Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics
Abstract
:1. Introduction
2. Eye-Tracking Systems in VR Environments
3. Reading, Legibility, and Eye Movement
Eye-tracking allows us to measure an individual’s visual attention, yielding a rich source of information on where, when, how long, and in which sequence certain information in space or about space is looked at.
4. Legibility of Road Signage
5. Arabic Road Signage
- Red;
- Green;
- Blue;
- White;
- Brown.
6. Research Hypothesis
6.1. Experiment
6.2. Participants
6.3. Apparatus
6.4. Stimuli and Task
7. Results
- Participants struggled to see signs placed near a roundabout, and some participants had difficulty seeing a sign with sun-glare in the afternoon or morning hours as the sun shone through drivers’ windshields at 45°.
- Participants struggled to identify the number on some of the speed signs due to them having small fonts.
- Participants ignored signs that were related to a specific location, e.g., Bait al-Hikmah
Statistical Analysis of Eye-Tracking, Cognitive Load, and Heart Rate in Relation to Video Content
8. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Bucchi, A.; Sangiorgi, C.; Vignali, V. Traffic Psychology and Driver Behavior. Procedia-Soc. Behav. Sci. 2012, 53, 972–979. [Google Scholar] [CrossRef]
- Oviedo-Trespalacios, O.; Truelove, V.; Watson, B.; Hinton, J.A. The impact of road advertising signs on driver behaviour and implications for road safety: A critical systematic review. Transp. Res. Part A Policy Pract. 2019, 122, 85–98. [Google Scholar] [CrossRef]
- Tejero, P.; Insa, B.; Roca, J. Reading Traffic Signs While Driving: Are Linguistic Word Properties Relevant in a Complex, Dynamic Environment? J. Appl. Res. Mem. Cogn. 2019, 8, 202–213. [Google Scholar] [CrossRef]
- Garvey, P.M.; Klena, M.J.; Eie, W.-Y.; Meeker, D.T.; Pietrucha, M.T. Legibility of the Clearview Typeface and FHWA Standard Alphabets on Negative- and Positive-Contrast Signs. Transp. Res. Rec. J. Transp. Res. Board 2016, 2555, 28–37. [Google Scholar] [CrossRef]
- Bullough, J.D.; Skinner, N.P.; O’Rourke, C.P. Legibility of urban highway traffic signs using new retroreflective materials. Transport 2010, 25, 229–236. [Google Scholar] [CrossRef]
- Dobres, J.; Chrysler, S.T.; Wolfe, B.; Chahine, N.; Reimer, B. Empirical Assessment of the Legibility of the Highway Gothic and Clearview Signage Fonts. Transp. Res. Rec. J. Transp. Res. Board 2017, 2624, 1–8. [Google Scholar] [CrossRef]
- Garvey, P.M.; Pietrucha, M.T.; Meeker, D. Effects of Font and Capitalization on Legibility of Guide Signs. Transp. Res. Rec. J. Transp. Res. Board 1997, 1605, 73–79. [Google Scholar] [CrossRef]
- Gene Hawkins, H., Jr.; Picha, D.L.; Wooldridge, M.D.; Greene, F.K.; Brinkmeyer, G. Performance Comparison of Three Freeway Guide Sign Alphabets. Transp. Res. Rec. J. Transp. Res. Board 1999, 1692, 9–16. [Google Scholar] [CrossRef]
- Al-Madani, H. Prediction of Drivers’ Recognition of Posted Signs in Five Arab Countries. Percept. Mot. Ski. 2001, 92, 72–82. [Google Scholar] [CrossRef]
