Journal of Emerging Trends in Computing and Information Sciences

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1 A Study on Methods to Calculate the Coefficient of Variance in Daily Traffic According to the Change in Hourly Traffic Volume Jung-Ah Ha Research Specialist, Korea Institute of Construction Technology, Korea ABSTRACT This study was designed to investigate methods to calculate the coefficient of variance in short-term traffic count (hereafter called STC) point to examine the sample size of the STC, a sample survey of traffic volume investigation. Significance level, tolerance and coefficient of variance are needed to calculate the frequency of traffic counting. Calculating the coefficient of variance is important as the coefficient of variance varies depending upon the actual data characteristic. Since it is inevitable for the subjectivity of researchers to intervene in the sample survey, where the known coefficient of variance is used, this study sought to apply a more objective method. For an analysis, a goodness-of-fit test was performed using hourly traffic patterns of permanent traffic count (hereafter called PTC) points, and the difference between the actual coefficient of variance and the coefficient of variance estimated by the goodness-of-fit test was calculated. The analysis results showed that effort of the coefficient of variance was about 4.5%, suggesting that it is possible to be applicable. It is determined that there is a need to devise more cost-effective traffic volume research methods by reflecting traffic characteristics by month and weekday. Keywords: Traffic volume, coefficient of variance, short-term traffic count, permanent traffic count 1. INTRODUCTION The road traffic survey is widely used in the planning, design and operation of the road, and it provides basic information for establishing road traffic planning and management plans. In addition, it yields an important data, that is frequently utilized in the study of various fields related to road and transportation. This traffic volume survey is largely divided into STC and PTC. The PTC is a survey to measure the number of vehicles that pass through specific points, usually 365 days, over a long period of more than one year with traffic survey equipment installed in the specific points, and the STC is a survey to identify the overall road usage as the one conducted extensively in all sections that requires traffic volume research. If permanent traffic counters are installed in all points, the calculation of accurate annual average daily traffic (hereafter called AADT) can be achieved. However, as the permanent traffic counters are expensive, the actual expenditure goes beyond the budget. Thus, the traffic data is collected by the STC at least once a year with the permanent traffic counters installed in only some points. However, in the current traffic volume survey, the AADT is calculated by annual survey conducted once to five times a year, regardless of traffic characteristics of the survey point, it is necessary to conduct a research to calculate the frequency of traffic counting by traffic characteristic of the point. In this regard, this study analyzed the coefficient of variance based on hourly traffic patterns that can be collected through the STC and investigated the appropriate number of investigations by the coefficient of variance. 2. BACKGROUND This study seeks to propose an objective basis for determining the frequency of traffic counting of the STC performed once to five times a year. Towards this end, methods to estimate the coefficient of variance of daily traffic volume according to changes in hourly traffic volume were investigated. According to the study by J.H.Jang et al. (2003), the AADT estimation error could be maintained at a certain level if the number of shortterm traffic counts varied depending on the coefficient of variance. As a kind of sampling survey, the STC belongs to the simple random sampling survey in case of investigating just one day from 365days without considering the characteristics of the survey point. As for the simple random sampling survey, the estimation error is determined by the frequency of traffic counting. Thus, in case a change in daily traffic volume is greater, the estimation error can be included in the tolerance only by increasing the number of investigations. However, the STC conducted one to five times a year cannot be used to identify changes in the actual point. Thus, this study examined a method for estimating traffic fluctuations at the point using hourly traffic volume collected by the STC data. Fig 1: Case when traffic fluctuation is large 835

