DEVELOPMENT OF ROAD SURFACE TEMPERATURE PREDICTION MODEL

International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD) ISSN(P): 2249-6866; ISSN(E): 2249-7978 Vol. 6, Issue 6, Dec 2016, 27-34 TJPRC Pvt. Ltd. DEVELOPMENT OF ROAD SURFACE TEMPERATURE PREDICTION MODEL PARK JAEHONG, YUN DUKGEUN, YOON CHUNJOO, KIM JINGUK & YANG CHOONGHEON Highway & Transportation Research Institute, Korea Institute of Civil Engineering and Building Technology, Korea ABSTRACT This study aimed to develop a model to predict a road surface temperature using atmospheric temperature and humidity measured via the thermal mapping system in order to predict road surface temperature in the road network where drivers are driving. In this study, a method that predicted a road surface temperature was proposed to prevent slip and slide traffic accidents in winter. A road surface temperature was predicted suing a road surface temperature in the driving trajectory using data of road surface temperature, atmospheric temperature, and atmospheric humidity collected from the vehicle and data were analyzed using descriptive statistics and T-test. Finally, a prediction model of road surface temperature was developed using KNN. The results of analysis on the KNN parameters showed that a minimum difference in temperature per min was 0.02, maximum difference was 4.82, and mean difference was 1.59. A total root mean squared error of the prediction results was 0.92. The analysis result on road surface prediction model was compared with that in the real road surface temperature to determine the models accuracy. The study result will be utilized in other road safety related studies related to weather in the future. KEYWORDS: Thermal Mapping System, Road Temperature Prediction, KNN & Traffic Safety Received: Oct 16, 2016; Accepted: Nov 22, 2016; Published: Nov 23, 2016; Paper Id.: IJCSEIERDDEC20163 INTRODUCTION Background Original Article In recent years, the numbers of traffic accidents and death tolls have been decreasing in Korea as much attention on traffic safety, research achievements, and technical level have increased steadily. However, still more than 5,000 deaths due to traffic accident are occurring in every year. Thus, a number of studies analyzed the causes of traffic accidents in detail and the results suggested that climate conditions such as bad weather influenced traffic accidents significantly. The analysis result on traffic accident severity due to climate conditions, particularly, road surface conditions occurred during the last three years (2012 2014) reported that road surface conditions such as wet or frozen conditions increased accident severity. The possibility of traffic accident occurrence is likely to increase due to frozen roads when a temperature drops in winter. In order to prevent traffic accidents due to frozen road surface, it is essential for drivers to be aware of frozen road surface and be careful about the condition but it is limited for drivers to observe the condition visually resulting in difficulties of increasing driver's attention. Since the frozen road surface occurred during winter season is one of the causes of traffic accident, it is necessary to conduct a study on how to predict a frozen road time and place and provide information about frozen road surface to drivers. The freezing phenomenon refers to water temperature dropped below zero. Road surfaces are vulnerable to be frozen at the time of not only night but also sunrise and sunset when a temperature decreases. Thus, it is necessary to provide information about road frozen conditions to drivers based on road surface temperature in order to predict frozen road surfaces due to a temperature drop at road surface. However, www.tjprc.org editor@tjprc.org

