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 resulted in severe congestion in Seoul metropolitan area. It has been getting severer and widen after implementing five-day workweek. Hence, understanding of weekend is a first step to establish transportation policy for resolving the problems. The research started with descriptive analysis using survey data to investigate the difference of travel pattern between weekdays and weekends. Then, the logit-models were developed for finding the characteristic factors after clustering 3 groups. They were divided by the degree of difference between weekend and weekday trips. The descriptive analysis showed that travel time and distance were longer on weekends. In addition, the model indicated that people, who had more income and stable asset such as 50 s and 60 s, tended to travel more on weekends. It is expected that age and welfare of life will increase, so the approach presented in the paper can be helpful to policymakers. Keywords Cluster Analysis, Logit Model, Travel Time, Weekday Travel, Weekend Travel. T I. INTRODUCTION RAVEL demand on the weekends has been continuously increasing due to following reasons: increase of leisure travel after implementing the five-day workweek, income level improvement, and people from diverse class fulfilling their desires. Famous attractions and cultural facilities including highways surrounding cities have shown serious traffic jam. Accordingly, a new traffic policy needs to be established to solve these problems. In this point, there is a growing concern and interest about the weekend trip. There are some differences between the characteristics of travel during weekends and weekdays. On weekends, people tend to use their own cars more than transits and travel slightly more than Annual Average Daily Traffic (AADT) [1]. In addition, it has a different peak hour pattern. These characteristics come from leisure travel which is major on Min-Seung. Song is with Yonsei University, Seoul, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea (corresponding author to provide phone: +82-2-2123-3569 ; e-mail: renoir7th@hotmail.com ). Sang-Su. Kim, is with Kunil Engneering, Gyeonggi-do, Seohyun-dong Bundang-gu Sungna, Korea. (e-mail: 2169660@naver.com). Jin-Hyuk. Chung is with Yonsei University, Seoul, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea (e-mail: jinchung@yonsei.ac.kr ). weekend trips [2]. Generally, the travel pattern for leisure has some unique characteristics unlike compulsory and routine trip [3]. Its travel distance and time is longer than other trips and it could be easily affected by seasonal change [4]. According to these reasons, the travel demands on weekends have been ignored in policy-making and infrastructure plan about transportation system due to its instability. Furthermore, there is a limitation that most of studies about travel pattern have focused on weekday perspective. For establishing transportation policy considering both weekdays and weekends trip, it is required to examine the factors generating the difference of travel pattern between weekdays and weekends. Some developed countries including USA and Japan have analyzed weekend and leisure trip steadily and reflected it during establishing transportation policy. The aim of this research is to examine trip maker's factors affecting weekend trip pattern and making the difference. Firstly, to find the difference, descriptive statistics was implemented by using traffic volume data and travel survey data (Seoul metropolitan household trip diary survey and Weekends trip survey, 2010). Secondly, the changes on weekend trip compared to weekdays were investigated by clustering method. Each group separated by travel time difference had similar characteristics, and those explained the trip maker's factors which affect the weekend trip pattern. Then, we developed logit-models to find individual variables