Acta Polytechnica CTU Proceedings https://doi.org/10.14311/APP.2025.52.0069 Acta Polytechnica CTU Proceedings 52:69–76, 2025 © 2025 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague THE INFLUENCE OF ROAD MARKING VISIBILITY ON LATERAL VEHICLE POSITION AND DRIVING SPEED Ivana Kučinaa,∗, Bernard Kosoveca, Filip Jezidžićb, Marija Ferkoa,b, Darko Babića a University of Zagreb, Faculty of Transport and Traffic Sciences, Department of Traffic Signaling, Vukelićeva 4, 10 000 Zagreb, Republic of Croatia b Smart View d.o.o., Rastočka 8a/2, 10020 Zagreb, Republic of Croatia ∗ corresponding author: ivana.kucina@fpz.unizg.hr Abstract. Road markings are a vital element of horizontal signalling, forming an integral part of traffic signalling. Their role in traffic safety involves providing timely warnings, safe guidance, and necessary information to ensure the safe movement of all road users. Under conditions of reduced visibility, often due to weather (night, fog, rain, etc.), road markings are often the only guide for drivers to determine the direction of the road. Research conducted using a driving simulator demonstrated the influence of road markings on driver behaviour, specifically on lateral vehicle position and driving speed. The results indicated that drivers behave differently depending on the visibility of road markings. A total of 31 participants took part in the study, with data collected on driving speed and lateral vehicle position for three different visibility levels of road markings on a straight road and on a right and left curve. Keywords: Road markings, visibility, driving simulator, driving speed, lateral position. 1. Introduction Road markings are a crucial traffic management com- ponent, providing visual guidance for drivers and other road users. Together with traffic signs, they are one of the most cost-effective measures for improving road safety [1]. The visibility of road markings depends on several factors, including the type of road marking material, the position of the marking, the number of markings, their age, the type of road, average annual daily traf- fic (AADT), speed limits, the amount of salt and abrasive used and winter maintenance activities [2]. Additionally, the quality and quantity of glass beads play a crucial role, as they directly affect the visibility and retroreflection of the markings [3–6]. The visibility of road markings is crucial for drivers and Advanced Driver Assistance Systems (ADAS) and the development of autonomous vehicles. Research from 2017 showed that camera-based detection of road markings improves with increased visibility, specifi- cally with higher retroreflection and contrast ratios [7]. The colour of the road markings also affects their vis- ibility and detection. For example, yellow markings on concrete pavements are more difficult for ADAS systems to detect due to lower contrast than white markings [8]. The evolution of road markings has prompted nu- merous studies on selecting materials and maintenance methods and assessing the impact of these markings on driver behaviour under varying conditions. Many stud- ies have examined the influence of longitudinal road marking widths and various treatments (transverse lines, chevron markings, dragon teeth, herringbone patterns, optical circles, horizontal warning signs, etc.) on driving speed and the lateral position of vehicles. Most of these studies assessed the effect of longitu- dinal marking widths on speed reduction [9, 10] and the impact of different treatments on the reduction of driving speed [11–17]. Some research has also focused on the effect of longi- tudinal marking widths on a vehicle’s lateral position, contributing to enhanced road safety. Wider longi- tudinal markings have been shown to reduce traffic accidents [18, 19]. Optical circles [16] and red median markings [17] significantly affect lateral positioning, while herringbone patterns were found not to influence lateral position in Ariën et al. [15] study. However, Charlton [20], Awan et al. [16], and Kazemzadehazad et al. [21] reported a significant impact lateral position. However, plain double yellow centre lines showed no noticeable effect on lateral vehicle position [20]. Drivers obtain over 90% of traffic-related informa- tion through their sense of sight. Since pavement markings and traffic signs are vital sources of this information, their visibility is critical, especially dur- ing night-time and in low-visibility conditions. The research primarily focuses on determining the mini- mum visibility levels of road markings and