Performance Appraisal of Urban Street-Lighting System: Drivers’ Opinion-Based Fuzzy Synthetic Evaluation

Abstract

Saudi Arabian urban roads and highways have witnessed a large number of traffic crashes. Road lighting is one of the most important factors influencing drivers’ safety during the nighttime. Street-lighting design (e.g., spacing and height), visibility (e.g., lane marking and oncoming vehicles), and drivers’ satisfaction (e.g., glare effect on eyes and overall ambiance) are primary criteria affecting the performance of an urban street-lighting system (USLS). The present study presents a methodology for the performance appraisal of USLS in Qassim, Saudi Arabia. An online questionnaire survey was developed to obtain drivers’ opinions on nine sub-criteria (three under each primary criterion). The responses were translated into a five-scale subjective rating system from very low to very high. Fuzzy synthetic evaluation (FSE) effectively aggregated the statistically diverse (p-value < 0.001) responses obtained on the three primary criteria. The study found that electronic billboards’ positioning, oncoming vehicle lights, and poor lighting in the course of bad weather (mainly dust) are mainly affecting the performance of USLS in the view of road users. The performance levels ranged between “medium” and “high”, with no criteria or sub-criteria achieving a “very high” level, suggesting a need for upgrades, such as conversion to LED lights and smart lighting control systems. The proposed methodology benefits the transportation ministries to identify lacking components of USLSs in different regions of Saudi Arabia. The methodology provides the opportunity to include additional or site-specific factors for appraising the performance of USLS before (during planning and design) or after the implementation of improvement actions.

1. Introduction

Road crashes are one of the leading causes of death worldwide [1]. Crashes are more frequent and severe in the dark (or dim light) than in the daytime [2]. Streetlights improve visibility, help reduce crashes at night by making potential hazards more detectable, and help drivers safely navigate the roads [3,4]. Since scotopic vision takes time to adjust and affects contrast sensitivity and visual acuity, a driver’s ability to respond to road conditions and traffic changes tends to reduce at nighttime. Streetlights on highways reduced crashes at night by an average of 40% [5]. Moreover, appropriate street-lighting can prevent road crashes, injuries, and deaths [6]. Lighting in tunnels affects not only visual performance but also visual aspects such as physiological fatigue and the mental stress of drivers [7].

Over-lighting and improperly located streetlights can cause light pollution, increase vector breeding, and may lead to sleep disturbance for the users [8]. Appropriate design of an urban street-lighting system (USLS) plays an important role in efficient and safe traffic operations, such as spacing and height [9], illumination brightness [8], and positioning of advertisement billboards [10]. Color appearance, color rendering, and discomfort glare are all essential subjective lighting factors because they influence the overall impression formed in the observer’s eye–brain system [11]. Poor or no illumination of streets and intersections increases the chance of crashes, particularly for pedestrians and cyclists outside the illuminated area under the influence of the headlights of an oncoming vehicle [12]. Effective design of midblock lighting improves road safety at nighttime for both drivers and pedestrians [13]. Furthermore, the appropriate height of lights avoids glare, which can affect the driver’s visibility [14].

Modern light-emitting diode (LED) lighting systems optimize luminaire design, save energy, and enhance the visual performance of street-lighting for various traffic conditions [15,16]. While LED adoption in the outdoor lighting sector is progressing, widespread adoption has yet to be realized. The observers can describe the illumination of streets by comparable values of objective lighting parameters, such as luminance level, threshold increment, and surround ratio of road signs’ construction [17,18]. Road lights serve imperative metropolitan and community capacities, advance downtown commerce and economic advancement, and upgrade security within the more highly populated areas [19]. This light source must account for how both the cones and poles are seen by the eye as shorter wavelengths deliver superior (cooler) light vision [20,21]. Glare from the oncoming cars, the height of the median, and road surface conditions also affect drivers’ visual performance [22]. Color appearance, color interpretation, and stress of glare visualization also impact the general impression made within the observer’s eye–brain expression [16,18]. The previous studies affirmed the self-evident preference for warm white lights by drivers due to better visibility of green items and clear perception of signs [17]. For appropriate planning of streetlights and the intended mix, colored lights and short wavelengths should be avoided [23,24]. Electronic billboards along the roadside also impact driver distraction and road safety [25].