that affect these groups. II. LITERATURE REVIEW There are some researches about weekend and leisure travel for establishing countermeasure to weekend transportation problems by using survey data. In general, these researches can be divided into two aspects. The first group is the research of predicting change of travel demands on weekends or leisure. There is a study that most of weekend trip consist of leisure trips in USA [5]. Another research indicates that travel distance is much longer on weekends than weekdays in Japan [6]. The second group is about analyzing factors that generate weekend trip. Recently, researches about the difference of travel time, activity and mode between weekdays and weekends have been studied by using comparative analysis on specific city data [7]. 113
This paper compared the differences between weekday and weekend travel by using descriptive statistical technique. Furthermore, in the aspect of choice modeling, the paper tried to find the individual factors that affected people to select travel pattern on weekends. Author J.K. Koo, S.S. Kim(2004) S.H. Choo et al. (2007) Y.J. Chang, S.I. Lee (2010) A.M. Lockwood et al. (2005) E. Sall, C.R. Bhat (2007) TABLE I LITERATURE REVIEW Contents A study on forecasting travel pattern on weekends and weekdays in the future based on survey in Seoul. Establishing weekend generation model using individual variables [8]. Research of the environmental factors which can affect leisure trip. Research of the difference of travel pattern between weekends and weekdays based on San Francisco Bay Area data [9]. A study on the activities which affect travel pattern and schedule on weekends based on San Francisco Bay Area data [10]. higher on weekends, due to higher percentage of leisure trip which requires automobiles. Purpose TABLE III DISTRIBUTION BY PURPOSE TRIP Weekday -off 1.1 1.5 1.2 Back to home 42.3 42.3 45.7 Work 18.2 11.3 4.0 School 12.1 6.9 0.4 Academy 5.8 2.6 2.2 Business 3.5 2.2 0.9 Back to work 1.5 0.3 0.1 Shopping 3.3 5.3 6.4 Leisure 5.2 15.6 15.8 Etc. 7.0 11.9 23.2 Total 100 100 100 III. WEEKDAY AND WEEKEND TRAVEL PATTERN ANALYSIS This research analysis was based on Seoul Metropolitan Household Trip Diary Survey and Weekend Trip Survey. Out of 8.27 million households (Seoul metropolitan area), 226,725 households (equivalent to 2.8%) were used for sample on weekdays and 13,631 households (equivalent to 6% of the weekday survey) for weekend travel pattern survey. A. Weekday and Weekend Travel Pattern Analysis 2,352 households had responded for both weekday and weekend surveys at the same time and it broke down to 905 households from Seoul, 138 households from Incheon, and 1,309 households from Gyeonggi-do. The number of each group people are 7,549, 2,931 and 4,160 respectively. The survey indicates that the number of trip is the highest in the order of weekdays, and. On weekdays, the number of commuting to work and school are the highest and on weekends leisure and etc. trip are the highest. In the aspect of trip mode, the survey indicates that the number of trip is the highest from weekdays, s and s. On weekdays, automobile and public transportation cover 32.8% and 28.9%. On s, they cover 43.4% and 15.5%. On s, they cover 48.9% and 22.6%. The comparison result indicates that the usage of automobile is Day members TABLE II TRIP BY PURPOSE Travelers Trips member traveler Weekday 7,549 6,225 14,599 1.93 2.35 7,549 5,324 10,595 1.40 1.99 7,549 3,936 7,614 1.01 1.93 Day members TABLE IV TRIP BY MODE Travelers Trips member traveler Weekday 7,549 6,225 17,192 2.28 2.76 7,549 5,324 13,877 1.84 2.61 7,549 3,936 9,881 1.31 2.51 Purpose TABLE V DISTRIBUTION BY MODE TRIP Weekday Walk 29.5 23.2 21.3 Automobile 32.8 43.4 48.9 Bus 16.2 15.2 13.8 Intercity bus 3.0 2.4 2.3 Express bus 0.1 0.2 0.2 Train 12.7 10.3 8.8 Taxi 0.8 1.4 1.5 Truck 2.1 1.8 1.4 Bicycle 2.7 2.0 1.8 Etc. 0.0 0.1 0.1 Total 100.0 100.0 100.0 Analyzing the relationship between mode and purpose trip, the percentage of automobile usage for leisure on weekends (51.2~52.5%) is higher than weekdays (34.6%). For the public transportation, the weekend usage (23.3%) is little higher than the weekdays (25.3~25.9%). The percentage of automobile 114