2 the graphs, traffic volume in winter is less than that in summer, and traffic volume in Friday and Saturday is greater compared to that of weekdays. Traffic fluctuation on weekday is found to be somewhat smaller than that on Friday, Saturday and Sunday. Fig 2: Case when traffic fluctuation is small 3. CHARACTERISTICS OF TRAFFIC VOLUME It is explained in the Traffic Monitoring Guide (2013) that since daily traffic volume has fluctuation characteristics by month and weekday, the correction factor should be applied to estimate the AADT. The characteristics of traffic volume by month and weekday in 2013 are shown in Fig3, Fig4. According to the study by S.H.Lim et al (2005), traffic survey points can be divided into urban, rural and recreation roads depending on regional characteristics. The urban road has a characteristic of larger traffic volume on weekdays compared to that on weekends due to a great number of commuter vehicles, and rural road is characterized by a constant level of traffic volume during the daytime. In addition, traffic volume of road near a tourist attraction is smaller on weekdays than on weekends, and hourly traffic characteristics are not significant. Fig5 to Fig7 show the graphs of hourly traffic characteristics of urban, rural and recreation roads. For classification of road characteristics, the STC points were divided into urban, rural and recreation roads based on the study results by S.H.Lim et al(2005), and hourly traffic volume of the points representing each road was displayed in the graphs. As can be seen from the graphs, hourly traffic patterns vary depending on the day of the week, and hourly patterns also differ according to the road characteristics. The urban road has a characteristic of heavy rush hour traffic on weekdays, and recreation road is characterized by greater traffic volume on weekends than on weekdays and also has a characteristic of large traffic volume during the daytime on weekends. In addition, the recreation road has great traffic volume during the daytime on weekends, but urban and rural road have great traffic volume during the nighttime on weekends in common. This is because traffic volume of vehicles going back home from tourist attractions is formed in the nighttime on weekends of urban and rural roads. Fig 3: Traffic pattern (monthly) Fig 4: Traffic pattern (Daily) Fig 3 and Fig 4 show monthly and daily traffic patterns of national highways in Korea. As can be seen in Fig 5: Hourly Traffic volume (Urban Road) 836

3 Fig 6: Hourly Traffic volume (Rural Road) Fig 7: Hourly Traffic volume (Recreation Road) Since hourly traffic characteristics are apparent by road characteristics and day of the week and hourly traffic data can be collected by the STC, this study sought to analyze methods for calculating the coefficient of variance according to the hourly traffic characteristics. To this end, a 365-traffic volume of 496 points collected from the PTC points of Korean national highways in 2013 was utilized. Among target points, roundtrip two lane roads are 172 points, and more than four lane roads are 324 points. The coefficient of variance by the number of lanes is distributed as follows. As hourly traffic volume shows different patterns on weekdays and weekends, traffic volumes on weekdays when the STC is actually performed was analyzed in this study. Fig 8: Distribution of coefficient of variance by the number of lanes The analysis on the coefficient of variance on weekdays in the PTC points found that there were few points with less than 5% in the coefficient of variance, and the coefficient of variance in points with more than four lanes was lower compared to that in points with two lanes. Given that most of rural and recreation roads are roundtrip two lane roads, the variance of rural and recreation roads is determined to be great. 4. ANALYSIS coefficient of variance of the point with hourly traffic volume. As hourly traffic volume show different characteristics on weekdays and weekends, an analysis was performed only by utilizing traffic volume on weekdays in consideration of these characteristics. Since traffic volume on weekdays has a relatively small variation compared to that on weekends, and the STC for traffic survey is carried out on weekdays, excluding national holidays and holiday seasons from March to December, the similarity of the coefficient of variance was determined by hourly traffic patterns on weekdays. In addition, this study analyzed if the coefficient of variance of the point with similar hourly traffic pattern is similar to the actual coefficient of variance under the assumption that the variation of daily traffic volume is similar according to road characteristics. In order to determine the similarity of hourly traffic patterns, this study utilized a chi-square goodness-of-fit test. The goodness-of-fit test is to verify which random variable follows the assumed distribution using sample data, and it is expressed in the following table. Table 1: Data collection Category j Observed O1 O2 O3... Oj Expected E1 E2 E3... Ej 837