- Al-Madani, H.; Al-Janahi, A.-R. Assessment of drivers’ comprehension of traffic signs based on their traffic, personal and social characteristics. Transp. Res. Part F Traffic Psychol. Behav. 2002, 5, 63–76. [Google Scholar] [CrossRef]
- Ghadban, N.R.; Abdella, G.M.; Alhajyaseen, W.; Al-Khalifa, K.N. Analyzing the Impact of Human Characteristics on the Comprehensibility of Road Traffic Signs. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Bandung, Indonesia, 6–8 March 2018; pp. 2210–2219. [Google Scholar]
- Mahgoub, A.O.; Choe, P. Comparing Design Preference of Guide Road Signs by Native Arabic Speakers and International Speakers in the State of Qatar. In Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 14–17 December 2020; pp. 504–508. [Google Scholar] [CrossRef]
- Taamneh, M.; Alkheder, S. Traffic sign perception among Jordanian drivers: An evaluation study. Transp. Policy 2018, 66, 17–29. [Google Scholar] [CrossRef]
- Beier, S.; Oderkerk, C.A.T. High letter stroke contrast impairs letter recognition of bold fonts. Appl. Ergon. 2021, 97, 103499. [Google Scholar] [CrossRef] [PubMed]
- Slattery, T.J.; Rayner, K. The influence of text legibility on eye movements during reading. Appl. Cogn. Psychol. 2010, 24, 1129–1148. [Google Scholar] [CrossRef]
- Franken, G.; Pangerc, M.; Možina, K. Impact of Typeface and Colour Combinations on LCD Display Legibility. Emerg. Sci. J. 2020, 4, 436–442. [Google Scholar] [CrossRef]
- Dijanić, H.; Jakob, L.; Babić, D.; Garcia-Garzon, E. Driver eye movements in relation to unfamiliar traffic signs: An eye tracking study. Appl. Ergon. 2020, 89, 103191. [Google Scholar] [CrossRef] [PubMed]
- Reingold, E.M.; Rayner, K. Examining the Word Identification Stages Hypothesized by the E-Z Reader Model. Psychol. Sci. 2006, 17, 742–746. [Google Scholar] [CrossRef] [PubMed]
- Henderson, J.M. Gaze Control as Prediction. Trends Cogn. Sci. 2017, 21, 15–23. [Google Scholar] [CrossRef] [PubMed]
- Torralba, A.; Oliva, A.; Castelhano, M.S.; Henderson, J.M. Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search. Psychol. Rev. 2006, 113, 766–786. [Google Scholar] [CrossRef] [PubMed]
- Goldberg, J.H.; Wichansky, A.M. Eye Tracking in Usability Evaluation: A Practitioner’s Guide. In The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research; Elsevier: Amsterdam, The Netherlands, 2003; pp. 493–516. Available online: https://www.researchgate.net/publication/259703518 (accessed on 18 March 2024). [CrossRef]
- Valdois, S.; Roulin, J.-L.; Bosse, M.L. Visual attention modulates reading acquisition. Vis. Res. 2019, 165, 152–161. [Google Scholar] [CrossRef] [PubMed]
- Klaib, A.F.; Alsrehin, N.O.; Melhem, W.Y.; Bashtawi, H.O.; Magableh, A.A. Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Syst. Appl. 2021, 166, 114037. [Google Scholar] [CrossRef]
- Kiefer, P.; Giannopoulos, I.; Raubal, M.; Duchowski, A. Eye tracking for spatial research: Cognition, computation, challenges. Spat. Cogn. Comput. 2017, 17, 1–19. [Google Scholar] [CrossRef]
- Arditi, A.; Cho, J. Serifs and Font Legibility. Vis. Res. 2005, 45, 2926–2933. [Google Scholar] [CrossRef] [PubMed]