Hence, street-lighting design (spacing, positioning of electronic billboards, etc.), visibility (signs, lane marking, oncoming vehicles, etc.), and drivers’ satisfaction (the glare effect on eyes and the overall road infrastructure’s ambiance) primarily affect the performance of a USLS. Appraising the performance of a USLS can identify the underperforming factors and help the concerned authorities to plan improvement actions. User-driven decision-making has been recognized as a robust and useful technique for infrastructure improvement [26,27]. Past studies have evaluated the impacts of these factors individually for a given case. Djokic et al. [28] compared high-pressure sodium with LED street-lighting systems based on drivers’ impressions. A subjective approach requested the drivers to instantly fill in the questionnaire survey about the luminance after driving through both types of roads in Belgrade. Davidovic et al. [11] evaluated drivers’ preference for street-lighting color using a questionnaire survey, in which the drivers selected the more appropriate installation amongst 3000 K and 4000 K LED lighting systems based on lighting design (e.g., detection of light- and dark-colored obstacles) and overall visibility. A study in Stockholm (Sweden) evaluated the impact of electronic billboards on drivers’ visual distraction (after looking at the billboard for over 2 s, using eye-tracking measures) and performance in terms of speed, lateral position, and minimum time headway [10].

The review of the literature highlights a lack of studies on performance appraisal of USLS, which (i) aggregate driver opinion on design, visibility, and satisfaction using a fuzzy-based (suitable for subjective evaluations) multicriteria approach and (ii) highlight problems in the Gulf region, including the Kingdom of Saudi Arabia (KSA), with one of the highest crash rates in the world.

The Ministry of the Qassim Region (MoQ) of the KSA found lighting conditions as one of the main factors affecting crash frequency [29]. Consequently, the MoQ determined to improve the region’s traffic lighting system by changing the existing road lights with LED technology. The present research targeted to appraise the performance (in terms of design, visualization, and driver satisfaction) of existing USLS based on drivers’ opinions to offer more feasible solutions to current road safety issues faced in Buraydah, the capital of Qassim. The performance evaluation criteria were established in the form of a questionnaire survey to assess compliance with KSA’s road safety codes for lighting design and placement of electronic advertisement boards. The main objectives of this research are to: (i) develop a questionnaire survey to obtain drivers’ responses on the performance of existing USLS, (ii) identify important criteria for performance evaluation, and (iii) develop a fuzzy-based evaluation method for information aggregation and decision-making.

The methodology developed in the present work is a step forward to giving appropriate attention to public opinion in decision-making for infrastructure enhancement. Moreover, the present work will help the Electricity Division of MoQ in its ongoing endeavor to determine whether light-emitting diode (LED) streetlamps were a viable solution for reducing energy consumption and maintenance costs and improving the area of Buraydah’s visual appeal. The drivers’ opinions and responses helped the team to evaluate whether excitant light by street-lighting is a desirable possibility to reduce crashes due to advertisement boards and lighting appearance. Furthermore, the research findings will support the Ministry of Transport and MoQ in defining baseline standards for the installation of new or upgradation of street-lighting systems to ultimately have a fully LED livelihood.

2. Methodology

2.1. Performance Appraisal Framework

Nine sub-criteria (PIs) were developed and grouped under three primary criteria, including design functionality, visibility adequacy, and driver satisfaction level, through literature and group discussions. Figure 1 illustrates the hierarchical-based assessment approach used to assess the efficacy of the USLS developed in the present study. In the subsequent step, a questionnaire survey was developed to assess drivers’ opinions of each sub-criterion, where each sub-criterion was translated into a question statement. An online survey was sent to the number of respondents as per the calculated sample size. Statistical analyses were performed on the obtained responses to evaluate the significance of the sub-criteria. Lastly, the fuzzy synthetic evaluation (FSE) method assessed the performance of the street-lighting system in the study area by aggregating the sub-criteria and the primary criteria.

2.2. Study Area

Buraydah City is the capital of the Qassim region, which is located at the center of Saudi Arabia. The city accommodates around 50% of the region’s population [30]. Being the largest city, Buraydah serves as the economic, social, and cultural hub of the region. Qassim region experiences a high traffic crash rate, much like other parts of KSA, primarily due to over-speeding, driver carelessness, use of mobile phones, violations of traffic laws, and inappropriate street-lighting [29,31]. With a large fraction of nighttime crashes in Buraydah, the MoQ is keen to know the effectiveness of street-lighting. Hence, the residents of Buraydah, being the primary road users, were considered for the performance appraisal of the USLS. Figure 2 illustrates the location of the study area and some images showing the city’s street-lighting condition.

2.3. Development of Performance Appraisal Criteria and Questionnaire

Questionnaires have long been a favored method of gathering data in subjective studies [32,33]. The method was found appropriate for the present research’s objectives about streetlights and the characteristics of the luminaire’s examination. In an effort to supply proof of the current street-lighting environment, a questionnaire survey investigated drivers’ opinion about the effectiveness of the existing USLS in Buraydah City. A questionnaire with nine closed-ended queries with multiple-choice response possibilities was developed based on expert validation for the targeted research objectives. All the questions were unambiguous and direct, and a set of answers was given to respondents, which provided the plausibility to select only one answer out of the five choices: “strongly agree”, “agree”, “not sure”, “disagree”, and “strongly disagree”.

