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A study on the influences of highway to car use and daily activities

A study on the influences of highway to car use and daily activities. Kuniaki Sasaki Kazuo Nishii Hiromu Sakai Ryuichi Kitamura Univ. of Yamanashi. Background. Kyoto is the historical city which attracts the most sightseeing tourists in Japan. The population of Kyoto is about 1.4 million.

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A study on the influences of highway to car use and daily activities

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  1. A study on the influences of highway to car use and daily activities Kuniaki Sasaki Kazuo Nishii Hiromu Sakai Ryuichi Kitamura Univ. of Yamanashi

  2. Background • Kyoto is the historical city which attracts the most sightseeing tourists in Japan. • The population of Kyoto is about 1.4 million. • The highway in Kyoto is heavily congested. • Two highways opened in 2003 at the south suburban area of Kyoto. • The change of road circumstance would affect the daily life of inhabitants’.

  3. The highway network in Kyoto Urbanized and historical area highways New exp. way (16.6km) Suburban area Railways

  4. The growth of car ownership in Kyoto area Million

  5. Purpose of this study • To Measure the impact of opening of highway to the inhabitant’s daily life Time constraints of car use may relax in the area. Will this relaxation cause any change of car use ? consequently cause changes in activity ?

  6. Activity Diary (AD) survey in one week • The car use is related deeply to activity pattern. Activity pattern is important to analyze change of car use. • Necessity of one week diary survey is • Effective to see the actual car use • Possible to see the difference and relation between weekday and weekend

  7. The flow of this research 2002~2003 • 1st wave panel survey • Relationship analysis between trip pattern and car use • Time use modeling 2003~2004 • 2nd wave panel survey • Relationship analysis between trip pattern and car use • Comparing the difference of relationships • Dynamic analysis of panel data HighwayOpen 30/Mar/2003

  8. Outline of the survey • 1st wave • Period: 4/Dec/2002~10/Dec/2002 • Area: suburban area of the south Kyoto • Sample size: 14142 individuals from 5052 HH • Household based individual survey • Household based analysis is the next theme • 2nd wave • Period: End of January 2004 Highway open on 30/Mar/2003

  9. AD questionnaire sheet 6：00 7：00 8：00 15 3045 15 3045 Bar chart type activity diary questionnaire 15-minute unit From 6:00 AM to 10:00 PM

  10. The number of trip and its mode share (weekday, worker) （persons） car 1200 Car 1000 rail 800 Rail 600 walk 400 Walk 200 others 0 Others 600 800 1000 1200 1400 1600 1800 2000 （time）

  11. The number of trip and its mode share (weekend worker) （persons） 1200 Car 1000 800 Rail 600 Walk 400 200 Others 0 600 800 1000 1200 1400 1600 1800 2000 （Time）

  12. Clustering trip patterns • Trip pattern characteristics are based on these four variables • Trip time by car • Trip time by other modes • The number of trips • Average trip length (by time) • Clustering by K-means method

  13. Trip pattern analysis of car users (2) three types of car use dominant groups Travel time by other mode Travel time by car weekday

  14. The characteristics of groups • Car dominant group average car time, Other time, trip number 1: Long trip type: 272 17 2.57 2: Multiple trip type 156 9 4.16 3: Short trip type 54 8 2.50 • Other Mode dominant group 4: Mass transit users 44 141 3.33

  15. The share of groups in weekdays and weekend

  16. Summary of trip pattern analysis • Automobile is mainly used in Weekends, especially, for shopping and leisure. • In the short trip (within 30 min.), 2/3 is shared by car • In both weekday and weekend, short trip group is the most share. • Shopping and private are the main purpose of car use in the group.

  17. Time use pattern clustering in each group • Based on trip pattern groups, time use pattern is clustered by K-means method. • The time use characteristics are the total time of activities as below. • In-home: Sleeping, meals, domesticity, study/work, leisure • Out-home: study/work, private, meals, shopping, leisure, other • Travel: total travel time

  18. Car dependency Group 1 Group 2 Group 3 Group 4 highlow Out home working Out home working Out home working Out home working 1st share 41％ 48％ 52％ 49％ Multiple activities Multiple activities Multiple activities In home domesticity 2nd share 28％ 17％ 17％ 21％ In home domesticity In home domesticity In home leisure In home leisure 3rd share 13％ 16％ 15％ 16％ In home leisure In home leisure Multiple activities In home domesticity 4th share 12％ 15％ 11％ 11％ Out home leisure In home work Out home leisure Out home leisure 5th share 6％ 4％ 5％ 3％ Activity pattern of each group(weekday)

  19. Car dependency highlow Time use pattern of each group(weekend) Group1 Group2 Group3 Group4 In home leisure In home leisure In home leisure In home leisure 1stshare 24％ 32％ 39％ 28％ domesticity Multiple activity domesticity domesticity 2ndshare 19％ 28％ 31％ 21％ Out home leisure domesticity Out home work Out home work 3rdshare 17％ 18％ 13％ 16％ Out home private Out home work Out home leisure Out home private 4thshare 16％ 9％ 7％ 14％ Out home private In home work Out home leisure In home hobby 5thshare 15％ 7％ 7％ 13％

  20. Summary of time use pattern analysis • The difference between weekday and weekend • There is not much difference in weekday • On weekend, the time use patterns vary in the trip pattern groups • The trip time may have relation with the time use pattern • Short time trip group → In home activity • Long time trip group → out home activity

  21. Time use modeling with the time constraints • Kitamura et. al (1997) type utility maximization model • The available time is 16 hours (6 AM to 10 PM) minus work/study time • Interaction to other people's activity is considered by using time dependent variable. aggregate share of activity on the free time location

  22. Variables in the model • Dependent variables • Total time use in 9 category of each sample • Independent variables • age, gender, life cycle stage, occupation, car ownership, trip characteristics

  23. The result and its summary of the time use model • Applied the model to each 4 trip type groups • Weekday: the estimated parameters' signs and its value → different • Especially, individual characteristics • Weekend: some of the value and signs →similar, But still different in many parameters

  24. Summary of this research • Car use in suburban area in Kyoto city is relatively high in weekends. • The purposes of car use are shopping and private activity of short range. • The trip characteristics are categorized into four type. • The time use patterns in each group vary in weekend.

  25. Future research • Collecting a time series data of road condition index and the time series data of circumstance such as land use. • Analyzing the 2nd panel data by the same method and comparing the result with that of the 1st wave. • Analyzing the dynamic change of relations between activity and travel. Present the results of these in the next SAKURA meeting

  26. Panel survey • To see the change of daily life and car use, it is necessary to collect activity data before and after highway opening. • To measure individual impact, panel survey is the most effective method. • “Before and after” type panel survey is adopted.

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