trip for shopping is higher on weekends (53.1~55.1%) compare to weekdays (32.1%) and the percentage of public transportation is 26.9% on weekdays and 19.1~29.1% on weekends. According to start-time distribution (Fig. 1), the travel volume is concentrated during commuting hour on weekdays. On, the travel volume is the highest at 9am and relatively distributed equally throughout 10am to 6pm. The peak time at 9am is for commuting like weekdays. is similar to s but the peak time starts at 10am which is later than s. Comparing this survey to the 2006 Seoul Metropolitan Travel Survey, the results were relatively similar. Weekda y TABLE VI (WEEKDAY) Etc. Total ~30min 86.1 67.5 56.6 77.9 88.4 59.8 82.5 70.2 75.4 69.0 7 30~60 11.4 20.9 30.1 13.3 9.3 25.0 13.3 15.6 17.1 20.3 60~90 0.5 7.4 10.5 5.7 1.8 8.8 3.6 9.4 3.9 7.1 90~120 1.0 2.5 2.1 2.0 0.4 2.5 0.2 1.2 1.8 2.1 120~180 0 0.8 0..5 0.7 0.1 1.7 0.2 0.8 0.8 0.7 180min~ 1.0 0.9 0.3 0.4 0 2.2 0.2 2.9 1.0 0.8 Total 100 100 100 100 100 100 100 100 100 100 TABLE VII (SATURDAY) Etc. Total E ~30min 74.9 61.4 60.2 86.6 79.5 57.0 75.4 46.9 64.0 62.0 6 30~60 15.9 22.9 29.4 9.2 15.2 28.4 20.1 22.7 18.0 21.7 60~90 5.3 7.5 7.5 2.7 4.3 6.4 2.6 8.7 8.4 7.1 Fig. 1 Start Time Distribution in Day of the Week To investigate the difference of travel pattern between weekends and weekdays, a comparative analysis was implemented in travel time and distance status. According to the TABLE VII, on weekdays, travel time less than 60 minutes covers the total of 89.3%, and on both and it covers 83.7% and 84.2%. The travel time that requires more than 90 minutes on weekdays covers 3.6% and on and it covers 9.2% and 8.7%. Especially, the leisure travel time more than 90 minutes on weekdays covers 4.9%, and on and it covers 21.7%, 10.3%. The travel for shopping which is less than 60 minutes covers 95%. In contrast, the long distance travel percentage is higher on weekend compared to weekdays. In the results of distance, the traveling distance less than 20km on weekdays is 88.4% and on and s, both, are 83.1% and 82.4%. The traveling distance more than 50km on weekdays is 1.8% and on and s, both, are 6.6% and 7.4% which is relatively high during the weekends. The leisure travel that is more than 50km on weekdays is 4.1% and on and, both are 17.7% and 10.6%. 90~120 2.9 2.8 1.7 0.7 0.8 3.4 1.1 5.4 3.5 3.0 120~180 0 1.9 0.5 0.5 0.0 2.1 0.4 3.5 1.8 1.8 180min~ 1.0 3.5 0.6 0.3 0.3 2.7 0.3 12.8 4.2 4.4 total 100 100 100 100 100 100 100 100 100 100 TABLE VIII (SUNDAY) Etc. Total ~30min 65.9 60.4 63.9 47.4 77.6 57.3 80.1 54.9 75.0 64.4 30~60 21.1 20.9 28.3 26.3 15.4 26.2 15.1 22.0 15.5 19.8 60~90 7.3 7.1 6.3 21.1 5.5 8.7 2.2 11.9 5.3 7.2 90~120 5.7 3.2 1.2 5.3 0.5 0 0.8 4.7 1.8 2.9 120~180 0 2.0 0.3 0 1.0 2.9 1.3 2.3 1.0 1.7 180min~ 0 6.3 0 0 0 4.9 0.5 4.3 1.4 4.1 total 100 100 100 100 100 100 100 100 100 100 115
B. Travel Characteristic Analysis by Travel Index The research analyzed the household variables that affected the pattern of travel volume, time, and distance. Through the t-test and ANOVA-test, the statistics significance was confirmed in all cases. In the aspect of travel volume, the travel volume of households increases as there are more members in household and if they have higher monthly income. Also for a household who owns two automobiles, it is indicated that the range of change is relatively small. In the aspect of travel time, the travel time on weekdays and increase depending on the household income. Household group with low income works on. The travel time on weekdays is usually higher with people in their 20's and 60's. However, during the