analysing how their visibility affects driver behaviour. Previous research indicates that the minimum retroreflection needed by drivers for safe night driving in dry condi- tions ranges between 100 and 150mcd lx−1 m−2 [22– 25]. Limited research has explored the impact of visibil- ity levels at night on bus traffic safety. The visibility 69 https://doi.org/10.14311/APP.2025.52.0069 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en I. Kučina, B. Kosovec, F. Jezidžić et al. Acta Polytechnica CTU Proceedings of pavement markings does not significantly affect the frequency of night-time crashes [26, 27]. Some studies have shown that improving visibility or having lower levels of retroreflection can reduce the frequency of col- lisions in both dry [28–30] and wet conditions [31, 32]. In conditions of reduced visibility, driving speed tends to decrease, whereas in conditions of higher visibility, driving speed increases [33–35]. A recent study by Fiolić et al. [35] found no significant differences in the vehicle’s lateral position at varying levels of visibility. The paper aims to determine whether the differ- ent visibility of road markings affects the driver’s behaviour, specifically regarding speed and lateral ve- hicle position. The research is based on data collected from a driving simulator. 2. Research methodology The research was carried out using a static driving simulator from Carnetsoft B.V. The simulator setup includes a driver section consisting of a seat with pedals, a steering wheel, and a gear shifter, along with three interconnected 30-inch displays. These displays offer a combined resolution of 5760×1080 and operate at a frame rate of 30Hz. The screens provide a 210° panoramic view of the environment through six channels: left, middle, and right views, along with three mirror displays. The middle screen also presents the vehicle’s control panel, which includes the speedometer, rev counter, turn signals, lighting, fuel gauge, and other indicators. This setup enhances the realism of the driving experience. The simulation software operates on a computer running Windows 10 (64-bit), with 8GB of RAM and 4GB of storage, ensuring smooth operation of the driving simulator (Figure 1). Three driving scenarios were used in the research, each divided into two parts. The total length of the scenario is 12.5 km, with the first part covering 2.24 km and the second part 10.26 km. The average driving time in the simulator ranges from 10 to 15 minutes, depending on speed. The first part, a warm- up, lasted approximately 2-3 minutes, depending on driving speed. The second part, where the actual research takes place, consists of three stages featuring identical road elements: a straight section, a right turn, and a left turn. However, these stages differ in the visibility of the road markings, with three lev- els: poor visibility (V1), medium visibility (V2), and high visibility (V3) (Figure 2). The scenarios differ in the sequence of these stages, with varying levels of road marking visibility for each. All scenarios oc- cur on a two-way road outside urban areas, with a total roadway width of 6.50 meters (3.25 meters per driving lane). The simulation replicates night driving conditions, and all participants drove with low beams to ensure consistent conditions. The driving scenario don’t include vehicles from the opposite direction to avoid influencing the participant’s behavior. Figure 1. Driving simulator setup. Figure 2. Scenarios: a) V1 – poor visibility, b) V2 – medium visibility, c) V3 – high visibility. The participant begins driving in the first stage, where road marking visibility is at its lowest (V1). This stage starts on a 150-meter straight section, with a posted speed limit of 90 kmh−1 (sign dimensions: 60 cm, height: 1.5m, distance from edge: 1m). After- wards, the participant drives through a 500-meter flat section, where data on speed and vehicle lateral posi- tion is collected. This is followed by another 150-meter stretch with traffic signs indicating an upcoming right turn (sign dimensions: 90 cm, height: 2.3m, distance from edge: 1m) and a speed limit of 70 kmh−1 (sign dimensions: 60 cm, height: 1.5m, distance from edge: 1.5m). The right turn begins with a 50-meter transition with a 200-meter radius, leading into a 110-meter turn with a 150-meter radius, and concludes with another 50-meter section with a 200-meter radius. Sharp turn signs (dimensions: 60 cm, height: 1.5m, distance from edge: 7.5m) are placed 37, 73.5, and 110 meters before the turn begins. After that, there is a 150-meter straight