4 Since it is impossible to calculate the coefficient of variance of the STC points, this study sought to calculate the error of the coefficient of variance by comparing the coefficient of variance of the point with similar patterns and the actual A point after finding out the point with similar hourly traffic patterns to 495 points except for A point selected among 496 PTC points through the goodness-of-fit test using hourly traffic patterns. The coefficients of variance for all 496 points were estimated by means of the goodness-of-fit test, and the estimation error of the coefficient of variance turned out to be 4.5% on average. In particular, the number of points with the coefficient of variance of less than 5% was 336, which accounts for 68% of the total number of points, indicating that the estimated value can be utilized. Table 2: Frequency table of estimation error (1) Error Frequency Rate (%) 0~10% % 10~20% 38 8% 20~30% 6 1% over 30% 3 1% total % Table 3: Frequency table of estimation error (2) (under 5%) Error Frequency Rate (%) 0~1% % 1~2% 73 15% 2~3% 68 14% 3~4% 49 10% 4~5% 43 9% total % Where, z(α) : significance level γ: tolerance σ: variance μ: mean From the preceding equation, σ/μ represents the coefficient of variance, and it is possible to calculate the frequency of traffic counting if the tolerance and significance level is determined. In the statistical analysis, a 5% significance level is generally used. Thus, in this study, the frequency of traffic counting was calculated by applying tolerance of 5%, 10%, 15% and 20% with the significance level fixed at 5%. The coefficient of variance and frequency of traffic counting by tolerance are shown in the following table. Table 4: Coefficient of variance and frequency of traffic counting by tolerance Item coefficient of Variance Tolerance 5% 10% 15% 20% 0~5% ~10% ~15% ~20% ~25% ~30% % or more APPLICATION coefficient of variance according to the change in hourly traffic volume. The background of this study lies in the need to guarantee the accuracy of AADT estimates based on the objective criteria for the number of the STC performed once to five times a year. Of course, the method to improve the reliability of the AADT is to increase the number of investigations. However, for a cost-effective investigation, this study examined the method to estimate the coefficient of variance of daily traffic volume to increase the accuracy with the minimum survey. The equation to calculate the number of investigations based on the estimated coefficient of variance is shown below. Fig 9: Coefficient of variance and frequency of traffic counting 6. CONCLUSION coefficient of variance in daily traffic volume of the STC points, which is required to estimate the AADT of the STC. The coefficient of variance in daily traffic volume of the STC points is used as an important parameter for 838

5 calculating the number of STCs. The goodness-of-fit test was utilized to calculate the coefficient of variance. According to the calculation results, the estimation error of the coefficient of variance turned out to be 4.5%, proving the applicability. According to the analysis, in case the frequency of traffic counting is calculated with the estimated coefficient of variance, the greater number of investigations will be needed compared to the current number of investigations. However, this study has its limitation in that the frequency of traffic counting was calculated without knowing traffic characteristics at all. It is expected that a more cost-effective investigation will be carried out if the similarity of traffic volume is analyzed by adding daily and monthly characteristics. REFERENCES [1] J.H.Jang, J.A.Ha, J.S.Oh, H.S.Kim, Precision Level on Traffic Counting Period by Coefficient of Variation Group of Monthly factor, Korea Society of Civil Engineers 2003 Convention (2003), [2] J.A.Ha, S.C.Oh. Estimating Annual Average Daily Traffic Using Hourly Trafic Pattern and Grouping in National Highway, THe Journal of the Korea Institute of Intelligent Transportation Systems, Vol.11(2012), 2nd, [3] S.H.Lim, J.A.Ha, J.S.Oh, Classification of National Highway by Factor Analysis, Journal of the Korean Society of Road Engineers, vol.7 No.3(2005), [4] MOLIT, Annual Traffic Volume Report (2014) AUTHOR PROFILE Jung-Ah Ha received her doctor s degree in transportation engineering at the Ajou University in Korea. Currently, she is a researcher specialist at Korea Institute of Civil Engineering and Building Technology. Her research interest covers intelligent transportation systems, traffic flow, and traffic volume count and traffic simulation. 839

A Study on Weekend Travel Patterns by Individual Characteristics in the Seoul Metropolitan Area

A Study on Weekend Travel Patterns by Individual Characteristics in the Seoul Metropolitan Area Min-Seung. Song, Sang-Su. Kim and Jin-Hyuk. Chung Abstract Continuous increase of activity on weekends have