weekends, the travel time of people in their 20's decreases and the travel time between the age of 40 and 60 increases. In the aspect of travel distance, the travel distance on weekdays is usually higher with people in their 20's. However, during the weekends, the travel distance in their 20's decreases and the travel distance between the age of 40 and 60 increases. In general, households with higher income have longer travel distance. IV. CLUSTER ANALYSIS THROUGH THE DIFFERENCE OF TRAVEL TIME BETWEEN WEEKDAYS AND WEEKENDS A. Cluster by Travel Time Change from Weekdays to Weekends Based on changes of travel time between weekdays and weekends, the groups were divided into "increase(a), decrease(b) and similar(c)" by using K- means clustering method and set them as dependent variables. The variables that might affect choosing those groups were selected. The connectivity of selected variables and chosen group was confirmed through stochastic significance test. The outcomes were inserted to the multiple-logit model. The concept is that traveler chooses groups (A, B, C) based on their individual characteristic. The coefficient of group of age 60's who spends more time on weekend activities was estimated higher(0.4367) and we can conclude that it was due to increase in life expectancy with better health and the trend of enjoying more leisure time after the retirement. Group A and B, both, have variables of being in their 60's and the coefficient are both positive. But the coefficient in group A is greater, which indicates that people who are in their 60's tend to travel more frequently on weekends rather than weekdays. The model shows that people in the age between 20~30 and people who work in the office tend to choose group B. The reason for this is because people who are in their 20's and 30's have a lot of activities on weekdays and people who work in office also like to rest at home due to the amount of activities TABLE IX TRIP CHOICE LOGIT MODEL BETWEEN WEEKDAYS AND WEEKENDS Group A (weekend trip > weekday s) Group B (weekend trip < weekday s) Group C (no difference) Variables during weekdays. Coefficient Standard error T-statistics P-value Constant -1.5752 0.0470-33.526 0.0000 60 s 0.4367 0.1254 3.481 0.0005 Student -0.6138 0.0973-6.308 0.0000 Job : etc. -0.3915 0.1670-2.344 0.0191 Constant -1.1251 0.0656-17.139 0.0000 10 s -1.8237 0.1481-12.315 0.0000 20 s 0.4274 0.0783 5.457 0.0000 60 s 0.2648 0.1202 2.203 0.0276 Student 0.8835 0.1226 7.207 0.0000 Job : etc. -0.3071 0.0674-3.515 0.0004 Job : service -0.4248 0.1367-3.107 0.0019 Job : office 0.3484 0.0906 3.846 0.0001 Job : agriculture -0.7091 0.2473-2.867 0.0041 Reference Group Number of Observations 6.838 Log Likelihood Function -5609.080 Chi-squared[11] 518.5135 Prob [chi-squared > value] 0.0000 B. Cluster by Travel Volume in Day of the Weekends To analyze how differently people travel on s and s, the logit-model was made with 3 groups (D, E, F). The group D indicates people who have the most travel volume on, and the group E indicates people who tend to travel on s compared to other days. Last group F indicates people who have no difference in travel between weekends and weekdays. These groups are based on sample which consists of people (1,820) who travel less on weekdays compared to weekends because they can show the characteristic factors about preferring travel on weekends better. The grouping and modeling methods appeared to have stochastic significance. In the analysis results, students tend to travel more on weekdays, but they prefer to travel on than. In contrast, people in their 50 s tend to travel more on weekends, but they prefer to travel on. It indicates that people in their 50's are financially stable, so they can go for leisure trip on weekends. In general, people who have more income tend to travel on, and people who have more cars and professional career tend to travel on. Depending on motorization, income increase, and implementation of the five-day workweek, travel volume on weekends is expected to increase. 116