- Mansfield, J.S.; Legge, G.E.; Bane, M.C. Psychophysics of Reading. XV: Font Effects in Normal and Low Vision. Investig. Ophthalmol. Vis. Sci. 1996, 37, 1492–1501. [Google Scholar]
- Tinker, M.A. Influence of simultaneous variation in size of type, width of line, and leading for newspaper type. J. Appl. Psychol. 1963, 47, 380–382. [Google Scholar] [CrossRef]
- Rayner, K. Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 1998, 124, 372–422. [Google Scholar] [CrossRef] [PubMed]
- Rayner, K.; Reichle, E.D.; Stroud, M.J.; Williams, C.C.; Pollatsek, A. The effect of word frequency, word predictability, and font difficulty on the eye movements of young and older readers. Psychol. Aging 2006, 21, 448–465. [Google Scholar] [CrossRef] [PubMed]
- Schieber, F.; Burns, D.M.; Myers, J.; Willan, N.; Gilland, J. Driver Eye Fixation and Reading Patterns while Using Highway Signs under Dynamic Nighttime Driving Conditions: Effects of Age, Sign Luminance and Environmental Demand. In Proceedings of the 83rd Annual Meeting of the Transportation Research Board, Washington, DC, USA, 11–15 January 2004. [Google Scholar]
- Carlson, P.; Miles, J.; Park, E.S.; Young, S.; Chrysler, S.; Clark, J. Development of a Model Performance-Based Sign Sheeting Specification Based on the Evaluation of Nighttime Traffic Signs Using Legibility and Eye-Tracker Data; Texas Transportation Institute: Bryan, TX, USA, 2010. [Google Scholar]
- Rockwell, T.H.; Overby, C.; Mourant, R.R. Drivers’ Eye Movements: An Apparatus and Calibration. Highw. Res. Rec. 1968, 247, 29–42. [Google Scholar]
- Mourant, R.R.; Rockwell, T.H. Map** eye-movement patterns to the visual scene in driving: An exploratory study. Hum. Factors: J. Hum. Factors Ergon. Soc. 1970, 12, 81–87. [Google Scholar] [CrossRef] [PubMed]
- Bhise, V.D.; Rockwell, T.H. Development of a Methodology for Evaluating Road Signs. In Bull 207 Final Report; Elsevier: Amsterdam, The Netherlands, 1973. [Google Scholar]
- Forbes, T.W.; Saari, B.B.; Greenwood, W.H.; Goldblatt, J.G.; Hill, T.E. Luminance and contrast requirements for legibility and visibility of highway signs. Transp. Res. Rec. 1976, 562, 59–72. [Google Scholar]
- Richards, O.W. Effects of luminance and contrast on visual acuity, ages 16 to 90 years. Am. J. Optom. Physiol. Opt. 1977, 54, 178–184. [Google Scholar] [CrossRef]
- Silavi, T.; Hakimpour, F.; Claramunt, C.; Nourian, F. The Legibility and Permeability of Cities: Examining the Role of Spatial Data and Metrics. ISPRS Int. J. Geo-Inf. 2017, 6, 101. [Google Scholar] [CrossRef]
- Rackoff, N.J.; Rockwell, T.H. Driver Search and Scan Patterns in Night Driving. Transp. Res. Board Spec. Rep. 1975, 156, 53–63. [Google Scholar]
- Shinar, D.; McDowell, E.D.; Rackoff, N.J.; Rockwell, T.H. Field Dependence and Driver Visual Search Behavior. Hum. Factors 1978, 20, 553–559. [Google Scholar] [CrossRef]
- USDHWA. Workshops on Nighttime Visibility of Traffic Signs: Summary of Workshop Findings. 2002. Available online: https://highways.dot.gov/safety/other/visibility/workshops-nighttime-visibility-traffic-signs-summary-workshop-findings-7 (accessed on 18 March 2024).
