetscape improvements along five arterial roadways in downtown Toronto. The results showed that roadside trees significantly decreased midblock crashes along pilot spots. Forment (2006) indicated that ecological management along highway corridors had a role in improving natural values and aesthetic appreciation.

Paradoxically, some studies object to the construction of roadside landscapes, especially after the emergence of artificial landscapes, such as single trees, poles, sculptures, ads, or other discrete facilities. Intrusive landscape can increase the level of visual variety, combined with a lack of size regulation on its structures. It increases landscape clutter and lowers aesthetic appreciation. This apparent cluttering is associated with a general lack of concern for size regulation on a roadway corridor. Moreover, trees induce undue risk to errant motorists. Trees account for more than 8 percent of traffic-related fatalities (Neuman, 2003). In 2009, trees accounted for 2,697 fatalities out of a total 10,555 fixed-object-collision fatalities (NHTSA, 2009).

Which side is right? Should we continue to enjoy the ecological, aesthetical functions brought by roadside landscapes, or should we remove them to avoid run-off-road accidents? Perhaps there is a lack of size regulation on roadway structures that causes these two contradictory judgments. AASHTO (2002) provided ideas, options, and examples to design more environmentally-friendly highway landscapes without compromising traffic safety and mobility. They stressed the importance of early public participation to achieve community-friendly highway landscape design. With this basic principle in mind, this paper aimed to investigate the optimum size solution of discrete structures along roads, including landscapes.

This is a typical multi-criteria decision making (MCDM) problem, featured with multi-attributes and uncertainty. It is also homogeneous to conduct a landscape quality assessment, with the aim to research road user perceptions and preferences for specified landscapes. Thus, this paper employed the landscape quality assessment theory and MCDM approaches to determine the best size solution from all feasible alternatives. The paper was conducted with three specific procedures:

• Establish 3D-visualization driving scenarios, with artificial structures in varied sizes.

• Conduct an interactive size assessment on these structures. • Identify the optimum size solution for artificial structures along the road.

LITERATURE REVIEW

In both practice and research, landscape quality is indicated by the ratings of visual aesthetic quality or human observer expressions of preference. Studies have been conducted since the 1960s to research road user perceptions and preferences for specified landscapes. It is an active research field in environmental perception research, and gradually became dominant in the 21st century. Daniel

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(2001) indicated that the assessment indices had been taken to encompass everything from basic utilitarian, spiritual needs to intrinsic natural values. With a substantial knowledge base, the evaluation process has become more technical and quantitative.

A visual landscape quality evaluation can be seen as a contest between expert approaches and public perception-based approaches, two main approaches for landscape quality assessment. The expert approach translates the landscape into formal design parameters on economic, legal, and biological fields of roadway environmental management. The evaluation processes favor the concerns on landscape scenic quality, such as harmony, unity, vividness, diversity, and ecology features. For instance, Gobster (1999) indicated that landscape quality assessment should be ecological and aesthetic-based. Yuan (2012) developed an evaluation procedure for a two-lane undivided greenway. The main evaluation indices were soil stability in slope embankment areas and environmental ecologic protection capability in roadside areas. Dramstad (2006) showed that a significant correlation existed between the map-derived indicators of landscape structure and visual preferences. The indicators were on spatial metrics, such as number of land types, number of patches, and land type diversity. The author also highlighted that different groups of people may interpret different values for landscape indicators.

The perception-based approach shows the concerns of landscape viewers and focuses on their sensory-perceptual response to the landscape. This approach is derived from psychophysical tradition. Quantitative indices based on human perception are used as a gauge to the properties of objects. Various psychological scaling methods associated with visual quality are applied to obtain the quantitative measures of perceived landscape visual quality. The main evaluation criteria are the sensory concerns of road users, such as their cognition and behavior, comfort, and driving fatigue. For instance, empirical studies addressed that driving stress could be a criterion to evaluate the subjects’ scene-viewing process. Li (2010) combined the expert principle with the human-perception one and selected indices on aesthetics (e.g., vividness, variety, naturalness) and perceptual response (e.g., travel security or comfort) to evaluate landscape quality. A model of highway landscape was constructed based on these selected indices. A typical case study proved the feasibility and reliability of the index system and the satisfaction model.

METHODOLOGY FAHP method

To obtain the optimum size solution from a set of feasible alternative structures, an MCDM theory was used in this paper. The method can quantize the decision maker’s preferences by ranking the importance/weights for the instances of an attribute.