TABLE X TRIP CHOICE LOGIT MODEL BETWEEN SATURDAY AND SUNDAY Group D ( trip > other s) Group E ( trip < other s) Group F (no difference) Variables Coefficient Standard error T-statistics P-value Constant -3.3397 0.3243-10.298 0.0000 50 s 0.5593 0.3032 1.845 0.0651 Income 0.2661 0.0865 3.076 0.0021 Student -0.5088 0.2214-2.298 0.0216 In Incheon 1.0588 0.2949 3.59 0.0003 Constant -2.5069 0.1832-13.682 0.0000 50 s 0.5940 0.2620 2.268 0.0234 Automobiles 0.2302 0.1233 1.867 0.0618 Job : expert 0.9684 0.3448 2.808 0.0050 Student -0.8171 0.2348-3.48 0.0005 In Incheon 0.6209 0.3179 1.953 0.0508 Reference Group Number of Observations 1.820 Log Likelihood Function -965.9187 Chi-squared[11] 59.3596 Prob [chi-squared > value] 0.0000 REFERENCES [1] J.K. Koo, S.S. Kim, A Study on the Improvement of the Weekend Traffic System Affected by Implementing the Five-Day Work Week, Research book 2004-1, Busan Development Institute, 2004 [2] S.H. Oh, J.Y. Young, A Study on Evaluation Process of Investment for Transportation Facilities Taking Leisure Travel Demand into Account, Kuktoyeon 2006-30, Korea Research Institute For Human Settlements, Dec. 2006 [3] H.K. Lee, A Study on Survey Method and Demand Forecast of Leisure Travel, Research book 2006-16, The Korea Transport Institute, Nov. 2006 [4] Y.J. Chang, S.I. Lee, An Impact Analysis of the Relationship between the Leisure Environment at People s Places of Residence in Seoul and their Leisure Travel on Weekends, Journal of Korea Planners Association, vol. 45, no. 6, pp. 85 100, Nov. 2007 [5] J.D. Hunt, P. McMillan, K. Stefan, Nature of Weekend Travel by Urban Households, 2005 Annual conference of the Transportation Association of Canada. [6] T. Yai, H. Yamada, N. Okamoto, Nationwide recreation travel survey in Japan : outline and modeling applicability, Transportation Research Record, vol. 1493, pp. 29-38, 1995 [7] S.H. Choo, S.N. Kwon, S.K. Kim, A Study on Weekend Travel Characteristics: A Case of Seoul, in Proc. 57th Conf. Korean society of Transportation, pp. 153-162, Sep. 2007 [8] S.H. Choo, A Study on Methods for Commuting Weekend Origin/Destination Travel, Research book 2007-16, The Korea Transport Institute, Nov. 2007 [9] A.M. Lockwood, S. Srinivasan, C.R. Bhat, Exploratory Analysis of Weekend Activity Patterns in the San Francisco Bay Area, Transportation Research Record, vol. 1926, pp. 70-78, Jan 2005 [10] E. Sall, C.R. Bhat, An Analysis of Weekend Work activity Patterns in the San Francisco Bay Area, Transportation, vol. 34, no. 2, pp. 161-175, 2007 V. CONCLUSION According to the findings of this study, people usually tend to travel on weekdays, but travel time(9.4%) and distance(53.7%) are higher on weekends than on weekdays. It is because, on weekdays, people make the most trips for business work, but on weekends, people travel for leisure which has longer travel time and distance. In analysis of travel time difference between weekdays and weekends, it is interesting phenomenon that people in their 60 s travel more frequently on weekends. In another analysis, which investigates factors that affect the day travel pattern of weeks, it is shown that people in their 50 s and people from a specific city (Incheon) tend to travel more frequently on weekends. On the other hand, people in their 20 s and students, who are not financially stable, don t make more trips on weekends. In the future, the number of old age group will increase due to medical progress. Also, other factors which can make positive effect on weekend trip such as increase in income, cars, and holidays will create more demands on weekend traffic. Therefore, transportation planners need to consider weekend trip as important as weekday trip in making policies and it is necessary to forecast individual characteristic factors of weekend travel. 117