28 Park Jaehong, Yun Dukgeun, Yoon Chunjoo, Kim Jinguk & Yang Choongheon atmospheric weather information that influences a road surface temperature directly or indirectly can be provided in a macroscopic range (unit of 5km*5km) so that it has a limitation to provide drivers with accurate temperature information on road surfaces. Therefore, this study aimed to develop a model to predict a road surface temperature utilizing weather information and road surface temperature data considering atmospheric temperature, humidity and wind direction in order to predict road surface temperature in the road network where drivers are driving. As a target road section to develop a prediction model of road surface temperature, some section in the Gyeongbu Expressway between Busan and Seoul was selected and thermal mapping system and data from the Automatic Weather System (AWS) were utilized for data collection required to road surface prediction. A significance test was conducted for each of the variables and a road surface temperature was predicted utilizing K-NN (K-Nearest Neighbor) algorithm, which was highly utilized in prediction models. Literature Review Previous studies on prediction of road surface temperature using weather data and road surface data conducted in Korea and other nations have been reviewed. Sato (2004) developed a prediction model of road surface temperature for mountainous areas and conducted a study on the need of explanation why the frozen road surface was important. Yang et al. (2011) developed a road surface temperature prediction model using heat-energy balance principle between road surface and atmosphere. Their road surface temperature prediction model consisted of two modules: Canopy 1 (description of heat exchange between road surface and atmosphere) and Canopy 2 (reflection of pavement characteristics during heat energy exchange process). The analysis on model execution showed that a mean error between two temperatures was less than 2. Kršmanc (2012) performed a study whether the METRo model used in Canadian National Weather Service can be applied to other nation (Slovenia). Kim et al. (2014) developed a prediction model of road surface temperature using neural network theory by setting road surface temperature data that can be obtained in the Road Weather Information System as variables. The data used to develop the model were road surface temperature, atmospheric temperature, and atmospheric humidity and a neural network that can predict a road surface temperature after one, two and three hours was designed. The analysis result showed that a standard deviation was 0.55 after one hour, 1.27 after two hour, and 1.43 after three hour and R2 value was 0.985, 0.923, and 0.903, respectively to indicate the explanatory power of the model. Park et al. (2014) developed United Model (UM)-Road that can predict road surface temperatures using UM meteorological fields produced in the KMA and road surface characteristic data and applied the model to real road surfaces to compare the results. The analysis result showed that the UM-Road simulated a change in trend of road surface temperature or maximum and minimum values during the non-precipitation time. When there was no precipitation or UM input value was accurate, the error rate showed that 50% of a deviation between UM-Road prediction and real measured values was within 1.5 and 90% of deviation was within 2.5. Song (2014) developed a model to predict road surfaces using energy balance theory on the road. His model represented a micrometeorological physical process, which was highly complex, and compared and verified the observation data of road weather observation systems in Germany and Korea Meteorological Administration (KMA). His analysis result proved that two systems were highly similar. Lee et al. (2015) developed a prediction model of accident processing time through K-Nearest Neighbor (KNN) algorithm, which was a non-parametric model using past traffic accident history data in the expressways in Korea. They classified accidents into accident grade to develop a model and data were extracted in terms of traffic volume, number of lanes, and time. Impact Factor (JCC): 6.3724 NAAS Rating: 3.01

Development of Road Surface Temperature Prediction Model 29 The results using the model were verified through the prediction error (MAPE) and revealed a low error compared to previous study results. Lee et al. (2015) analyzed traffic accidents caused by various weather conditions (precipitation, visibility, humidity, cloudiness, and temperature etc.) by using AWS weather data via logistic regression model and decision making tree model. The conclusion of the analysis result showed that precipitation and temperature were the most influential factors to traffic accidents. Son et al. (2015) developed a road surface prediction model using weather and traffic data. In the road surface temperature prediction model, a heat balance method was applied and vehicle radiant heat and tire friction heat were configured to consider a traffic volume in the model. The analysis result after model construction showed that RMSE, which was an error between observation and prediction values, was 1.97. Analysis Methodology The study method was divided into three stages: investigation, analysis, and conclusion in order to develop a prediction model of road surface temperature. The investigation stage was a model investigation stage to predict a road surface temperature and collect meteorological data provided by the thermal mapping system and AWS. Road surface temperatures, atmosphere temperature, and atmosphere humidity where vehicles were running were collected using the thermal mapping system and missing data and abnormal data were removed from the collected data. Moreover, AWS meteorological data provided by the KMA were collected and matched with the GPS acquisition location of the driving vehicle. The specifications and systems of the thermal mapping system used in the study are presented in Table 1 and Figure 1. Table 1: Main Sensor Specifications Category Atmospheric humidity measuring sensor Atmospheric temperature measuring sensor Road surface temperature measuring sensor GPS sensor Atmospheric humidity measurement range Accuracy of atmospheric humidity measurement Atmospheric temperature measurement range Accuracy of atmospheric temperature measurement Road surface temperature measurement range Accuracy of road surface temperature measurement Measurement point position (gap distance from the ground) Operating voltage Data output Measurement accuracy(positioning accuracy) Description 0% ~ 100% RH ±3% RH -20 ~ +80 ±0.3-25 ~ +100 ±0.5 40mm ~ 1000mm 10VDC ~ 32VDC CAN Communication 1.8m www.tjprc.org editor@tjprc.org