Ethics approval of the funding agency was secured before disclosing the survey to the target population, which consisted of individual drivers from different age groups using the city roads of Buraydah City on a daily basis. The questionnaire was distributed online to different age groups, with the minimum of a high school education level, after explaining the general concept and target of the questionnaire to evaluate the three main criteria for USLS, defined in

Table 1. Question statements and performance criteria for an urban street-lighting system.

Code	Criteria	Code	Sub-Criteria	No.	Question Statement
C1	Design Functionality	SC11	Illumination brightness adequacy	1	The illumination brightness of the city roads is considered adequate
		SC12	Spacing appropriateness	2	The level of lighting distribution on the roads is sufficient and regular
		SC13	Positioning of roadside advertisement billboards	3	Advertisement mobile screens and billboards beside the road do not adversely affect visibility
C2	Visibility adequacy	SC21	Visibility of lane marking	4	Current street-lighting design leads you to choose the right track
		SC22	Visibility of road pavement and signs	5	The current night lighting in the city of Buraydah enables you to see the pavement, traffic signs, and the sides of the road
		SC23	Impact of opposite side vehicle lighting	6	Lights from oncoming cars in the opposite direction do not adversely affect visibility due to proper barrier design
C3	Driver satisfaction level	SC31	Impact on eyes	7	The currently available street-lighting in your city is suitable for the eyes and does not cause glare
		SC32	Lighting performance in bad weather	8	During bad weather (dust or rain), the lighting is clear and adequate
		SC33	Road lighting ambiance	9	Road lighting ambiance is good in the city of Buraydah
	Overall performance of street-lighting system			10	Overall opinion on the performance of the street-lighting system based on your driving experience in Buraydah

. In addition to personal information (not stated in this manuscript), the questionnaire included nine question statements, as described in Table 1.

2.4. Statistical Analysis of Survey Responses

The population of Buraydah was 669,000 in the year 2020 [34]. With 5.25 persons per household as per the Buraydah City Review Report [35] and assuming 2 drivers (older than 18 years) per household, the population proportion of road users was found to be 38%. The survey sample size was calculated using the following formula [36]:

$$n=\frac{z^2\times p\left(1-p\right)}{e^2}$$

(1)

where n is the sample size, the z value is 1.96 for the 95% confidence level, the margin of error was assumed to be 5% (0.05), and p is the population proportion.

The participants were asked to complete all the questions in the questionnaire (see Table 1). In addition, some basic personal information of the respondents was also gathered, such as age, use of glasses, the number of crashes experienced (if any), time of the crash, and the type of road where the crash occurred (i.e., main road or branch road). Personal information is not disclosed here.

The responses to the questionnaire survey inform the drivers’ perception of design functionality, visibility adequacy, and driver satisfaction level of the street-lighting system of Buraydah City. Statistical analysis of the collected data was performed to estimate the percentage frequencies of all the responses. Before using this data for the proposed model for identifying the risk factors, the Chi-square independence test established the level of association (weak, moderate, or strong) between the design functionality, visibility adequacy, and driver satisfaction level of the street-lighting system. An example of the null and alternative hypotheses for illumination brightness adequacy is provided in the following:

H1. 

The null hypothesis: illumination brightness adequacy is a perfectly independent factor and does not affect the driver’s visual performance.

H2. 

The alternative hypothesis: illumination brightness adequacy is a dependent factor and somehow affects the driver’s visual performance.

The Chi-square method is based on expected frequencies at which the null hypothesis holds true. The expected frequencies for all the factors against the drivers’ opinions were calculated using the following relationship [37]:

$$e_{ij}=\frac{o_i\times o_j}{N}$$

(2)

where e_ij represents the expected frequency, o_i represents the marginal column frequencies, o_j represents the marginal row frequencies, and N is the total number of responses.

As the o_i and o_j differ, the residuals were estimated as:

$$r_{ij}=o_{ij}-e_{ij}$$

(3)

A larger r_ij value (absolute) denotes a large difference between the observed responses and the null hypothesis. The Chi-square $\left({\chi }^2\right)$ test statistic was estimated by adding all the residuals as:

$${\chi }^2=\sum \frac{{\left(o_{ij}-e_{ij}\right)}^2}{e_{ij}}$$

(4)

Next, for a given ${\chi }^2$ and degree of freedom, the factors’ independence in the population (p-value) was estimated as:

$$d_f=\left(i-1\right)\times \left(j-1\right)$$

(5)

where i and j are the number of rows and columns in the contingency table.