One of the most outstanding MCDM approaches is the Analytic Hierarchy Process (AHP). It has been widely used in multi-criteria decision making and has been applied successfully in many practical decision making problems. It assigns a crisp or absolute weight value to a pair-wise comparison judgment in the evaluation process. Yet, this cannot handle uncertain and vague decision making problems, such as the human decision process (Saaty, 1978). To address this issue, fuzzy logic theory is incorporated into AHP. The adaptive method takes uncertainty and subjectivity into account to assess the criteria importance. Thus,

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the Fuzzy Analytic Hierarchy Process (FAHP) method is used in this paper to determine the final weights of alternatives.

Research framework

The FAHP contains three stages of work procedures in the decision process. Stage 1: Establish the hierarchy and define the criteria. First, an evaluation hierarchy structure is established. Generally, a typical evaluation hierarchy contains three essential levels, respectively, for the goal, criteria, and alternative, as depicted in Figure 1. The goal level demonstrates the final objective of the whole hierarchical structure. The criterion and sub-criterion levels define the relative weight performance of each criterion. The alternative level describes the alternatives to be evaluated and prioritized in this hierarchy.

Goal

Criterion 1 Criterion n

Sub-criterion n Sub-criterion 1 Sub-criterion nSub-criterion 1

Alternative 2 Alternative nAlternative 1

Goal Level

Criterion Level

Sub-criterion level

Alternative Level

Figure 1. Typical evaluation hierarchy

Stage 2: Develop a fuzzy judgment matrix. A fuzzy judgment matrix is developed by pair-wise comparison (Saaty, 1978). Several definitions of fuzzy number, membership function, and TFN are discussed first.

A fuzzy number A is any fuzzy subset of real numbers R, following the membership function μ( ), and μ( ) is mapped to the closed interval [0, ω] (0 ≤ ω ≤ 1) and can be described by the following distribution in Equation (1):

( )( , , , , ) = 0; ≤ ; < ≤1; 0; < ≤< ≤

(1) Where, a, b, c, d are all real numbers ( < < < ) and A is

assumed to be convex with its bound(−∞ < , < +∞). When ( ) follows a trapezoidal distribution, it can be described by the

following distribution in Equation (2):

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( )( , , , , ) = 0; ≤( − )/( − ); < ≤1;( − )/( − )0; ; < ≤< ≤< (2)

A triangular fuzzy number (TFN) is denoted by M. If a fuzzy number’s membership function ( )( ) ( ∈ [0,1]) follows the distribution in Equation (3), it is known as a TFN [26]:

( )( ) = ( − )/( − ); ∈ ( , )( − )/( − ); ∈ ( , )0;

(3) Where, and , respectively, represent the lower and upper bounds of

the fuzzy number , and m is the mean value of the lower and upper bounds. TFN is associated with a 1–9 level scale to determine the significance of

each criterion by pair-wise comparisons. The scale is classified into five levels. Each level is described as a TFN, that is, Equally important (0, 1, 3), Weakly important (1, 3, 5), Fairly important (3, 5, 7), Strongly important (5, 7, 9), and Extremely important (7, 9, 11). A geometrical explanation of the linguistic variables is shown in Figure 2. The range of the linguistic variables can demonstrate user perception and preference precisely.

Figure 2. Linguistic variables for criteria weights

Stage 3: Calculate the fuzzy weights of each criterion. This stage contains four steps listed below.

• Step 1: Calculate the value of fuzzy synthetic extent with respect to the ith object. The value of the fuzzy synthetic extent with respect to the ith object is defined in Equation (4).

= (∑ )/(∑ ∑ ) (4) The equations to calculate ∑ and ∑ ∑ are represented

by Equations (5) and (6). ∑ = ∑ ∑ ∑ (5) ∑ ∑ = (∑ ∑ ∑ ) (6)

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• Step 2: Compare the degree of possibilities of two TFNs. The degree of possibility of = ( , , ) ≥ = ( , , ) is defined as ≥ = [min ( ( ), ( ))] and can be equivalently expressed in Equation (7):

≥ = ℎ ∩ = ( ) = 1; ≥0; ≥( − )/[( − ) − ( − )]; ℎ (7)

Where, is the ordinate of the highest intersection point between and . To compare and , values of both ≥ and ≥ are required. • Step 3: Perform the consistency test. The consistency of the

judgment matrix is performed and verified. C.I. is the consistency index, and C.R. is the consistency ratio. If . < 0.10, the estimation is accepted, and the corresponding eigen-vector W related to can be used to sort weigh vector; otherwise, a new comparison matrix is solicited. These two parameters are calculated in Equation (8) and (9).