30 Park Jaehong, Yun Dukgeun, Yoon Chunjoo, Kim Jinguk & Yang Choongheon Figure 1: Picture of Thermal Mapping System An uninterrupted flow facility (Gyeongbu Expressway) was selected as a target section for development of prediction model of road surface temperature. Since the thermal mapping system used in this study acquires time-base data, acquiring data from uninterrupted flow roads can obtain more appropriate data than from interrupted flow roads where traffic delay or congestion can occur. Data from 01:00 to 06:00 for five hours were acquired in every 1min. and driving trajectories of vehicles acquired through the investigation were presented in Figure 2. Figure 2: Vehicle Trajectory for Acquisition Data The observation locations of the AWS that can affect the driving trajectories of the vehicle are presented in Table 2. and data were matched with one-min observation data utilizing the AWS data from the shortest distance from the center of the observation location. Table 2: Location of AWS Region (No) Latitude Longitude Altitude Region (No) Latitude Longitude Altitude Gyeongsan (827) 35.82507 128.74160 77.09000 Osan (550) 37.18797 127.04872 40.20000 Gyeryong (636) 36.31318 127.24065 131.99000 Gimcheon (822) 36.08128 128.10164 83.31000 Sangbuk (900) 35.58243 129.09982 124.31000 - - - - In this study, 6 factors were collected from the thermal mapping system and AWS. The atmospheric temperature and humidity and road surface temperature acquired from the thermal mapping data refer to information about the section where the vehicle was driving and the observation temperature in the AWS refers to an atmospheric temperature of the data acquired from the meteorological observation point. Wind speed 1 refers to aggregate data during 1minute and wind speed 10 refers to aggregate data during 10minute. Impact Factor (JCC): 6.3724 NAAS Rating: 3.01

Development of Road Surface Temperature Prediction Model 31 In the analysis stage, T-test that tested significance between variables was conducted with regard to each of data collected from the thermal mapping system and AWS thereby deriving a statistical significance between variables. In addition, accuracy was done by comparing a distance between individual data and analysis was done using the K-NN model. Accuracy of analysis result was presented through RMSE. T-test is a parametric statistics technique that tests whether mean values of two groups or two correlated samples were sampled from the same population. It is a parametric test that tests a statistical difference between two groups and one of the most widely used simple methods. The K-Nearest Neighbors (K-NN) algorithm is a pattern recognition method, which is a nonparametric method that is used in classification or regression. The K-NN algorithm searches the current state vector and past state vector to construct a cluster consisting of K neighbors which uses distance metric. History data are needed for prediction in order to construct the K-NN model and derived results using the K-NN model are compared with measured road surface temperature. A root mean square deviation (RMSD) or root mean square error (RMSE) was a frequently used measure of the difference of values observed in real environment and predicted values or estimated values. It is suitable to represent precision. Each of the differences is called residual and RMSD is used as a measure to aggregate residuals. ANALYSIS RESULTS Descriptive Statistical Analysis The results of technical statistics (mean, max, min, standard deviation, and variance) of the data collected through the thermal mapping system and AWS were presented. The minimum, maximum, and mean temperatures collected from the atmosphere using the thermal system were 1.40, 6.40, and 1.80 and the minimum, maximum, and mean temperatures collected from the road surface were 1.30, 6.40, and 1.60. The minimum, maximum, and mean temperatures collected from the AWS were 3.00, 4.00, and 1.60. The variance analysis on each variable showed that a change in road surface temperature was smaller than that in atmospheric and observation temperatures as presented in Table 3. T-Statistics Analysis Table 3: Result of Description Statistical Analysis N Minimum Maximum Average Standard Standard Deviation Deviation Atmospheric temperature ( ) 252-1.40 6.40 1.80 2.12 4.50 Atmospheric humidity (%) 252 0.00 84.80 72.40 7.34 53.88 Road surface temperature ( ) 252-1.30 6.40 1.60 1.66 2.74 Observation temperature ( ) 252-3.00 4.00 1.04 2.75 7.55 Wind speed 1 (m/s) 252 0.00 5.00 1.22 1.12 1.25 Wind speed 10 (m/s) 252 0.00 3.00 1.29 1.01 1.02 The T-Statistics analysis was conducted to test the statistical significance of each parameter and the result showed that significance probabilities of all parameters were 0.00. T-value was derived as follows: 13.44 for atmospheric temperature, 156.58 for atmospheric humidity, 15.36 for road surface temperature, 5.98 for observation temperature, 17.318 for wind speed 1, and 20.232 for wind speed 10. Thus, as shown in T-value results, variables that can be obtained using the thermal mapping system and AWS had similar significances statistically and influenced positive effect. The results of T-test are presented in Table 4. www.tjprc.org editor@tjprc.org