- Yin, Y.; Wen, H.; Sun, L.; Hou, W. The Influence of Road Geometry on Vehicle Rollover and Skidding. Int. J. Environ. Res. Public Health 2020, 17, 1648. [Google Scholar] [CrossRef] [PubMed]
- Olson, P.L.; Bernstein, A. The Nighttime Legibility of Highway Signs as a Function of Their Luminance Characteristics. Hum. Factors J. Hum. Factors Ergon. Soc. 1979, 21, 145–160. [Google Scholar] [CrossRef] [PubMed]
- Lai, C.J. Effects of color scheme and message lines of variable message signs on driver performance. Accid. Anal. Prev. 2010, 42, 1003–1008. [Google Scholar] [CrossRef] [PubMed]
- Zwahlen, H.T. Traffic sign reading distances and times during night driving. Transp. Res. Rec. 1995, 1495, 140–146. [Google Scholar]
- Zwahlen, H.T.; Russ, A.; Schnell, T. Viewing Ground-Mounted Diagrammatic Guide Signs Before Entrance Ramps at Night: Driver Eye Scanning Behavior. Transp. Res. Rec. J. Transp. Res. Board 2003, 1843, 61–69. [Google Scholar] [CrossRef]
- Hudák, M.; Madleňák, R. The Research of Driver’s Gaze at the Traffic Signs. CBU Int. Conf. Proc. 2016, 4, 896–899. [Google Scholar] [CrossRef]
- Sawyer, B.D.; Dobres, J.; Chahine, N.; Reimer, B. The Cost of Cool: Typographic Style Legibility in Reading at a Glance. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2017, 61, 833–837. [Google Scholar] [CrossRef]
- Wang, L.G. Influence of different highway traffic signs on driver’s gaze behavior based on visual characteristic analysis. Adv. Transp. Stud. 2021, 53, 211–219. [Google Scholar]
- BoutrosFonts. Boutros Advertisers Naskh—Boutros Fonts—Arabic Font Design Experts. 1977. Available online: https://www.boutrosfonts.com/Boutros-Advertisers-Naskh.html (accessed on 18 March 2024).
- Department of Municipalities and Transport. Abu Dhabi Manual on Uniform Traffic Control Devices (MUTCD); Department of Municipalities and Transport: Abu Dhabi, United Arab Emirates, 2020.
- Ismail, A.A.-I. Comprehension of Posted Highway Traffic Signs in Iraq. Tikrit J. Eng. Sci. 2012, 19, 62–70. [Google Scholar] [CrossRef]
- Abu-Rabia, S. Reading in Arabic Orthography: The Effect of Vowels and Context on Reading Accuracy of Poor and Skilled Native Arabic Readers in Reading Paragraphs, Sentences, and Isolated Words. J. Psycholinguist. Res. 1997, 26, 465–482. [Google Scholar] [CrossRef] [PubMed]
- Jamoussi, R.; Roche, T. Road sign romanization in Oman: The linguistic landscape close-up. Aust. Rev. Appl. Linguist. 2017, 40, 40–70. [Google Scholar] [CrossRef]
- Jamson, S.L.; Tate, F.N.; Jamson, A.H. Evaluating the effects of bilingual traffic signs on driver performance and safety. Ergonomics 2005, 48, 1734–1748. [Google Scholar] [CrossRef]
- Lemonakis, P.; Kourkoumpas, G.; Kaliabetsos, G.; Eliou, N. Survey and Design Consistency Evaluation in Two-Lane Rural Road Segments. WSEAS Trans. Syst. Control 2022, 17, 50–55. [Google Scholar] [CrossRef]