section with signs indicating 70 vol. 52/2025 The influence of road marking visibility on lateral vehicle. . . an upcoming left turn and a speed limit, followed by a left turn identical to the right turn. The participant then approaches a four-way intersection where they have the right of way, transitioning to the second stage of the scenario. In this stage, road marking visibility is at a medium level (V2). The participant first navigates through the left turn, then a straight section, followed by the right turn, and finally, another intersection where they again have the right of way. In the third and final stage, where road marking visibility is at its highest (V3), the participant drives through a right turn, a left turn, and ends on a straight section, concluding the test. The exact sequence applies to the other two scenarios, with the order of the straight sections and the right and left turns being the only difference. At the start of the study, participants were provided with detailed instructions, after which they signed a consent form. Additionally, they completed a ques- tionnaire that collected personal information such as date of birth, gender, date of obtaining a driver’s license, self-assessment of driving ability, driving fre- quency, estimated kilometres driven per year, and any vision problems. Participants were also required to complete a self- assessment of their psychophysical condition before and after the driving simulation. It was explained that mild nausea might occur during the simulation, and the session should immediately stop if they ex- perienced symptoms such as headache, discomfort, or nausea for safety reasons. Participants were in- structed to drive using only the low-beam headlights. It was emphasized that the study was not assessing their driving quality, and no penalties would be im- posed for traffic violations. After the driving session, participants completed another form to assess their general condition post-simulation, aiming to identify any changes in their psychophysical state. The study involved 31 adult participants, all re- quired to have a driver’s license of at least category B. Of the 31 participants, 10 were women (32.26%) and 21 were men (67.74%). The average age of partic- ipants was 26.68 years (Min = 18.89 years, Max = 32.69 years, SD = 2.92 years), and the average driving experience was 7.03 years (Min = 0.77 years, Max = 14.66 years, SD = 3.07 years). Participants rated their driving skills on a scale from 1 to 5, with an average score of 4.06 (Min = 3, Max = 5, SD = 0.62). The estimated annual distance driven by participants averaged 12 354.84 kilometres (Min = 1 000 km, Max = 50 000 km, SD = 12 149.07 km). 3. Results The conducted analysis aimed to determine whether and to what extent different levels of road marking visibility, or the quality of road markings, affect driver behaviour. This study observed two key variables: vehicle speed and lateral position. A one-way ANOVA was conducted to analyse the impact of visibility on these variables across three distinct sections of the road. A comparison was made between the impact of different levels of visibility of road markings on certain road sections on the driving speed and lateral position. 3.1. Descriptives The descriptive statistics for speed and lateral posi- tion across different visibility levels and road sections provide detailed insights into driver behaviour in the driving simulator study. The descriptive statistics analysis for the left curve section shows that the mean speed for different levels of road marking visibility (poor, medium, high) re- mains relatively consistent, ranging from 25.574m s−1 to 26.243m s−1. The standard deviation of speed is also stable across visibility conditions, indicating similar variability in driving speed under all three conditions. For the lateral position on the left curve, the means are close to zero for all visibility levels, suggesting that drivers, on average, maintained their position near the centre of the lane. However, the stan- dard deviation of lateral position, which ranges from 0.379m to 0.419m, indicates a noticeable variation in how consistently drivers maintained their position. Poor road marking visibility has a slightly higher stan- dard deviation (0.419m) than medium (0.379m) and high (0.393m) visibility conditions, implying greater deviation from the centreline under poor visibility. In the right curve section, speed remained sta- ble, with mean values between 25.632m s−1 and 25.694m s−1 across different levels of road marking visibility. The