The estimated Chi-squared $\left({\chi }^2\right)$ compared with the critical Chi-squared $\left({\chi }_c^2\right),$ obtained from the ${\chi }^2$ distribution at p < 0.05, established the acceptance and rejection of the null hypothesis of independence. The critical values were 3.84, 5.99, 7.82, 9.49, 11.07, 12.59, and 26.296, with related d_f, estimated from Equation (5), of 1, 2, 3, 4, 5, 6, and 16. The ${\chi }^2$ higher than the ${\chi }_c^2$ rejected the null hypothesis.

As the ${\chi }^2$ test’s performance depends on a sufficiently large sample size, the estimated significance does not clearly state the degree of the effect. The effect size (ES), estimating the strength of the association between the sub-criteria (given in Table 1) and the drivers’ opinion about the overall performance of the USLS (Question 10 in Table 1), was estimated using the results of ${\chi }^2$ test for each sub-criterion using Cramér’s V.

Cramér’s V was estimated by:

$$V=\sqrt{\frac{{\chi }^2}{n.d_f}}$$

(6)

where n denotes the total number of responses. As per the interpretation of ES presented by Cohen [33], ES < 0.2 represents a small association, 0.2 < ES ≤ 0.6 a medium association, and ES > 0.6 a large association between the parameters.

2.5. Street-Lighting System Appraisal Using Fuzzy Synthetic Evaluation

The following assumptions were made to assess the efficacy of the street-lighting system of Buraydah City: (i) there are two drivers (road users) in each household, and (ii) all the drivers hold a driving license and have the same level of understanding about road infrastructure, traffic signs, lane marking, and street-lighting, irrespective of the number of years of driving experience.

Statistical analysis in the previous section identifies the important criteria affecting USLS performance. The hierarchical approach to assess the urban street-lighting system assessment presented in Figure 3 requires a weighting scheme and an aggregation scheme, which turns it into a multicriteria analysis (MCA) problem.

Fuzzy synthetic evaluation (FSE) was found appropriate to aggregate the data on PIs gathered through survey responses. Fuzzy set theory has been used in the past to solve MCA problems with uncertain data, subjective information, and imprecise judgment [38]. FSE is an application of the fuzzy set theory, which solves a multicriteria decision-making problem by synthetically evaluating an object relative to an objective in a fuzzy environment [39]. FSE has recently been used in smart city development and traffic congestion assessment projects [40,41]. The FSE method was adopted to assess the urban street-lighting system, which has essentially been defined as an MCA problem in the present study [42]. The hierarchical structure of the problem presented in Figure 3 was solved by FSE using the following steps.

The drivers’ perception of different aspects of the street-lighting systems was secured through the questionnaire survey presented in Table 1. The universe of discourse (UoD) linguistically defining the five-level rating scheme is presented in

Table 2. Subjective response levels and street-lighting system effectiveness rating.

Response Level	Sj	Criteria and Sub-Criteria Performance
		Fuzzy Range	Subjective Rating	Color Coding Scheme
Strongly disagree	1	0, 0, 2	Very low
Agree	2	1, 2, 3	Low
Not sure	3	2, 3, 4	Medium
Disagree	4	3, 4, 5	High
Strongly disagree	5	4, 5, 5	Very high

.

The term $x_{ij}^{SC}$ denotes the degree of association of each sub-criterion to the first level, where S_j = 1, 2, 3, 4, and 5. Additionally, see Table 2 for S_j values.

Equation (7) describes it in the matrix form:

$${\left(X_i^{SC}\right)}_{1\times 5}=\left(x_{i1}^{SC},x_{i2}^{SC},x_{i3}^{SC},x_{i4}^{SC},x_{i5}^{SC}\right)$$

(7)

where $X_i^{SC}$ represents the sub-criteria i (where i = 1, 2, 3) under each criteria k, where k = 1, 2, 3.

Equation (8) calculated the impact of each sub-criterion ($S{C}_{ki}$):

$$S{C}_{ki}={{\displaystyle \sum }}_{i=1}^5\left(S_j\ast x_{ij}^{SC}\right)$$

(8)

Equation (9) estimated the relative weights for the sub-criteria for assessing the criteria performance at level 2 of Figure 1:

$$w_i^{SC}=S{C}_{ki}/{{\displaystyle \sum }}_{i=1}^{n}S{C}_{ki}\hspace{1em},\mathrm{where}n=3$$

(9)

The weighted matrix developed by Equation (8) and the evaluation matrix by Equation (6) were aggregated using FSE. Equation (9) estimated the membership functions for each sub-criterion, where i = 1, 2, …, t:

$$d_{tj}^{SC}={\sum }_{i=1}^k{w}_i^{SC}\ast x_{ij}^{SC} , k=3$$

(10)

$${\left(D_t^C\right)}_{1\times 5}={\left(W_i^{SC}\right)}_{1\ast k}\ast {\left(X_i^{SC}\right)}_{k\times 5}=\left(d_{t1, }^{SC}{d}_{t2, }^{SC}{d}_{t3, }^{SC}{d}_{t4, }^{SC}{d}_{t5 }^{SC}\right)$$

