Journal of Emerging Trends in Computing and Information Sciences

Transcription

1 Drivers Reading Time Model on Variable Message Signs Using Korean Characters 1 Taehyung Kim, 2 Taehyeong Kim *, 3 Cheol Oh, 4 Bum-Jin Park, 5 Hyoungsoo Kim 1 Research Fellow, The Korea Transport Institute, Korea 2 Senior Researcher, Korea Institute of Civil Engineering and Building Technology, Korea (*corresponding author) 3 Associate Professor, Hanyang University, Korea 4 Senior Researcher, Korea Institute of Civil Engineering and Building Technology, Korea 5 Research Fellow, Korea Institute of Civil Engineering and Building Technology, Korea ABSTRACT Variable message sign (VMS) which is used for providing real-time information on traffic conditions and accident occurrences is an important component of intelligent transport systems (ITS). Proper consideration of drivers message reading time is necessary for designing VMS message phase and duration. The drivers message reading time depends on various factors, such as the number of information units, the number of lines, character height, drivers travel speed, and driver characteristics. In this study, a portable VMS (PVMS) using Korean characters was specially manufactured for the study of VMS message reading time, which has three lines with eight characters per line. Various field experiments were conducted using this portable VMS to obtain drivers message reading time with respect to the above causal factors. The relationships between the drivers message reading time and various causal factors were statistically tested by ANOVA. Also, regression modeling techniques were applied to develop an estimation model for drivers message reading time. By the final established model, it is shown that the message reading time is affected by the number of information units, drivers travel speed, gender and age group. Keywords: VMS, ATIS, Message reading time, Regression model, ANOVA 1. INTRODUCTION The need to provide drivers with real-time information has spawned a drastic increase in the use of variable message signs (VMSs). Variable message sign which is used for providing real-time information on traffic conditions and accident occurrences is an important component of intelligent transport systems (ITS). Many studies have been conducted to develop guidance for transportation officials and field personnel to instruct them on how to effectively use VMSs based on the type of situation, goal of the message, and predominant travel speed. It is essential that the scheme of the VMS message phase and duration be designed with consideration of the drivers message reading time. Reading time will depend on various factors, such as the number of information units, the number of message lines, character height, drivers travel speed, and driver characteristics. However, there have been not many efforts to explore the relationship between the drivers message reading time and the above causal factors. In this study, a portable VMS (PVMS) was specially manufactured for the study of VMS message reading time, which has three lines with eight Korean characters per line. Various field experiments were conducted using this portable VMS to obtain drivers message reading time with respect to the number of information units, the number of message lines, character height, drivers travel speed, and driver characteristics such as gender or age. The relationships between the drivers message reading time and various causal factors were statistically tested by ANOVA. Also, regression modeling techniques were applied to develop an estimation model for drivers message reading time. The proposed methodology and model are expected to be useful in designing proper VMS message phase and duration. 2. LITERATURE REVIEW There have been a variety of research and experiments that studied driver reaction and behavior around VMS, either in a laboratory environment or on roadways. Driving simulators have been widely utilized to conduct research related to message expression type, duration, formation and expression operations on VMS. Dudek et al. [1] used a driving simulator to analyze the effects of three message display patterns: 1) flashing an entire one-frame message, 2) flashing one line of a one-frame message, and 3) alternating text on one line of a three-line VMS while keeping the other two lines of text the same. A total of 64 drivers participated in the experiment. The driving simulator generated useful data, such as acceleration noise and headways, which were used as measures of effectiveness in the study. They concluded that there were no significant differences in average reading time and understanding between flashing messages and static messages. In addition, they reported that the flashing messages may have adverse effects on message comprehension for unfamiliar drivers. Dutta et al. [2] investigated factors affecting the readability and comprehension of multiple phase messages using a driving simulator. They suggested optimal message display types that were intended not to interrupt the driving tasks of drivers. A total of 48 participants were instructed to drive a car to a destination following messages displayed on the VMS in a driving simulator. Response times and task successes were recorded and analyzed. The authors suggested that VMS messages should be displayed at a rate of 0.5 seconds per word in a phase as an optimum message display 632