- Sloboda, M.; Szabó-Gilinger, E.; Vigers, D.; Šimičić, L. Carrying out a language policy change: Advocacy coalitions and the management of linguistic landscape. Curr. Issues Lang. Plan. 2010, 11, 95–113. [Google Scholar] [CrossRef]
- Street, J.; Papaix, C.; Yang, T.; Büttner, B.; Zhu, H.; Ji, Q.; Lin, Y.; Wang, T.; Lu, J. Street Usage Characteristics, Subjective Perception and Urban Form of Aging Group: A Case Study of Shanghai, China. Sustainability 2022, 14, 5162. [Google Scholar] [CrossRef]
- Wang, J.; **, Signs, and Other Forms of Information. In International Encyclopedia of Transportation; Elsevier: Amsterdam, The Netherlands, 2021; Volume 1. [Google Scholar] [CrossRef]
- Cristea, M.; Delhomme, P. Facteurs influençant la lecture des automobilistes et leur compréhension de messages embarqués portant sur le trafic routier. Rev. Eur. Psychol. Appl. 2015, 65, 211–219. [Google Scholar] [CrossRef]
- Di Stasi, L.L.; Megías, A.; Cándido, A.; Maldonado, A.; Catena, A. Congruent visual information improves traffic signage. Transp. Res. Part F Traffic Psychol. Behav. 2012, 15, 438–444. [Google Scholar] [CrossRef]
- Kirmizioglu, E.; Tuydes-Yaman, H. Comprehensibility of traffic signs among urban drivers in Turkey. Accid. Anal. Prev. 2012, 45, 131–141. [Google Scholar] [CrossRef]
- Rajesh, R.; Gowri, D.R.; Suhana, N. The usability of road traffic signboards in kottayam. In Emerging Trends in Engineering, Science and Technology for Society, Energy and Environment—Proceedings of the International Conference in Emerging Trends in Engineering, Science and Technology, ICETEST 2018, Kerala, India, 18–20 January 2018; CRC Press: Boca Raton, FL, USA, 2018; pp. 323–328. [Google Scholar] [CrossRef]
Road Signs | |||||||
---|---|---|---|---|---|---|---|
Guide Signs | Warning Sign | Control Sign | Prohibitory Sign | ||||
Bait Al Hikmah | 2 | Roundabout | 1 | Do Not Enter | 1 | Speed Sign | 6 |
Airport | 1 | Pedestrian Crossing | 1 | Give Way | 1 | ||
Hospital | 1 | U-turn | 1 | ||||
University City | 1 | ||||||
Al Juraina | 1 |
Correlation Matrix for Eye-Tracking Data | ||||
---|---|---|---|---|
Y | X | |||
Y | Pearson’s r | — | ||
p-value | — | |||
Spearman’s rho | — | |||
p-value | — | |||
Kendall’s Tau B | — | |||
p-value | — | |||
X | Pearson’s r | 0.079 | *** | — |
p-value | <0.001 | — | ||
Spearman’s rho | 0.041 | *** | — | |
p-value | <0.001 | — | ||
Kendall’s Tau B | 0.029 | *** | — | |
p-value | <0.001 | — |
One Sample T-Test for Eye-Tracking | |||||||||||
Statistic | df | p | Effect Size | 95% Confidence Interval | |||||||
Lower | Upper | ||||||||||
X | Student’s t | 14.39 | 7092 | <0.001 | Cohen’s d | 0.1709 | 0.1474 | 0.19432 | |||
Y | Student’s t | −1.43 | 7179 | 0.153 | Cohen’s d | −0.0169 | −0.04 | 0.00627 | |||
Descriptive Statistics for Eye-Tracking | |||||||||||
N | Mean | Median | SD | SE | |||||||
X | 7093 | 0.04865 | 0.038 | 0.285 | 0.00338 | ||||||
Y | 7180 | −0.0044 | −0.01 | 0.261 | 0.00308 |
Paired Samples Test for the Cognitive Load of All Participants in Every Video | |||||||||
---|---|---|---|---|---|---|---|---|---|