(11)

With known membership functions of t number of criteria at level 2, Equation (12) estimated the overall impact of the sub-criteria ($S{C}_{ij}$):

$$C_1={{\displaystyle \sum }}_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)$$

(12)

Likewise, Equations (13) and (14) estimated the overall impact of C₂ and C₃ as:

$$C_2={{\displaystyle \sum }}_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)$$

(13)

$$C_3={{\displaystyle \sum }}_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)$$

(14)

Level 3: Calculate the overall effectiveness of USLS

First, the relative weights of C₁, C₂, and C₃ were estimated using Equation (14) to estimate the impact of C₁, C₂, and C₃ on the street-lighting system.

$$w_{Gt}^{C1}=\left({\sum }_{i=1}^k{C}_i\right)/{\sum }_{t=1}^q{\left({\sum }_{i=1}^k{C}_i\right)}_t,\hspace{1em}q=2$$

(15)

where $w_{Gt}^{C1}$ are the relative weights of the criteria ($C_1,$ $C_{2 }$, and $C_3$), t represents the number of criteria (q = 3), and i denotes the number of sub-criteria under $Y_i$ (k = 3) and C_i (k = 3), and:

$$d_{Allj}^{ESL}={\sum }_{t=1}^q{w}_{Gt}^C\ast d_{tj}^C,\hspace{1em}q=3$$

(16)

$${\left(D_{All}^C\right)}_{1\ast 4}={\left(W_{Gt}^C\right)}_{1\ast 2}\times {\left(D_G^C\right)}_{2\ast 4}=\left(d_{t1, }^C{d}_{t2, }^C{d}_{t3, }^C{d}_{t4 }^C,d_{t5 }^C\right)$$

(17)

$$ESL={\sum }_{i=1}^3\left(S_j\ast d_{tj}^C\right)$$

(18)

Equation (17) assesses the USLS based on driver perception judged through a questionnaire survey. Finally, USLS was subjectively defined based on the calculated values by Equation (18), using the scales defined in Table 2.

3. Results

3.1. Statistical Analysis of Questionnaire Survey Responses

This study aimed to assess the effectiveness of the street-lighting system of Buraydah based on the drivers’ opinions while using the city’s roads during the nighttime. A sample size of n = 362 was determined from Equation (1) and the online questionnaire was sent to more than 400 respondents, who were able to access the google form. With a 64% response rate, 253 drivers (70% of the sample size) filled out the questionnaire to record their perception of the street-lighting system.

Figure 4 presents the descriptive statistics of the survey response from the car drivers in the study area. Figure 4a shows that more than half (54%) of the drivers were younger than 40 years of age, out of which the majority (29%) were younger than 30 years. Around 47% of the drivers wore glasses and therefore are potentially more affected by the inappropriate USLS in the area (Figure 4b). A high percentage (40%) of the respondents had never faced any crash, which reflects a moderate overall road safety in the study area. Out of the remaining, 31% of the respondents had experienced a crash only once in their lifetime, while 11% had faced three crashes over their lifetime. Interestingly, the distribution between the drivers who faced road crashes during the daytime (37%) and the nighttime (37%) was equal, while the remaining drivers experienced crashes in both day and night times (Figure 4d). Figure 4e shows that most (55%) of the crashes occurred on main roads with speed limits higher than the branch roads. The Chi-square test applied to age and use of glasses revealed an insignificant association of these parameters with the performance of USLS, with ${\chi }^2$ values less than ${\chi }_c^2$ and ES < 0.2 (see

Table 3. Summary of responses, hypotheses, and the level of association between sub-criteria provided in Table 1 and overall performance.

Criterion No.	Statement to Assess Sub-Criteria	Strongly Agree	Agree	Not Sure	Disagree	Strongly Disagree	ꭕ2	Significance at p < 0.05	Cramér’s V	Association
C11	The illumination brightness of the city roads is considered adequate	45	129	44	25	12	114	Significant	0.34	Moderate
C12	The level of lighting distribution on the roads is sufficient and regular	47	104	60	33	11	128.1	Significant	0.36	Moderate
C13	Advertisement mobile screens and billboards beside the road do not adversely affect visibility	48	90	39	47	31	29.4	Significant	0.17	Weak
C21	Current street-lighting design leads you to choose the right track	56	123	40	23	13	121.4	Significant	0.35	Moderate
C22	The current night lighting in the city of Buraydah enables you to see the pavement, traffic signs, and the sides of the road	45	116	48	35	11	168.2	Significant	0.41	Moderate
C23	Lights from oncoming cars in the opposite direction do not adversely affect visibility	42	79	48	59	28	106.2	Significant	0.33	Moderate
C31	The currently available street-lighting in your city is suitable for the eyes and does not cause glare	54	109	41	39	12	138.0	Significant	0.37	Moderate
C32	During bad weather (dust or rain), the lighting is clear and adequate	35	57	55	64	24	47.2	Significant	0.22	Moderate
C33	Road lighting level’s ambiance is good in the city of Buraydah	48	132	40	21	12	227.1	Significant	0.47	Moderate

).