2 time. Yang et al. [3] researched factors affecting VMS message comprehension, such as number of phases, flashing effects, color, combinations of colors, usage of abbreviations, age and gender, using a driving simulator and questionnaire. Study results suggested that VMS messages should be static and one-framed with specific wording and no abbreviations. They also found that amber or green or amber-green combinations are the most favored colors. Cohn et al. [4] applied computer simulation and investigated the effects of "off-frame" time, message alignment and the usage of abbreviation. They found 0.3 seconds of off-frame time is the most effective time for message delivery. They also found that left alignment or staggered alignment in the case of three rows (left, middle and right alignment for the first, second and third rows, respectively) are more effective formats than middle message alignment. A study on the message display formats of portable variable message signs was conducted by Wang and Cao [5]. Through a series of computer simulation experiments, the authors investigated influences of the interaction between display format, number of message lines, and driving lanes. Drivers were asked to make responses signaling comprehension of the VMS messages. It was found that messages displayed all at once took less response time than sequentially displayed messages. Single line messages were better than multiple-line messages. Drivers could better view portable VMSs when they were driving in the outer lane. It was also found that older drivers exhibited slower response and less accuracy than younger drivers, and woman drivers exhibited slower response but higher accuracy than men drivers. Hustad et al. [6] conducted human factors laboratory studies to evaluate abbreviations currently used on changeable message signs (CMSs) in New Jersey and developed new abbreviations specific to the needs of New Jersey transportation agencies. In the study, a list of abbreviations was given to drivers and they were asked to write down the meanings. The results revealed that 18 of the abbreviations were quite well understood by 85 percent of the New Jersey drivers surveyed. They also found that some of the abbreviations were considered unacceptable and there were regional differences with respect to driver comprehension of some of the abbreviations. Ullman et al. [7] conducted a legibility study of 9-in. and 10.6-in. letters on changeable message signs with lightemitting diodes. The 60 participants drove a test vehicle as they approached changeable message signs with one of the above letter heights. The distance from the sign at which the participant could correctly read a three-letter word was recorded. The 85th percentile legibility distance for the 9-in. letter height was 228 ft. for daytime conditions and 114 ft. for nighttime conditions. For the 10.6-in. letter height, the 85th percentile legibility distances were 324 ft. for daytime and 203 ft. for nighttime conditions, respectively. Ullman et al. [8] investigated how graphic and symbol displays can improve or assist communication with drivers. Through three human factors evaluation of alternative designs, researchers identified specific design elements that should or should not be used in graphic displays. Additionally, some of the key benefits were identified for the use of graphic displays as compared to equivalent text messages. First, a graphic display appears to improve the ability of drivers to identify available lanes in a problem area. Second, the delivery of incident descriptor information (e.g. accidents or work zones) through the use of graphic symbols improves comprehension levels of non-native-language drivers (e.g. a driver whose primary language is Spanish). Third, the viewing time required for comprehension by a non-native speaker may be shortened as a result of the use of graphics and symbols. Lai [9] conducted an ergonomic study on the message design of Chinese variable message signs on urban roads in Taiwan. Effects of color scheme and number of message lines of VMS on participants response were investigated through a laboratory experiment. A total of 30 university students participated in the experiment and each participant went through a total of 84 randomized VMS presentations in each test block. Two successive test blocks were conducted for each participant. The results showed color scheme and number of message lines are significant factor for participants response time to VMS. Messina et al. [10] assessed advisory message associated with three driving advisory conditions(dacs), Merge to the Right Lane, Zip Merge, and Continue Travel Normally through a questionnaire survey and driving simulation to seek the best message in advising drivers in different traffic conditions when approaching