. = .. (8) . = ( − )/( − 1) (9)

For each size of matrix, random matrices are generated, and their mean of

C.I. value (random index, R.I.) can be computed, as illustrated in Table 1.

Table 1. Random Index with Respect to n n 1 2 3 4 5 6 7

R.I. 0 0 0.58 0.90 1.12 1.24 1.32

• Step 4: Calculate the possibility degree of a convex fuzzy number. If the degree of possibility of a convex fuzzy number is greater than k convex fuzzy numbers ( = 1, 2, ⋯ , ), then it can be defined by Equation (10).

(M ≥ , , ⋯ , ) = (M ≥ ), (M ≥ ), ⋯ , M ≥ = M ≥ (10)

Assuming that ( ) = (S ≥ S ) for ( = 1, 2, ⋯ , ). The weight vector is represented by Equation (11):

= ( ( ) ( ) ⋯ , ( )) (11) Where, ( = 1, 2, ⋯ , ) are n elements.

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CASE STUDY Surveys show that the advanced 3D virtual-reality technique is now an

affordable approach to collect observer real-time response to the environment interactively. Responses to a high-fidelity simulated scenarios correlate highly with on-site responses. A simulator is known for its low cost to conduct evaluation analysis. The external interference elements can be eliminated simply by modifying the simulation parameter. Thus, the evaluation environment of this case study was designed to be driving simulator-based, with a controllable virtual environmental landscape modeled. Ten cuboids, with sizes m3 ( = 1, 2, ⋯ , 10), formed the evaluation alternative set. Ten experts were recruited to have a simulated drive and gave their perceptual judgment on the alternative artificial structures while traveling along the road. Data were then collected and analyzed by the FAHP method. The goal was to evaluate the visual quality of roadway structures in different sizes and select the optimum size with the best visual quality.

Evaluation criteria

This paper combined the evaluation criteria commonly used in the expert approach and public perception-based approach. Four corresponding criteria were generated, denoted by ( = 1, 2, … , 4).

• A1 was the roadside structure’s harmony with the environment. This criterion judged whether it was in harmony with the environment in color, texture, size, or contrast ratio.

• A2 was the conspicuity of the roadside structure to be recognized easily by its outline.

• A3 was the comprehensive excitement degree to viewers brought by the roadside structure. This criterion judged whether it made the viewers be full of spirit or relieved their driving fatigue.

• A4 was the comprehensive depress degree to viewers brought by the roadside structure. This criterion judged the degree to which it made the viewers be depressed, fearful, anxious, or uncomfortable.

Virtual environment The 3Dsmax and a driving simulator (AutoSim AS160) were used to

establish the simulated scenarios. A set of parameters was regulated on the road and roadside structures in this study.

The simulation scenarios were based on a 25 km-long corridor of the Jingjin highway, which is started from the 4th east ring of Beijing, China. It is a six-lane state highway containing a side slope with typical grassland covered on the topography (as shown in Figure 3). The simulated roadway was modeled as a six-lane freeway with lane width of 3.75 meters and a clear zone of 9 meters. Motorist drove on the right lane close to the slope to avoid disturbance from large vehicles. The simulated slope was textured with vegetation to have a more natural appearance. The slope gradient was set to 30 degrees. The driving speed was limited to 120km/h.

Due to the high-traveling speed, all the roadside objects were identified by spatial contours, featuring a three-dimensional cuboid. In this paper, the roadside structure was substituted by an artificial cuboid textured with vegetation. Due to cost limitations in practice, the maximum size of the roadside structure was 100 m3. Therefore, 10 cuboids with sizes m3 ( = 1, 2, ⋯ , 10) were

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mounted on the central slope randomly in 2-km intervals. According to the color wheel theory, a strong lightness contrast occurs when colors exactly opposite one another are combined. Based on this theory, the color of the cuboids were set to red and purple to bring maximum contrast to the green slope to enhance the conspicuity of the cuboids.

(a) (b)

Figure 3. Tester driving in simulator (a) viewing cuboid along road (b) Participants

Ten professional experts in the landscape architecture or traffic engineering fields who grasped the FAHP method well were hired to conduct this evaluation test. Four were females. The mean age of the experts was 42.8 years, with average 10-years’ driving experience. All experts were sober with no caffeine or nicotine before the experiment. All had corrected vision.