Figure 5 presents the percentage for each sub-criterion assessed through the corresponding question, as presented in Table 3. It can be seen in Figure 5 that 54–68% of the drivers agreed (or strongly agreed), 15–23% were not sure, and 15–30% disagreed (or strongly disagreed) on the effectiveness of the “design functionality” of the USLS in Buraydah City. Likewise, 47–70% of drivers agreed (or strongly agreed), around 16–19% stated that they were not sure, and 14–34% disagreed (or strongly disagreed) on the “visibility adequacy”. Around 40–73% agreed (or strongly agreed), 13–23% said that they were not sure, and 14–37% disagreed (or strongly disagreed) with the statements querying about the high levels of driver satisfaction. The results of the Chi-square test presented in Table 3 show a significant association between all the sub-criteria to the overall performance of USLS, i.e., estimated with the help of Question 10 in Table 1. Cramér’s V values also showed a moderate association, with ES values ranging between 0.2 and 0.6 for all the sub-criteria, except C₁₅, with a slightly smaller value. Hence, all the sub-criteria were used to appraise the performance of USLS in the study area.

3.2. Street-Lighting System Assessment Using FSE

The FSE method described in the methodology section provides an opportunity to capture the entire range of responses. The method gives higher weights to the response level (e.g., agree and disagree) towards which most of the respondents are inclined too. However, consideration is also given to the less selected response level (see Table 2).

Equation (7) estimated the degree of association to the 5-level rating (S_j = 1, 2, 3, 4, 5) for each sub-criterion. The term $X_1^{SC}$, representing the first sub-criterion, “illumination brightness adequacy” (refer to Table 1), was calculated as:

$${\left(X_i^{SC}\right)}_{1\times 5}=\left(x_{i1}^{SC},x_{i2}^{SC},x_{i3}^{SC},x_{i4}^{SC},x_{i5}^{SC}\right)=\left(0.047,0.098,0.173,0.506,0.176\right)$$

Equation (7) estimated the performance score of $S{C}_{11}$ as:

$$\begin{gathered} S{C}_{11}={\sum }_{i=1}^5\left(S_j\ast x_{ij}^{SC}\right)=1\times 0.047+2\times 0.098+3\times 0.173+4\times 0.506+ \\ 5\times 0.176=3.67 \end{gathered}$$

Similarly, $S{C}_{ki},$ representing all the sub-criteria in Table 1, was calculated as:

$$S{C}_{12 }\left(sapcing appropriateness\right)=3.56$$

and

$$S{C}_{13}\left(Positioning of roadside advertisement billboards\right)=3.30.$$

Relative importance weights of the sub-criteria, under each criterion, were estimated using Equation (9):

$$w_{11}^{SC}=S{C}_{ki}/{\sum }_{i=1}^{n}S{C}_{ki}=3.67/(3.67+3.56+3.30)=3.67/10.53=0.348$$

Likewise, the weight for $w_{12}^{SC}$ was found to be 0.338, and 0.314 for $w_{13}^{SC}$.

Next, FSE applied Equation (11) to calculate the membership functions for each criterion:

$$\begin{array}{c}{\left(D_t^C\right)}_{1\times 5}={\left(W_i^{SC}\right)}_{1\ast 3}\times {\left(X_i^{SC}\right)}_{3\times 5}\hfill \\ =\left[0.3480.3380.314\right]\times \left[\begin{array}{ccccc}0.047& 0.098& 0.173& 0.506& 0.176\\ 0.043& 0.129& 0.235& 0.408& 0.184\\ 0.122& 0.184& 0.153& 0.353& 0.188\end{array}\right]\hfill \\ D_1^C=\left[\begin{array}{ccccc}0.069& 0.135& 0.188& 0.425& 0.182\end{array}\right]\hfill \end{array}$$

Correspondingly, the membership functions for $D_2^C$ and $D_3^C$ were calculated as:

$$\begin{gathered} D_2^C=\left[\begin{array}{ccccc}0.066& 0.149& 0.177& 0.420& 0188\end{array}\right] \\ {D}_3^C=\left[\begin{array}{ccccc}0.063& 0.163& 0.181& 0.405& 0.185\end{array}\right] \end{gathered}$$