work zones. To identify participants preferences towards a series of messages posted on variable message signs, a questionnaire survey was first deployed. A total of 81 subjects participated in the survey. The effectiveness of several top rated message identified in the survey was further assessed through a driving simulation. Subjects were asked to verbally respond with a number when they identified a message, denoting the DAC associated with that message. As reviewed above, there have been many studies that used driving simulators or laboratory experiments to determine the effect of VMS message operations on human factors. However, there have been not many attempts to study drivers message reading time through field experiments, which is essential for the development of a VMS message phase and duration scheme. Therefore, the purpose of this study was to develop an estimation model for VMS message reading time that would be dependent on various causal factors, such as the number of information units, the number of message lines, character height, drivers travel speed, and driver characteristics such as gender or age. 3. EXPERIMENT DESIGN A variable message sign was specially manufactured for the study of VMS message reading time (see Fig 1). 633

3 (a) PVMS for experiment (b) Size of PVMS(unit: mm) Fig 1: Portable VMS A PVMS was designed having three lines with eight Korean characters per line. The character colors included red, green, and amber which were used for indicating congestion/accident information, direction sign, and general information, respectively as a usual way of color usage for VMS in Korea. A 500-meter road tangent segment having 4 lanes (2 lanes in each direction) was selected to conduct field experiments at the Hanyang University in Korea (see Fig 2). Fig 2: Experiment site Also, the field experiments were conducted under real road situations without any lane closure in both directions. Field experiment equipment was composed of PVMS, DGPS (PDA, GPS, and Satellite Antenna), a vehicle, and other support equipment as shown in Fig 3. (b) Equipped vehicle Fig 3: Experiment equipment and equipped vehicle The operator in the field used an application program to control the PVMS messages with regard to the number of information unit, number of lines, and character height. A participant (driver) drove the experiment site in an equipped vehicle with a surveyor. The reading time was measured as follows; when the participant identified the sign during driving, the participant pressed the stopwatch to tell the identifying of PVMS characters and the surveyor read the time. Again, the participant pressed the stopwatch to tell the completeness of reading a displayed message on PVMS and the surveyor recorded the completed time before performing the following scenarios. Thus, the participant s message reading time is defined as the time from when the participant identified legible PVMS characters to when the participant completed reading a displayed message. Field experiments were conducted during the daytime from January 23rd to February 1st in We recruited volunteers for field experiments and a total of 61 subjects participated in the VMS reading time experiments. Each participant had a valid Korea driver s license and actual driving experience of more than a year. There were 48 male participants and 13 female participants. More detailed characteristics of the participants are shown in Fig 4. (a) Gender (a) Experiment equipment 634

4 Table 1: The set of scenarios (a) The set of scenarios (1-11). (b) (c) Driving experience Fig 4: Characteristics of participants Various causal factors, such as information unit of VMS messages, number of lines, character height, drivers travel speed, and driver characteristics such as gender or age were assumed to significantly affect the drivers message reading time. The conditions and parameters used to construct the experiment scenarios varied as follows: 1) Information unit of VMS messages: 2 to 8 units 2) Number of lines: 2 or 3 lines 3) Character height: 20, 30, 40, or 50 cm 4) Travel speed: 10 km/h to 80 km/h 5) Driver characteristics: gender and age Field experiments conducted 22 scenarios varying the height of message character and the amount of displayed information. A participant drove the experiment site iteratively for measuring a driver s response to each scenario. Table 1 presents all scenarios and one of scenarios is randomly displayed for each experiment. Information unit represents a minimum unit of message which has an independent meaning. For example, if a message displayed on VMS is Nodeul-Road Sungsan Han-River 7min, as shown in the scenario 5 in Table 1, the message would have four information units; Nodeul-Road (road name), Sungsan (origin), Han-River (destination) and 7min (travel time). 635

5 (b) The set of scenarios (12-22). (b) By gender (c) By age group 4. ANALYSIS AND MODEL DEVELOPMENT 4.1 Statistical analysis This section shows a summary of baseline statistics to characterize the data collected through the VMS reading time experiments as shown Fig 5 and Table 2. (d) By character height group (a) All collected data (e) By speed range group Fig 5: Information units vs. message reading time Table 2: ANOVA results (I) (a) ANOVA table for message reading times by information unit Squares Square