Paired Differences | t | df | Significance | ||||||
Mean | Std. Dev. | Std. Error Mean | 95% Confidence Interval of the Difference | One-Sided p | Two-Sided p | ||||
Lower | Upper | ||||||||
Video 1 | |||||||||
Participant 1 | 139.99 | 81.61 | 4.86 | 130.43 | 149.56 | 28.81 | 281 | <0.001 | <0.001 |
Participant 2 | 139.92 | 81.54 | 4.86 | 130.36 | 149.47 | 28.82 | 281 | <0.001 | <0.001 |
Participant 3 | 139.87 | 81.54 | 4.86 | 130.31 | 149.43 | 28.81 | 281 | <0.001 | <0.001 |
Participant 4 | 140.65 | 81.86 | 4.87 | 131.07 | 150.22 | 28.91 | 282 | <0.001 | <0.001 |
Participant 7 | 137.01 | 79.9 | 4.81 | 127.54 | 146.48 | 28.49 | 275 | <0.001 | <0.001 |
Participant 8 | 140.06 | 81.6 | 4.86 | 130.49 | 149.62 | 28.83 | 281 | <0.001 | <0.001 |
Participant 9 | 142.42 | 83 | 4.9 | 132.77 | 152.06 | 29.07 | 286 | <0.001 | <0.001 |
Participant 10 | 142.06 | 82.71 | 4.9 | 132.43 | 151.68 | 29.05 | 285 | <0.001 | <0.001 |
Participant 11 | 138.43 | 80.69 | 4.84 | 128.92 | 147.94 | 28.66 | 278 | <0.001 | <0.001 |
Participant 12 | 135.44 | 78.93 | 4.78 | 126.04 | 144.85 | 28.36 | 272 | <0.001 | <0.001 |
Participant 13 | 133.46 | 77.84 | 4.75 | 124.12 | 142.8 | 28.13 | 268 | <0.001 | <0.001 |
Participant 14 | 145.43 | 84.72 | 4.95 | 135.69 | 155.17 | 29.39 | 292 | <0.001 | <0.001 |
Participant 15 | 130.91 | 76.4 | 4.71 | 121.65 | 140.17 | 27.85 | 263 | <0.001 | <0.001 |
Participant 16 | 141.44 | 82.41 | 4.89 | 131.83 | 151.04 | 28.98 | 284 | <0.001 | <0.001 |
Participant 18 | 141.37 | 82.41 | 4.89 | 131.76 | 150.98 | 28.97 | 284 | <0.001 | <0.001 |
Video 2 | |||||||||
Participant 5 | 121.47 | 70.88 | 4.53 | 112.55 | 130.39 | 26.83 | 244 | <0.001 | <0.001 |
Participant 6 | 125 | 72.9 | 4.6 | 115.96 | 134.05 | 27.23 | 251 | <0.001 | <0.001 |
Participant 8 | 110.91 | 64.83 | 4.34 | 102.37 | 119.44 | 25.61 | 223 | <0.001 | <0.001 |
Participant 11 | 111.93 | 65.38 | 4.35 | 103.36 | 120.5 | 25.74 | 225 | <0.001 | <0.001 |
Participant 12 | 110.41 | 64.51 | 4.32 | 101.9 | 118.92 | 25.57 | 222 | <0.001 | <0.001 |
Participant 13 | 125.92 | 73.5 | 4.62 | 116.84 | 135 | 27.31 | 253 | <0.001 | <0.001 |
Participant 15 | 109.85 | 64.24 | 4.32 | 101.36 | 118.35 | 25.49 | 221 | <0.001 | <0.001 |
Participant 16 | 111.95 | 65.35 | 4.35 | 103.38 | 120.51 | 25.76 | 225 | <0.001 | <0.001 |
Participant 2 | 135.54 | 78.92 | 4.78 | 126.14 | 144.94 | 28.38 | 272 | <0.001 | <0.001 |
Participant 3 | 136.02 | 79.01 | 4.79 | 126.6 | 145.43 | 28.45 | 272 | <0.001 | <0.001 |
Participant 7 | 138.04 | 80.17 | 4.82 | 128.56 | 147.52 | 28.67 | 276 | <0.001 | <0.001 |
Participant 18 | 138.06 | 80.15 | 4.82 | 128.58 | 147.54 | 28.67 | 276 | <0.001 | <0.001 |
Paired Samples Test for Heartrate of All Participants in Every Video | |||||||||
---|---|---|---|---|---|---|---|---|---|
Paired Differences | t | df | Significance | ||||||
Mean | Std. Dev. | Std. Error Mean | 95% Confidence Interval of the Difference | One-Sided p | Two-Sided p | ||||
Lower | Upper | ||||||||
Video 1 | |||||||||