standard deviation for speed was again similar, indicating no significant change in speed vari- ability based on the visibility of road markings. How- ever, the lateral position shows more variation in stan- dard deviation, especially in poor visibility (0.429m). This suggests that drivers had considerable variabil- ity in maintaining their lateral position under poor visibility conditions. In the straight road section, speeds were slightly higher, with the mean ranging from 26.598m s−1 to 27.037 m s−1. The standard deviation for speed re- mains consistent across visibility levels. For lateral position, the standard deviation under poor visibility was 0.333m, increasing to 0.382m under high visibil- ity. This indicates slightly more variability in lateral position under high visibility conditions, potentially due to drivers feeling more confident and adjusting their position more frequently. The following multiple comparisons were crucial to understanding whether these observed mean dif- ferences were statistically significant across different levels of road marking visibility. Given that road marking visibility impacts both speed and lateral po- sition, conducting post-hoc tests to identify specific pairs of visibility conditions that differed significantly was important. These comparisons help clarify road 71 I. Kučina, B. Kosovec, F. Jezidžić et al. Acta Polytechnica CTU Proceedings Road Variable Visibility N Mean Std. Deviation Std. Error Minimum Maximum section Poor 12,126 25.574 5.757 0.052 14.490 37.657 Speed Medium 11,858 26.168 5.841 0.054 12.764 37.653 [m s−1] High 11,821 26.243 5.621 0.052 16.706 37.660 Left Total 35,805 25.992 5.748 0.030 12.764 37.660 curve Poor 12,126 0.107 0.393 0.004 -1.238 1.434 Lateral Medium 11,858 0.115 0.379 0.003 -1.433 1.232 position [m] High 11,821 0.090 0.419 0.004 -0.906 1.485 Total 35,805 0.104 0.397 0.002 -1.433 1.485 Poor 33,139 25.632 5.291 0.029 12.032 37.663 Speed Medium 33,061 25.694 5.124 0.028 16.070 37.663 [m s−1] High 33,130 25.641 5.243 0.029 15.249 37.661 Right Total 99,330 25.656 5.220 0.017 12.032 37.663 curve Poor 33,139 0.028 0.429 0.002 -2.193 2.834 Lateral Medium 33,061 0.012 0.400 0.002 -1.201 1.983 position [m] High 33,130 0.019 0.408 0.002 -2.426 2.084 Total 99,330 0.019 0.412 0.001 -2.426 2.834 Poor 18,619 26.598 4.614 0.034 17.572 37.662 Speed Medium 18,312 27.037 4.706 0.035 18.543 37.626 [m s−1] High 18,342 27.008 4.722 0.035 16.402 37.627 Straight Total 55,273 26.880 4.685 0.020 16.402 37.662 road Poor 18,620 0.109 0.333 0.002 -1.157 2.091 Lateral Medium 18,312 0.078 0.326 0.002 -0.884 1.082 position [m] High 18,342 0.092 0.382 0.003 -1.163 1.324 Total 55,274 0.093 0.348 0.001 -1.163 2.091 Table 1. Descriptive statistics. marking quality’s nuanced effects on driving behaviour across various road sections. 3.2. Left curve Levene’s Test of Homogeneity of Variances for both speed and lateral position showed that the assumption of equal variances is violated for the left curve section, as all the p-values are less than 0.001. Because of that, we used Welch’s ANOVA and Brown-Forsythe tests to determine whether there was a significant difference between the observed groups. Both the Welch and Brown-Forsythe tests were significant (p < 0.001), indicating a statistically signif- icant difference in speed across the three levels of road marking visibility on the left curve. This section’s road marking visibility levels (low, medium, high) affect ve- hicle speed significantly. Similarly, both tests showed a statistically significant difference in lateral position across the visibility levels (p < 0.001), meaning that road marking quality affects vehicle lateral position significantly on the left curve. Next, we proceeded with multiple comparisons using the Games-Howell post hoc test. The Games-Howell post hoc test results revealed significant speed and lateral position differences across road marking visibility levels. For speed, both poor and medium visibility resulted in significantly lower speeds compared to high visibility of road markings (MD = −0.593ms−1 and −0.669ms−1, respectively, both p < 0.001). However, no significant difference was found between medium and high visibility (p = 0.568). For lateral position, significant differences were ob- served between poor and high visibility of road mark- ings (MD = 0.017m, p = 0.003) and between medium and high visibility (MD = 0.025m, p < 0.001). These results indicate that vehicles tend to deviate less from the centre in high visibility conditions compared to poor visibility. No significant differences were found between poor and medium visibility for lateral posi- tion (p = 0.303). 