6 (b) ANOVA table for message reading times by gender Squares Square (c) ANOVA table for message reading times by age group Squares Square (d) ANOVA table for message reading times by character height Squares Square (e) ANOVA table for message reading times by speed range Squares Square In this figure, the values represent the mean message reading time are mean. Fig 5(a) illustrates the relationship between the number of information units and message reading time. As could be expected, there is a clear trend that the message reading time increases as the number of information units increases. What is interesting from Fig 5(a) is that the message reading time increases drastically between four information units and five information units. The reason for this could be that it takes more time for drivers to read the messages on VMS with the change of line (i.e., two lines to three lines) rather than the change in the number of information units in the same line. This result indicates that considerable care must be taken in determining a message reading time for the scheme of VMS message phase and duration. Also, ANOVA using SPSS 19 was performed to show whether or not the means of the message reading times among information units are significantly different like Table 2 (a). It is shown that the message reading times among information units are significantly different at a 95 % confidence level. Fig 5(b) shows the difference of message reading time between male and female. It should be noted that there is a difference in the sample size between men (48 people) and women (13 people). It seems visually that there are no significant differences in the means of the message reading times between male and female, except perhaps for the information unit ranges from 2 to 3. Also, Table 2(b) shows the result of ANOVA that was performed to show whether or not the means of the message reading times between men and women are significantly different. It is shown that the message reading times between men and women are significantly different at a 95 % confidence level. Fig 5(c) shows the difference in message reading time by age group along information units. It seems visually that there are significant variations in the means of message reading times across age groups. This finding indicates that there may be differences in drivers understanding of the messages displayed on VMS. It is difficult to find any general trend among the age groups, but it is evident that the message reading time of the age group is relatively longer than the other age groups along the whole information unit range. Also, ANOVA was performed to show whether or not the means of the message reading times among age groups are significantly different like Table 2(c). The result of ANOVA shows that the means of message reading times among age groups are significantly different at a 95 % confidence level. Fig 5 (d) shows the difference of message reading time by character height along information units. It seems visually that there are significant variations in the means of the message reading times within same information unit by the character height. This finding indicates that there may be differences in drivers message reading time according to the character height, although it is difficult to find any general pattern with respect to the number of information units. Thus, we performed ANOVA to show whether or not the means of the message reading times among character height groups are significantly different like Table 2 (d). As expected, ANOVA result shows that the message reading times among character height groups are significantly different at a 95 % confidence level. Fig 5 (e) shows the difference of message reading time by speed range along information units. It seems visually that there are significant differences in the means of the message reading times within same information unit by speed range for the information unit ranges from 2 to 4. ANOVA was performed to show whether or not the means of the message reading times among speed range groups are significantly different like Table 2 (e). By the result of ANOVA, as expected, it is shown that the message reading times among speed range groups are significantly different at a 95 % confidence level. 637