Subsequently, Equations (11)–(13) calculated the membership functions of t number of criteria at level 2 of Figure 3 as:

$$\begin{array}{c}\mathit{Design} \mathit{functionality} (C_1)={\sum }_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)=(1\times 0.069+2\times 0.135+3\times 0.188+\hfill \\ 4\times 0.425+5\times 0.182=3.51\hfill \end{array}$$

and

$$Visibility adequacy (C_2)={\sum }_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)=3.52$$

$$Driver satisfaction level \left(C_3\right)={\sum }_{i=1}^5\left(S_j\ast d_{tj}^{SC}\right)=3.48$$

Then, the relative weights of $C_1$, $C_2$, and $C_3$ were estimated using Equation (14) to estimate their contribution to USLS’s performance, as:

$$w_{C1}=\frac{3.51}{3.51+3.52+3.48}=0.334$$

$$w_{C2}=\frac{3.52}{3.51+3.52+3.48}=0.334$$

$$w_{C3}=\frac{3.48}{3.51+3.52+3.48}=0.331$$

Then, the membership functions for each criterion were determined using Equation (16), as:

$$\begin{array}{c}{\left(D_{All}^C\right)}_{1\times 5}={\left(W_{Gt}^C\right)}_{1\ast 3}\times {\left(D_G^C\right)}_{3\ast 5}=\left(d_{t1, }^C{d}_{t2, }^C{d}_{t3, }^C{d}_{t4 }^C,d_{t5 }^C\right)\hfill \\ =\left[\begin{array}{ccc}0.334& 0.334& 0.331\end{array}\right]\times \left[\begin{array}{ccccc}0.069& 0.135& 0.188& 0.425& 0.182\\ 0.066& 0.149& 0.177& 0.420& 0.188\\ 0.063& 0.163& 0.181& 0.405& 0.185\end{array}\right]\hfill \\ {\left(D_{All}^C\right)}_{1\times 5}=\left[\begin{array}{ccccc}0.066& 0.149& 0.182& 0.416& 0.185\end{array}\right]\hfill \end{array}$$

Finally, Equation (17) appraised the effectiveness of USLS (USLS_Eff) in Buraydah City, as:

$$\begin{array}{c}USL{S}_{Eff}={\sum }_{i=1}^3\left(S_j\ast d_{tj}^C\right)=(1\times 0.066+2\times 0.149+3\times 0.182+4\times 0.416+5\times \hfill \\ 0.185)=3.501\hfill \end{array}$$

Using the subjective rating scheme presented in Table 2, the level of effectiveness of the street-lighting system is “high”. Figure 6a presents the final results for all the sub-criteria and Figure 6b for the three primary criteria. The fuzzy range of each performance level defined in Table 2 is defined with a color-coding scheme from red (very low to low), green (low to medium), amber (medium to high), and yellow (high to very high) tones.

4. Discussion

The Qassim Region reported one of the highest traffic crashes in Saudi Arabia with an increase in fatalities of 24.3% in 2022, which is one of the highest increases in the Kingdom [43]. The situation demands all sorts of road safety measures, including improved USLS. Performance evaluation criteria to appraise the performance of USLS were developed from the literature and based on expert judgment. The first criterion (C₁), “design functionality”, was assessed by three sub-criteria, including illumination brightness adequacy (SC₁₁), spacing appropriateness (SC₁₂), and positioning of advertisement boards (SC₁₃). Figure 6 shows that the performance of SC₁₁ was “medium to high”, with a crisp performance score of 3.67. Past studies have reported good technical functionality of the thermal and electrical design of LED technology [44]. As almost 90% of the roads of Buraydah City have been installed with LED, the performance is expected to improve with complete LED-based street-lighting. Most of the respondents found the spacing between the poles appropriate, which resulted in a “medium to high” performance (3.56) of SC₁₂. The distance between the two poles of light is 2.5 to 3 times the pole’s height in Buraydah City, which is a global standard [9]. The minimum score of 3.13 of SC₁₃ shows some inappropriate positioning in the densely populated areas (also see Figure 2). Both the frequency and duration of fixations were reported higher in the case of electronic billboards, in comparison to other signs installed along the roadside [10,45]. The overall “medium to high” performance of C₁ suggests a need for improvement, particularly regarding the locations of electronic billboards.