32 Park Jaehong, Yun Dukgeun, Yoon Chunjoo, Kim Jinguk & Yang Choongheon Table 4: Result of Description Statistical Analysis t 95% Confidence Interval Degree of P-value Mean Value Difference Freedom (Both Sides) Difference Lower Limit Upper Limit Atmospheric temperature ( ) 13.48 251 0.00 1.80 1.53 2.06 Atmospheric humidity (%) 156.58 251 0.00 72.40 71.49 73.31 Road surface temperature ( ) 15.36 251 0.00 1.60 1.40 1.81 Observation temperature ( ) 5.98 251 0.00 1.04 0.69 1.38 Wind speed 1 (m/s) 17.32 251 0.00 1.22 1.08 1.36 Wind speed 10 (m/s) 20.23 251 0.00 1.29 1.16 1.41 K-NN Analysis A time used for pattern analysis of K-NN, which was employed to develop a prediction model of road surface temperature was set at 10 min in this study. The number of similar patterns that were developed to predict a road surface temperature was set to 20 patterns and 100-min history data prior to the prediction time were used to predict a 1-min road surface temperature. The results of analysis on the KNN parameters in every one min showed that a minimum difference in temperature per min was 0.02, maximum difference was 4.82, and mean difference was 1.59. A total root mean squared error of the prediction results was 0.92. CONCLUSIONS AND FURTHER RESEARCH The analysis result on traffic accident severity due to climate conditions, particularly, road surface conditions occurred during the last three years (2012 2014) reported that road surface conditions such as wet or frozen conditions increased accident severity. The possibility of traffic accident occurrence is likely to increase due to frozen roads when a temperature drops in winter. Thus, as a pre-requisite research to prevent traffic accident due to frozen road surface, this study developed a model that can predict a road surface temperature in vehicle driving sections. In order to predict road surface temperatures, data collected from the thermal mapping system and AWS (atmospheric temperature, atmospheric humidity, road surface temperature, and wind direction) were employed. This study developed a model that can predict road surface temperatures utilizing weather information and road surface temperature data based on the analysis result. However, the following additional research is needed to ensure higher reliability of the derived results than this study. First, this study predicted a road surface temperature by selecting a single section in uninterrupted flow road. However, information from more various sections is needed to develop a prediction model of road surface temperature than used in this study. Second, this study employed temperature, humidity, and wind speed as variables required to predict a road surface temperature. However, various other variables are also needed such as pavement heat of the road, longwave radiant heat and latent heat emitted from road surface. Third, significance between variables was tested using T-statistic and predicted using the KNN model as a model to predict a road surface temperature. For future research, it is needed to improve prediction accuracy using diverse prediction model algorithms including KNN. The study results will be expected to be utilized in other studies that can improve road traffic safety using road surface temperature in the future. Impact Factor (JCC): 6.3724 NAAS Rating: 3.01

Development of Road Surface Temperature Prediction Model 33 ACKNOWLEDGEMENTS This research was supported by a grant from Inner Research Program Development of Driving Environment Observation, Prediction and Safety Technology Based on Automotive Sensors funded by Korea Institute of Civil Engineering and Building Technology. REFERENCES 1. Crevier, L. P. & Delage, Y., (2001). A New Model for Road-Condition Forecasting in Canada. Journal of Applied Meteorology. 40, 2026-2037. 2. Kim, I. S., Yang, C. H., & Choi, K., (2014). Development of a Surface Temperature Prediction Model Using Neural Network Theory, Korean Society of Transportation, 32(6), 686-693. 3. Kršmanc, R., Šajn Slak, A., Čarman, S., & Korošec, M., (2012). METRo Model Testing at Slovenian Road Weather Stations and Suggestion for Further Improvements. 16th International Road Weather Conference in Helsinki. 4. Park, M. S., Joo, S. J., & Son, Y. T., (2014). Development of Road Surface Temperature Prediction Model using the Unified Model output (UM-Road). Korea Meteorological Society, 24(4), 471-479. 5. Lee, K., Jung, I., Noh, Y., Yoon, S., & Cho, Y., (2015). The Effect of Road Weather Factors on Traffic Accident. Journal of the Korean Data & Information Science Society, 26(3), 661-668. 6. Lee, S. B., Han, D. H., & Lee, Y. I., (2015). Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level. Journal of Korean Society of Transportation, 33(5), 497-507 7. Sato, N., Thornes, J. E., Maruyama, T., Akira S., & Yamada, T., (2004). Road Surface Temperature Forecasting (Case Study in a Mountainous Region of Japan). 6th International Symposium on Snow Removal and Ice Control Technology. 8. Song, D. W. (2014). The Prognostic Model for the Prediction of the Road Surface Temperature by Using the Surface Energy Balance Theory, Journal of the Korean Geotechnical Society, 30(11), 17-23. 9. Son, Y. T., Jeon, J. S., & Whang, J. M. (2015). Developing a Model to Predict Road Surface Temperature using a Heat- Balance Method, Taking into Traffic Volume, The Journal of The Korea Institute of Intelligent Transport Systems, 14(2), 30-38. 10. Sin, G. H., Song, Y. J., & You, Y. G., (2011). Bridge Road Surface Frost Prediction and Monitoring System. The Korea Contents Association, 11(11), 42-48. 11. Yang, C. H., Park, M. S., & Yun, D. G. (2011). A Road Surface Temperature Prediction Modeling for Road Weather Information System. Journal of Korean Society of Transportation, 29(2), 123-131. www.tjprc.org editor@tjprc.org