7 4.2 Model development A multiple regression modeling approach was used to establish a functional relationship between VMS message reading time and several key independent factors, such as the number of information units, number of lines, character height, drivers travel speed, and driver characteristics such as gender and age. Several categorical variables, such as number of message lines (e.g., 2 lines or 3 lines), gender (e.g., female or male) and age group (e.g., 20-30, 30-40, 40-50, or 50-60), were represented as dummy variables (e.g., 0, 1, etc.). The following variables were selected as potential independent factors for estimating VMS message reading time: 1) Number of information units 2) Number of message lines 3) Character height 4) Travel speed 5) Gender (female = 0, male = 1) 6) group 1 (20-30 =1, otherwise =0) 7) group 2 (30-40 = 1, otherwise = 0) 8) group 3 (40-50 = 1, otherwise = 0) The results from the multiple regression analysis using SPSS 19 are summarized in Table 3. Information Units, Number of Lines, Travel Speed, Gender, Group 1, Group 2 and Group 3 were identified as statistically significant independent variables at a 95% confidence level. Table 3: Results of multiple regressions (I) Variable Coefficient Standard Error t-stat P-value Constant Information Units Number of Lines Travel Speed Gender Group Group Group R-square: 0.349, Adj. R-square: Table 4 shows the correlation matrix between the independent variables. We notice that the information unit and the number of lines are highly correlated with VMS message reading time. Moreover, it shows that those two variables are highly correlated with each other. Also, information unit and character height are highly correlated with each other. Table 4: Correlation matrix table between the independent variables Reading Time Information Units Number of Lines Character Height Travel Speed Gender Group 1 Group 2 Group 3 Reading Time Information Units ** Number of Lines ** ** Character Height ** ** Travel Speed ** * Gender ** ** Group ** ** ** Group ** ** Group * ** ** ** * **: correlation is significant at the 0.01 level (2-tailed) *: correlation is significant at the 0.05 level (2-tailed) Stepwise addition and subtraction methods were used to refine the variable set. Since the number of information units and the number of lines are highly correlated with each other, further analysis was conducted to identify which variable is a more valuable predictor of VMS message reading time. When adjusted R-square values were compared with the individual regression models, it was found that the number of information units is a more valuable predictor than the number of lines. Also, character height was removed from the variable set by the same way. Tables 5 and 6 summarize the final results of the multiple regression analysis and ANOVA without Number of lines and Character height. Table 5: Results of multiple regressions (II) Variable Coefficient Standard Error t-stat P-value Constant Information Unit Gender Travel Speed Group Group Group R-square: 0.326, Adj. R-square: Table 6: ANOVA table for the regression model Squares Square Regression Residual As a goodness-of-fit of the model, adjusted R-square is and the value of F statistic (F*) is which shows that the model is significant at 0.05 level. As can be expected, message reading time increases as the number of information units and/or age group increases. However, the coefficient of travel speed turned out to be a negative value, indicating that the message reading time decreases slightly as travel speed increases. The reason could be that at high speeds drivers are more careful and cautious to any change in the surrounding environment. Hence, they react very promptly to the provision of a VMS message, resulting in decreased message reading time. Also, it is shown that message reading time of male is shorter than that of female. The reason could be that the response time of male driver is faster than that of female driver. The coefficients of age groups are bigger than 638

8 those of other variables. This indicates that the effect of age on message reading time is larger than those of other factors. Especially, message reading time of age group is much longer than those of the others group. is based on PVMS message with 20 to 50 cm of character height in Korean language. Furthermore, the model cannot explain drivers reading time during night time or in a rainy day. The final established model used only the statistically significant variables and is expressed as the following equation: VMS message reading time (sec.) = x 0.020y 0.308z 1.633A A A3 where x = number of information units y = travel speed (km/h) z = gender (Female: 0, male: 1) A1= age group 1 (20-30: 1, otherwise: 0) A2= age group 2 (30-40: 1, otherwise: 0) A3= age group 3 (40-50: 1, otherwise: 0) 5. CONCLUSIONS A variety of ITS projects have been implemented to alleviate traffic congestion and enhance driving conditions. Advanced traffic information systems (ATIS), which is a product of such ITS projects, provides useful information to users to support their route selection decision. VMS is an important component in a driver s decision process in that it can convey real-time traffic information, such as traffic congestion, incident occurrence, and detours. The purpose of this study was to propose a methodology and a model for determining drivers VMS message reading time, which is essential for the design of any VMS message phase and duration scheme. This study conducted various field experiments using specially manufactured portable VMS to obtain drivers message reading time with respect to the number of information units, the number of message lines, character height, drivers