The visibility of lane marking (SC₂₁), visibility of road pavement and signs (SC₂₂), and impact of oncoming vehicle lighting (SC₂₃) assessed the second criterion (C₂), “visibility adequacy”. The performance of SC₂₁ was found high, with a score of 3.73, almost in the middle of the high-performance zone. These findings show that there is desirable night-time visibility of lane marking in the study area. As per the Federal Highway Administration (U.S. Department of Transportation), highly maintained and visible marking (SC₂₂) and efficient street-lighting reduce crash risk by improving the nighttime visibility for drivers and other users [46]. The performance score of 3.19 for SC₂₃ shows a medium satisfaction level of road users in the study area, which means that under existing visibility conditions, drivers commonly face difficulties associated with oncoming vehicle lighting. Low headlight beams engender insufficient visibility and a high safety risk, while high beams propagate a disturbing glare to the drivers [47]. Although most of the new vehicle lighting system has increased the optimal photometric performance, it is still essential to establish the luminous intensity limits for high-beam headlights. The results also show room for improvement in the design of medians and barriers in the study area. During USLS renovations, optimizing photometric characteristics of pavement surfaces and lighting design, by studying various scenarios, can improve road safety and energy efficiency [48]. The smart LED-based USLS, properly designed for environmental conditions and pedestrian traffic, can also be adopted as an energy conservation practice [44]. A study by Buyukkinaci [49] reported no impact of lower luminous fluxes (lighting levels) on visibility levels for vehicles driven at a constant speed under the limits. Hence, USLS can be automated for speed limits, traffic volumes, traffic density, and road and environmental conditions to save up to 40% of energy.

Driver satisfaction level (C₃) was assessed using three sub-criteria, including impact on eyes (SC₃₁), lighting performance in bad weather (SC₃₂), and road lighting ambiance (SC₃₃). SC₃₁ attained a 3.6 scrip score, showing a medium level of driver satisfaction, as 20% of the drivers disagreed or strongly disagreed about the negligible or no impact of streetlights on their eyes. These findings suggest both reevaluations of the existing design of USLS as well as conversion to complete LED lights. Nevertheless, dimmed LED streetlights can affect driver visibility, and consequently, distance recognition and reaction times [50], which directs to further investigations and optimization of the dimming effect of LED lights. Adding speed limits to such road sections can enhance road safety. The performance of USLS in bad weather (SC₃₂) obtained a medium level (3.06), which was the minimum score of a sub-criterion under C₃. Over 37% of respondents either disagreed or highly disagreed that there is no impact of bad weather on USLS performance. These results are expected due to frequent dust storms before the summer season. The overall efficiency of streetlights, even LED luminaires, is subject to a phenomenon known as dirt depreciation [51]. However, due to less heat generation in comparison to simple streetlights, less dust and debris adhere to LED lights. Besides 14% of respondents, all the drivers supported the overall ambiance (SC₃₃) of USLS in the study area, which resulted in a “medium to high” performance score of 3.72.

FSE overall revealed a “medium to high” performance for all three primary criteria, which results in a similar overall effectiveness of USLS in Buraydah City. However, these results suggest a complete shift to LED lights. The study showed that FSE is a useful method for evaluating the performance of an urban infrastructure system based on users’ experiences and opinions.

5. Conclusions

Drivers, being the primary users of urban highways and roads, are the most affected by an inappropriate design of urban street-lighting systems. The performance of urban street-lighting systems can be effectively appraised through drivers’ opinions gathered with the help of a questionnaire survey. A performance evaluation framework consisting of nine sub-criteria encompassed under three main criteria was designed. Each sub-criterion was appraised through a question posed to the respondents. Chi-squared and Cramér’s V tests found a significant association between the evaluation sub-criteria and the performance of USLS. Fuzzy synthetic evaluation aggregated the statistically diverse responses obtained on the lighting design, visibility, and driver satisfaction, the three primary criteria. The primary criteria evaluation revealed that the positioning of electronic billboards, the impact of oncoming vehicle lights, and poor lighting during bad weather (mainly dust) are the main underperforming sub-criteria (factors) affecting the performance of urban street-lighting systems in the study area. Largely, all the primary criteria need further improvement, with performance levels ranging between “medium” and “high”, with no criteria or sub-criteria achieving a “very high” performance level.

The following factors can be included in future studies on the performance assessment of urban street-lighting systems. The impact of lighting on nighttime accidents can be assessed with more accuracy by asking the drivers questions related to the visibility and design of street-lighting, immediately after the occurrence of a crash. Likewise, the information about nighttime crashes that involved a pedestrian can be used to evaluate the height of the luminary design. As vehicle sizes (lightweight or heavyweight) are equipped with headlights of different intensities (lumens), future research should include this factor in the evaluation process.

The proposed methodology can be used for translating human judgment into a more comprehensible measure for performance improvement of the urban street-lighting system in Qassim (KSA) and elsewhere. The transportation ministries in different regions can add additional measures (factors) to the questionnaire survey to appraise the street-lighting system’s performance and driver satisfaction before making improvement decisions, such as conversion to LED lights and smart lighting control systems.