Participant 1 | 66.73 | 86.83 | 11.31 | 44.11 | 89.36 | 5.91 | 58 | <0.001 | <0.001 |
Participant 2 | 60.5 | 87.26 | 11.36 | 37.76 | 83.23 | 5.33 | 58 | <0.001 | <0.001 |
Participant 3 | 143.51 | 85.93 | 11.19 | 121.12 | 165.91 | 12.83 | 58 | <0.001 | <0.001 |
Participant 4 | 75.43 | 86.93 | 11.32 | 52.78 | 98.08 | 6.67 | 58 | <0.001 | <0.001 |
Participant 7 | 75.43 | 86.93 | 11.32 | 52.78 | 98.08 | 6.67 | 58 | <0.001 | <0.001 |
Participant 8 | 73.56 | 84.57 | 11.01 | 51.53 | 95.6 | 6.69 | 58 | <0.001 | <0.001 |
Participant 9 | 52.29 | 84.68 | 11.03 | 30.23 | 74.36 | 4.75 | 58 | <0.001 | <0.001 |
Participant 10 | 101.57 | 90.45 | 11.88 | 77.79 | 125.36 | 8.56 | 57 | <0.001 | <0.001 |
Participant 11 | 118.69 | 99.23 | 13.03 | 92.6 | 144.78 | 9.11 | 57 | <0.001 | <0.001 |
Participant 12 | 52.29 | 84.68 | 11.03 | 30.23 | 74.36 | 4.75 | 58 | <0.001 | <0.001 |
Participant 13 | 73.56 | 84.57 | 11.01 | 51.53 | 95.6 | 6.69 | 58 | <0.001 | <0.001 |
Participant 14 | 70.92 | 82.61 | 10.76 | 49.39 | 92.45 | 6.6 | 58 | <0.001 | <0.001 |
Participant 15 | 124.04 | 96.58 | 12.58 | 98.87 | 149.21 | 9.87 | 58 | <0.001 | <0.001 |
Participant 16 | 42.53 | 86.89 | 11.32 | 19.89 | 65.17 | 3.76 | 58 | <0.001 | <0.001 |
Participant 18 | 67.51 | 85.36 | 11.12 | 45.27 | 89.76 | 6.08 | 58 | <0.001 | <0.001 |
Video 2 | |||||||||
Participant 5 | 35.46 | 73.16 | 10.25 | 14.88 | 56.03 | 3.47 | 50 | <0.001 | 0.001 |
Participant 6 | 54.58 | 76.19 | 10.78 | 32.93 | 76.24 | 5.07 | 49 | <0.001 | <0.001 |
Participant 8 | 47.08 | 73.6 | 10.31 | 26.38 | 67.78 | 4.57 | 50 | <0.001 | <0.001 |
Participant 11 | 113.04 | 92.74 | 12.99 | 86.96 | 139.13 | 8.71 | 50 | <0.001 | <0.001 |
Participant 12 | 105.22 | 90.23 | 12.64 | 79.84 | 130.6 | 8.33 | 50 | <0.001 | <0.001 |
Participant 13 | 84.67 | 82.93 | 11.62 | 61.35 | 108 | 7.3 | 50 | <0.001 | <0.001 |
Participant 15 | 51.46 | 71.06 | 9.95 | 31.47 | 71.44 | 5.18 | 50 | <0.001 | <0.001 |
Participant 16 | 22.42 | 76.4 | 10.7 | 0.93 | 43.9 | 2.1 | 50 | 0.021 | 0.041 |
Participant 2 | 74.12 | 90.44 | 11.78 | 50.56 | 97.69 | 6.3 | 58 | <0.001 | <0.001 |
Participant 3 | 80.99 | 88.36 | 11.41 | 58.16 | 103.81 | 7.1 | 59 | <0.001 | <0.001 |
Participant 7 | 80.89 | 95.43 | 12.22 | 56.45 | 105.33 | 6.63 | 60 | <0.001 | <0.001 |
Participant 18 | 78.84 | 96.44 | 12.35 | 54.14 | 103.54 | 6.39 | 60 | <0.001 | <0.001 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lataifeh, M.; Ahmed, N.; Elbardawil, S.; Gordani, S. Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics. Computers 2024, 13, 123. https://doi.org/10.3390/computers13050123
Lataifeh M, Ahmed N, Elbardawil S, Gordani S. Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics. Computers. 2024; 13(5):123. https://doi.org/10.3390/computers13050123
Chicago/Turabian StyleLataifeh, Mohammad, Naveed Ahmed, Shaima Elbardawil, and Somayeh Gordani. 2024. "Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics" Computers 13, no. 5: 123. https://doi.org/10.3390/computers13050123