3.3. Right curve Levene’s Test of Homogeneity of Variances for the right curve section revealed different speed and lateral position results. For speed, the test showed significant results (p < 0.001), indicating that the assumption of equal variances is violated across the visibility levels. In contrast, the p-values were greater than 0.05 for the lateral position, suggesting that the assumption of homogeneity of variances is met. Based on these results, we used Welch’s ANOVA to compare speeds and standard ANOVA to compare lateral positions. The Robust Tests of Equality of Means for speed on the right curve yielded the following results: Welch Statistic = 1.371, with p = 0.254 and Brown-Forsythe Statistic = 1.349, with p = 0.260. Both tests indicated no statistically significant difference in speed across 72 vol. 52/2025 The influence of road marking visibility on lateral vehicle. . . Dependent Visibility (I) Visibility (J) Mean Difference (I–J) Std. Error Sig. Variable Speed Poor Medium -0.593* 0.075 < 0.000 [m s−1] High -0.669* 0.074 < 0.000 Medium High -0.076 0.074 0.568 Lateral Poor Medium -0.007 0.005 0.303 position [m] High 0.017* 0.005 0.003 Medium High 0.025* 0.005 < 0.000 * The mean difference is significant at the 0.05 level. Table 2. Multiple comparisons (Games-Howell post hoc tests) for the left curve. Dependent Visibility (I) Visibility (J) Mean Difference (I–J) Std. Error Sig. Variable Lateral Poor Medium 0.016* 0.003 < 0.000 position [m] High 0.009* 0.003 0.013 Medium High -0.007 0.003 0.066 * The mean difference is significant at the 0.05 level. Table 3. Multiple comparisons (Tukey HSD post hoc tests) for the right curve. the three visibility levels (poor, medium, and high) since the p-values are greater than 0.05. The results suggest that variations in road marking visibility do not significantly influence vehicle speed on the right curve. Since the result was not significant, there is no need for post hoc comparisons for speed on the right curve. The results of the ANOVA for lateral position on the right curve revealed a statistically significant dif- ference across the three visibility levels, with an F- statistic of 12.895 and a p-value of less than 0.001. This indicates that road marking quality impacts how vehicles position themselves laterally on this road sec- tion. Given the significance of these findings, further post-hoc analysis was conducted to identify which specific visibility levels (poor, medium, high) differ from each other in terms of lateral positioning. The Tukey HSD post hoc analysis for lateral posi- tion revealed significant differences between the levels of road marking visibility. Specifically, vehicles in the poor road marking visibility condition exhibited a sig- nificantly greater lateral position compared to those in the medium (MD = 0.016m, p < 0.001) and the high road marking visibility condition (MD = 0.009m, p = 0.013). Conversely, there was no significant difference in lateral position between medium and high road marking visibility (MD = −0.007m, p = 0.066). 3.4. Straight road Levene’s Test of Homogeneity of Variances results for the straight road section indicated significant viola- tions of the assumption for both speed and lateral position, with p-values less than 0.001, suggesting that the variances are unequal across the visibility levels for both variables. Consequently, Welch’s ANOVA was utilized to compare means and determine the dif- ferences in speed and lateral position across the three road marking visibility conditions. The Robust Tests of Equality of Means results for the straight road section revealed significant differ- ences in speed and lateral position across the three lev- els of road marking visibility. Specifically, the Welch statistic for speed was 51.611 (p < 0.001), and for lateral position, it was 42.709 (p < 0.001). These find- ings indicate that variations in road marking visibility significantly influence vehicle speed and lateral posi- tioning. Consequently, post hoc analyses are necessary to determine the differences between the visibility lev- els. The Games-Howell post-hoc analysis for the straight road section revealed significant differences in both speed and lateral position across the visibility levels. For speed, vehicles in the poor road marking visi- bility condition drove significantly slower than those in the medium (MD = −0.438 kmh−1, p < 0.001) and high road marking visibility conditions (MD = −0.410 kmh−1, p < 0.001). There was no significant difference in speed between medium and high visibility (p = 0.832). In terms of lateral position, vehicles in the poor road marking visibility condition showed a signif- icantly greater lateral position movement compared to both medium (MD = 0.032m, p < 0.001) and high road marking visibility conditions (MD = 0.018m, p < 0.001). Additionally, vehicles in the medium road marking visibility condition also had a significantly greater lateral position movement than those in the high visibility condition (MD = −0.014m, p < 0.001). 