travel speed, and driver characteristics such as gender and age. A 500-meter road tangent segment having 4 lanes (2 lanes for each direction) was selected to conduct field experiments at the Hanyang University in Korea. The relationships between the drivers message reading time and various causal factors were statistically tested by ANOVA. Also, regression modeling techniques were applied to develop an estimation model for drivers message reading time. By the final established model, it is shown that the message reading time is affected by the number of information units, drivers travel speed, gender and age group. The proposed methodology for determining VMS message reading time would be greatly useful for ITS designers and planners in the decision making process for proper VMS message phase and duration. In the field, it is not easy to apply message display time of PVMS by considering driver s age and the speed of the individual vehicle. However, it is possible to develop the model of the reading time required considering information units and 85th percentile speed instead of individual vehicle s speed as the final model applied in the field. A significant contribution of this study was a novel approach to determine VMS message reading time through field experiments using the amount of message displayed on the VMS, travel speed, gender and age group. However, it should be noted that there are some limitations of the experiment and the estimated model in this paper. The model ACKNOWLEDGEMENTS This research was supported by a grant from a Strategic Research Project ( , Developing of Real- Time Traffic Tracking Technology Based on View Synthesis) funded by the Korea Institute of Civil Engineering and Building Technology. REFERENCES [1] C. L. Dudek, S. D. Schrok, G. L. Ullmand, and S. T. Chrysler, Flashing message features on changeable message signs, Transportation Research Record 2006(1959), [2] A. Dutta, D. L. Fisher, and D. A. Noyce, Use of a driving simulator to Evaluate and optimize factors affecting understandability of variable message signs, Transportation Research Part F 7(4-5) (2004), [3] C. M. Yang, D. Waters, C. C. Cabrera, J.-H. Wang, and C. E. Collyer, Enhancing the message displayed on dynamic message signs, In Proceedings of the third international driving symposium on human factors in driver assessment, training and vehicle design (2005). [4] T. Cohn, D. Greenhouse, and K. Nguyen, Structure of the message on the changeable message sign (CMS), Intellimotion 11(2) (2005), 8-9. [5] J.-H. Wang, and Y. Cao, Assessing message display formats of portable variable message signs, Transportation Research Record 1937(2005), [6] M. W. Hustad, L. Conrad, and C. L. Dudek, Driver understanding of abbreviations on changeable message signs in New Jersey, Transportation Research Record, 1689(1999), [7] B. R. Ullman, G. L. Ullman, C. L. Dudek, and E. A. Ramirez, Legibility distances of smaller letters in changeable message signs with light-emitting diodes, Transportation Research Record 1918(2005), [8] B. R. Ullman, N. D. Trout, and C. L. Dudek, Use of graphics and symbols on dynamic message signs: technical report, FHWA/TX-08/ , Texas Transportation Institute (2009). [9] C. J. Lai, Effect of color scheme and message lines of variable message signs on driver performance, Accident Analysis and Prevention 42(2010), [10] J. Messina, M. Song, J. D. Ortiz-Varela, and J. H. Wang, Assessing the message design on variable message signs in mitigating bottleneck issue at work zones, In Proceedings of TRB 91st Annual Meeting, 639

9 Transportation Research Board, Washington D.C. (2012). AUTHOR PROFILES Taehyung Kim received the degree in transportation engineering at the University of Maryland in the U.S. Currently, he is a research fellow at the Korea Transport Institute. His research interest covers transportation engineering, intelligent transportation systems, and U-T. Taehyeong Kim received the degree in transportation engineering at the University of Maryland in the U.S. Currently, he is a senior researcher at Korea Institute of Civil Engineering and Building Technology. His research interest covers intelligent transportation systems, information technology, optimization, paratransit, logistics, and simulation. Cheol Oh received the degree in transportation engineering at the University of California, Irvine in the US. Currently, he is an associate professor at Hanyang University. His research interest covers transportation engineering, intelligent transportation systems, and safety. Bum-Jin Park received the degree in transportation engineering at the Yonsei University in Korea. Currently, he is a senior researcher at Korea Institute of Civil Engineering and Building Technology. His research interest covers intelligent transportation systems, traffic flow, and information technology. Hyoungsoo Kim received the degree in transportation engineering at the University of Maryland in the U.S. Currently, he is a research fellow at Korea Institute of Civil Engineering and Building Technology. His research interest covers intelligent transportation systems. 640