Procedure

Before the formal experiment, a participant had a 15-minute warm-up period to get familiar with the scenario and rules. The rules on the scoring approach and the pair-wise comparison for criteria importance were explained to the experts. The scoring approach for the object’s visual quality was classified to three levels based on the grade range from 0 to 100 scores: unqualified level (below 60), qualified level (61– 84), and excellent level (above 85). The pair-wise comparison was based on the TFN theory. Every expert was asked to go through the evaluation criteria and assign a range of the linguistic variables to it in the form of TFN. These two steps were both fulfilled in the simulator driving process. A personal driving experience in the simulator would ensure making a reliable evaluation to the visual quality of the object.

RESULTS Descriptive analysis

The evaluation process for the 1st cuboid in size 1 m3 is analyzed step-by-step to explain the specific deduction process. Step 1: Calculate the value of fuzzy synthetic extent with respect to the ith object. The evaluation results were used to calculate the value of fuzzy synthetic extent with respect to the 1st object. The pair-wise comparison values from the tests were transformed into TFNs. The fuzzy judgment matrix W is shown in Equation (12).

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= 12341 2 3 4 (1, 1, 1)(8, 9, 10)(6, 7, 8)(6, 7, 8)

(1/10, 1/9, 1/8)(1,1,1)(1, 3, 5)(0, 0, 1)(1/8,1/7, 1/6)(1/5, 1/3, 1)(1,1,1)(1, 3, 5)

(1/8,1/7, 1/6)(1/5, 1/3, 1)(1/5, 1/3, 1)(1,1,1) (12)

Step 2: Compare the degree of possibilities of two TFNs. The fuzzy judgment matrix W was divided into three matrices according to the upper, lower, and median value of each object. The lower-bound matrix (W ) was formed by choosing the lower bound value from the fuzzy judgment matrix W, shown in Equation (13). It was selected equivalently to express the other matrices based on the degree of possibilities of these three matrices.

= 18661/10110

1/81/5111/81/51/51 (13)

Step 3: Perform the consistency test. By calculating C.I. and C.R. based on Equations (8) and (9), the consistency of the judgment matrix W was performed and verified. The value of C.R. was 0.0003<0.10, so the matrix was consistent. The weights for A1, A2, A3, and A4 were calculated to be 0.1755, 0.3072, 0.3279, and 0.1894, respectively, as shown in Table 2. Step 4: Calculate the possibility degree of a convex fuzzy number. The average score given to the 1st object with respect to the criteria was 40. This was multiplied by the weights of each criterion to calculate the final synthetic score, labeled as Score in Equation (14). Similarly, the synthetic score of the ith

object (i=2, 3… 10 ;) was calculated, with final scores for all cuboids summarized in Table 2. Score = ∑ × (14)

Where, Scorei is the synthetic score of the ith object, i=1, 2,…,10; is

the average weight of the jth criterion of the ith object; is the respective score of

the ith criterion of the ith object; nc is the number of criteria of the ith object.

Optimal size recommendation

The score distribution with respect to the cuboid ID is shown in Figure 4. It indicated that three cuboids, with ID numbers 5, 6, 7, scored relatively higher than the other cuboids. They were in sizes 52 m , 62 m , and 72 m , with scores all above 85. Based on this finding, it was concluded that a roadside structure of sizes 25 m –49 m were the optimum size solution for roadside structures, which had excellent visual quality in the evaluation process.

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Table 2. Final Score of ith Object

Cuboid ID

Size (m3)

Criterion ID

Criterion weights

Object score Score

1 12

A1 0.1755 40

40 A2 0.3072 40 A3 0.3279 40 A4 0.1894 40

2 22

A1 0.2755 50

53.8 A2 0.32 60 A3 0.2122 65 A4 0.1171 60

3 32

A1 0.1425 70

65.9 A2 0.4858 50 A3 0.2941 65 A4 0.1926 65

4 42

A1 0.1531 75

75.6 A2 0.3059 80 A3 0.359 80 A4 0.156 70

5 52

A1 0.2531 85

88.0 A2 0.3059 80 A3 0.359 80 A4 0.156 85

6 62

A1 0.1625 80

89.5 A2 0.3858 90 A3 0.3594 85 A4 0.1326 85

7 72

A1 0.1825 95

93.9 A2 0.3858 90 A3 0.3394 90 A4 0.1326 85

8 82

A1 0.1325 75

72 A2 0.3658 80 A3 0.2951 75 A4 0.1526 70

9 92

A1 0.1325 55

52.7 A2 0.2858 60 A3 0.3651 55 A4 0.1366 60

10 102

A1 0.1525 45

34.2 A2 0.2575 40 A3 0.2651 45 A4 0.1286 40

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Figure 4. Score distribution with respect to cuboid ID