73 I. Kučina, B. Kosovec, F. Jezidžić et al. Acta Polytechnica CTU Proceedings Dependent Visibility (I) Visibility (J) Mean Difference (I–J) Std. Error Sig. Variable Speed Poor Medium -0.438* 0.049 < 0.000 [m s−1] High -0.410* 0.049 < 0.000 Medium High 0.028 0.049 0.832 Lateral Poor Medium 0.032* 0.003 < 0.000 position [m] High 0.018* 0.004 < 0.000 Medium High -0.014* 0.004 < 0.000 * The mean difference is significant at the 0.05 level. Table 4. Multiple comparisons (Games-Howell post hoc tests) for the left curve. 4. Discussion This study investigated how different levels of road marking visibility impact driver behaviour, mainly fo- cusing on speed and lateral position, across three dis- tinct road sections: left curve, right curve, and straight road. The findings reveal several notable trends in how road marking visibility influences driving perfor- mance, contributing to the broader understanding of road safety and driver behaviour in varying visibility conditions. 4.1. Speed and road marking visibility Across all road sections, the analysis revealed that speed was generally higher in the straight road section compared to the left and right curves, regardless of visibility level. In particular, the straight road saw the highest average speed under medium visibility conditions (27.037m s−1). Interestingly, in the left curve section, while differences in speed between poor, medium, and high visibility were statistically signif- icant, these differences were relatively small, rang- ing from 25.574m s−1 to 26.243m s−1. The Games- Howell post hoc test confirmed that speeds in the poor and medium visibility conditions were significantly lower than in the high visibility condition (MD = −0.593ms−1 and MD = −0.669ms−1, respectively), suggesting that improved road marking visibility en- courages drivers to maintain slightly higher speeds on curves. A similar finding regarding reduced driv- ing speeds in low-visibility conditions and increased speeds in better visibility was observed in previous studies [33–35]. The speed remained consistent across all visibility levels on the right curve, with no statistically sig- nificant differences. This finding suggests that the geometric challenge of the right-directed curve may substantially influence driving speed more than road marking visibility, leading drivers to adopt more cau- tious speeds irrespective of visibility. For the straight road, visibility had a more pro- nounced impact on speed. Vehicles in the poor road marking visibility conditions drove significantly slower than those in both the medium (MD = −0.438ms−1) and high visibility conditions (MD = −0.410ms−1). Higher driving speeds in response to better road marking visibility are attributed to the driver’s in- creased sense of security. With greater detection dis- tances, drivers can follow the road more clearly and prepare for upcoming situations. This is supported by research showing that increasing the retroreflec- tivity of road markings improves detection distance. For example, road markings with a retroreflection of 100mcd lx−1 m−2 are visible from 91.44 meters away [23, 36]. However, there was no significant dif- ference in speed between medium and high visibility, indicating that further improvements may not sig- nificantly alter driving speed once visibility reaches a medium threshold. 4.2. Lateral position and road marking visibility Lateral position, which indicates how consistently drivers maintained their position within the lane, also showed significant variability based on road marking visibility. In the left curve section, road marking vis- ibility had a significant effect, with vehicles in high visibility conditions deviating less from the centreline than those in poor and medium road marking visibility conditions (MD = 0.017m and MD = 0.025m, respec- tively). These results suggest that higher-quality road markings help drivers maintain a more consistent lane position on curves, potentially reducing the risk of lane departures. Similarly, lateral position varied significantly on the right curve between visibility conditions. Vehicles in poor visibility had a more significant lateral deviation than those in medium and high visibility conditions (MD = 0.016m and MD = 0.009m, respectively), indicating that poor road markings may cause drivers to struggle to maintain the lane position, particularly in curved sections. On the straight road, the pattern was similar, with significant differences in lateral position between poor, medium, and high visibility conditions. Drivers in poor road marking visibility conditions exhibited the greatest lateral deviation, while those in high visibil- ity conditions maintained the most consistent lane position. The medium visibility condition also showed 74 vol. 52/2025 The influence of road marking visibility on lateral vehicle. . . more significant lateral deviation than the high visibil- ity condition, supporting the statement that improved road markings lead to better lateral control, even on straight road sections. Previous studies also confirm the fact that the vis- ibility of road markings impacts a vehicle’s lateral position. Specifically, reduced visibility of these mark- ings results in a more erratic lateral position, both on straight sections and in curves [37, 38]. However, since different road marking treatments (transverse lines, dragon teeth, herringbone patterns, optical cir- cles, etc.) do affect a vehicle’s lateral position in curves [14–17]. Further research is needed to explore how the visibility of various road markings influences a vehicle’s positioning within the traffic lane. 5. Conclusions The visibility of road markings is a crucial factor in traffic safety, as over 90% of information is obtained through vision. Research has shown that visibility impacts driver behavior. The results of the study can help experts and practitioners in understanding how markings affect drivers in night-time conditions and in making decisions related to the implementation of particular materials for markings, as well as for planning the maintenance activities. Therefore, it is necessary to properly maintain markings and signs in order to maintain an adequate level of visibility and thus ensure of increasing road safety. Road mark- ings must be properly and consistently maintained to ensure adequate visibility. One limitation of the study is that it was conducted using a driving simulator, which cannot fully replicate the complexity of real-world driving. Additionally, the number and age of participants may have influenced the results, presenting another limitation. Future research should also consider various weather conditions, such as rain, fog, and snow, during night- time, and include a larger pool of participants. The findings from the driving simulator should be com- pared with data from studies conducted under real road conditions, where sections of the road would fea- ture markings with varying levels of visibility (high, medium, and low). Acknowledgements References [1] D. Babić, M. Fiolić, D. Babić, T. Gates. Road markings and their impact on driver behaviour and road safety: A systematic review of current findings. Journal of Advanced Transportation 2020(1):7843743, 2020. https://doi.org/10.1155/2020/7843743 [2] K. Shahata, H. Fares, T. Zayed, et al. 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Transportation Research Record 2676(12):691–702, 2022. https://doi.org/10.1177/03611981221097095 76 https://doi.org/10.1080/15389588.2018.1532568 https://doi.org/10.1016/j.aap.2021.106013 https://doi.org/10.1016/j.aap.2012.01.028 https://doi.org/10.1016/j.aap.2020.105527 https://doi.org/10.1016/j.aap.2006.12.007 https://doi.org/10.1016/j.trf.2019.03.002 https://doi.org/10.1177/0361198196152900108 https://doi.org/10.3141/1605-08 https://doi.org/10.3141/1715-09 https://doi.org/10.3141/1824-14 https://doi.org/10.17226/23255 https://doi.org/10.3141/2056-03 https://doi.org/10.3141/2337-08 https://doi.org/10.1061/(ASCE)TE.1943-5436.0000863 https://doi.org/10.1061/(ASCE)TE.1943-5436.0000863 https://doi.org/10.1016/j.aap.2019.105271 https://doi.org/10.1016/j.jsr.2014.02.011 https://doi.org/10.1016/j.trf.2023.10.025 https://doi.org/10.1155/2020/7843743 https://doi.org/10.1186/s12544-020-00425-7 https://doi.org/10.1177/03611981221097095 Acta Polytechnica CTU Proceedings 52:69–76, 2025 1 Introduction 2 Research methodology 3 Results 3.1 Descriptives 3.2 Left curve 3.3 Right curve 3.4 Straight road 4 Discussion 4.1 Speed and road marking visibility 4.2 Lateral position and road marking visibility 5 Conclusions Acknowledgements References