DISCUSSION AND CONCLUSION Discussion

Several limitations of this research must be raised as follows. First, this paper confined the evaluation object to the discrete structure along the road. It was substituted by an artificial cuboid textured with vegetation in the 3D-visualization scenario. A further evaluation should be considered on consecutive landscape groups and stripes. Second, this paper combined the evaluation criteria in the expert approach and public perception-based approach to evaluate the importance of each criterion via pair-wise comparison. The ecological indices should be considered in further research. Finally, future research should be conducted to verify the proposed size solution under real roadway scenarios. We will seek more cooperation and support from the Chinese government to execute the on-road test.

Conclusion

This paper proposed a theoretical optimum size solution for roadside structures, especially for discrete landscapes along the road. It indicated that artificial structures could be built along the road with a reasonable size solution. This paper does not favor landscape design without considering run-off-road accidents or landscape clutter caused by unregulated sizes, nor does it favor tree removal ideas without considering the ecological/aesthetic function of the landscape. A compromise solution on size design was proposed for roadside structures, including discrete landscape. This was intended to be a more environmentally-friendly design method without compromising safety and mobility benefits. The results could provide a theoretical basis for flexible landscape design in transportation corridors in China. The project also demonstrated the feasibility to elicit the evaluation process by 3D visualization techniques.

ACKNOWLEDGEMENT

This work is supported by National Natural Science Foundation of China (Grant No.: 51208008), the National Key Basic Research Program of China (Grant No.: SN: 2012CB723303), and RiXinRenCai program of Beijing University of Technology. Their supports are gratefully acknowledged.

40.00 53.80

65.90 75.60

88.00 89.50 93.90

72.00

52.70

34.20

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Scor

e

Cuboid ID

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REFERENCES AASHTO (2002). “Road Side Design Guide.” Washington DC. Amekudzi, A.A., Smith, M.K., Brodie, S.R., et al. (2013). “Impact of

environmental justice on transportation.” Journal of Transportation Research Record, 2320, 1-9.

Daniel, T.C. (2001). “Whither scenic beauty? Visual landscape quality assessment in the 21st century.” Landscape and Urban Planning, 50, 267-281.

Dramstad, W.E., Sundli, M.T., and Fjellstad, W.J. (2006). “Relationships between visual landscape preferences and map-based indicators of landscape structure.” Landscape and Urban Planning, 78: 4, 65-474.

Dumbaugh, E. (2006). “The design of safe urban roadsides—An empirical analysis in design. CD-ROM.” Transportation Research Board of the National Academies, Washington, DC.

Froment, J., and Domon, G. (2006). “Viewer appreciation of roadway landscapes. The contribution of ecologically managed embankments in Quebec, Canada.” Landscape and Urban Planning, 78: 1-2, 14-32.

Gobster, P.H. (1999). “An ecological aesthetic for forest landscape management.” Landscape, 18: 1, 54-64.

Lechtenberg, Karla A. Schrum, Kevin, Stolle C. Steven, et al. (2013). “Cost-effective safety treatment of trees on low-volume roads in design. CD-ROM.” Transportation Research Board of the National Academies, Washington, DC.

Li, H., Zhang, X., Duan, Y. et al. (2010). “A model for highway landscape evaluation.” ICCTP, Integrated Transportation Systems, 2695-2703.

Li, Y., and Lu, Jian. (2012). “Evaluation approach for two-lane undivided highway green landscape. In environment and energy, CD-ROM.” Transportation Research Board of the National Academies, Washington, DC.

Naderi, J.R. (2003). “Landscape design in the clear zone. The effects of landscape variables on pedestrian health and driver safety in design. CD-ROM.” Transportation Research Board of the National Academies, Washington, D C.

National Highway Transportation Safety Administration (NHTSA). (2009). Traffic Safety Facts, Early Edition. U.S. Department of Transportation, Washington, DC.

Neuman, T.R., Pfefer, R., Slack, K.L., et al. (2003). “A guide for addressing collisions with trees in hazardous locations, guidance for implementation of the AASHTO Strategic Highway Safety Plan, National Cooperative Highway Research Program (NCHRP) Report.” Transportation Research Board, 3, 500.

Saaty, T.L. (1978). “Modeling unstructured decision problems-the theory of analytical hierarchies.” Math comput Simulation, 20, 147-158.

United States Department of Transportation. (1998). Federal Highway Administration (FHWA) Highway Statistics.

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