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Ph.D. Student, Hyoung-Chul Kim Professor, Ikki Kim

South Korea Emme User’s Conference April 21, 2010. Dynamic Analysis for Exclusive Median Bus-lane Policy during Weekday and Tollgate Booth Open-close Metering Policy in Korea National Freeway Network with Dynameq. Ph.D. Student, Hyoung-Chul Kim Professor, Ikki Kim.

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Ph.D. Student, Hyoung-Chul Kim Professor, Ikki Kim

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  1. South Korea Emme User’s Conference April 21, 2010 Dynamic Analysis for Exclusive Median Bus-lane Policy during Weekday and Tollgate Booth Open-close Metering Policy in Korea National Freeway Network with Dynameq Ph.D. Student, Hyoung-Chul Kim Professor, Ikki Kim Department of Transportation Engineering, Hanyang University

  2. Overview of Dynameq - 1 -

  3. I/O Data and Model of Dynameq • Input Data: • - traffic demand, network definition, traffic control plans • Outputs: • - simulation results, path-based results • Path-Choice Model • Traffic Simulation: • - car-following, lane-changing, gap-acceptance - 2 -

  4. Outline • Case Studies of Dynamic analysis in Korea national freeway network with Dynameq • Case Study 1: • Exclusive Median Bus-Lane Policy during weekday • Case Study 2: • Tollgate Booth Open-close Metering Policy - 3 -

  5. Case Study1 • ( Exclusive Median Bus-Lane Policy ) PangyoIC • 분석시간(Time span): • 16:00-21:00 • 분석지역(Spatial extent): • - 경부고속도로, • (Gyeong-bu Expressway) • 판교IC-신탄진IC • (Pangyo-IC ~ Sintanjin-IC) SuwonIC Gyeong-bu Expressway SintanjinIC - 4 -

  6. Network • Network: • - 요금체계가 폐쇄형(Close System)인 23개 고속도로 • Centroid: • - 256개 영업소(Tollgate) Centroid: 영업소(Tollgate) • < Dynameq Network> - 5 -

  7. Time Slice OD • Data: TCS(Toll Collection System) Data • Collection Period: • 14:00-21:00, • February 4, 2005 Car1 Car2 Car3 • Vehicle Classification Car4 Car5 Car6 - 6 -

  8. Network Calibration • Network Calibration was performed by modifying the free flow speed, capacity of each link. • Error rate was calculated using DUE flows • and observed flows on each link at time slice - 7 -

  9. Network Calibration (cont’d) • Result of network calibration is reasonable in reflecting real traffic conditions because most of all link’s error rate is within 30% at each time slice. - 8 -

  10. Network Calibration (cont’d) • Comparison of average travel time between origin and destination using simulation results and observed TCS data • Estimated average travel time is similar to the observed average travel time so that DTA model can be applied in real transportation policies - 9 -

  11. Traffic Condition • 8:00-9:00 PM • 5:00-6:00 PM • 7:00-8:00 PM • 6:00-7:00 PM • Above figures shows that the link flows are represented by bar and color theme • After 7:00 PM, Congestion begins to be dissolved gradually up to a certain point. - 10 -

  12. Policy scenarios PangyoIC • 1. Scenario1: • - 판교IC-신탄진IC (Pangyo-IC ~ Sintanjin-IC) • 2. Scenario2: • - 판교IC-수원IC (Pangyo-IC ~ Suwon-IC) • 3. Scenario3: • - 수원IC-신탄진IC (Suwon-IC ~ Sintanjin-IC) Scenario2 SuwonIC Scenario1 • <Assumption> • - 승용차에서 버스로의 수단전환율: • (Mode shift ratio from auto to bus:) • 10%, 20%, 30%, 35% Scenario3 SintanjinIC - 11-

  13. Measurement • Car and Bus mode were just considering. • The formula for computing total travel time Where, = Link m = Mode (Auto, Bus) = Mode(m)’s Occupancy(person) T= Total Travel Time = Mode(m)’s Travel Time on link = Mode(m)’s Volume on link - 12 -

  14. Result of Policy Scenarios (Unit: Hour) * Scenario 1: Pangyo-IC ~ Sintanjin-IC * Scenario 2: Pangyo-IC ~ Suwon-IC * Scenario 3: Suwon-IC ~ Sintanjin-IC - 13 -

  15. Conclusion • This study analyzes various scenarios based on DTA model and real time data from TCS • The result represents Median Bus-lane policy is meaningful when Mode Shift Ratio from auto to bus is greater than 35% • Since It means that the overall public transportation policies need to make a mode shift from Auto to Bus. - 14 -

  16. Case Study2 • (Tollgate Booth Open-close Metering Policy ) Background • Extreme congestion makes mobility limited • Traffic congestion causes Huge social cost and many traffic accident • So, it is necessary for studying how to improve the mobility on expressway - 15 -

  17. Network, OD • This sample network and OD are designed for traffic condition of special traffic period - 16 -

  18. Methodology • Tollgate Booth Open-close Metering Start Historical Data New Time Slice OD Time Slice OD DTA Selected Link Analysis DTA No Observed Link Flows Yes LOS > D ? Yes No H=T ? End H=H+1 - 17 -

  19. Methodology (cont’d) • KHCM, 2004 • Reduction Selected OD matrix Current Condition < LOS D Ex) Density ≥ 19 (pc/km/lane) • Tollgate Booth Open-close Metering Selected link Analysis - 18 -

  20. Result A • Comparison of speed between before and after case on each time slice 07:30 09:15 After Case: 100 km/h (Time slice) Before Case: 79 km/h (Km/h) • X axis: time slice • Y axis: (Average travel speed – Designed speed(100 km/h) ) Before Case After Case - 19 -

  21. Result A • Comparison of density between before and after case on each time slice Before After Before Case: - 59(pc/km/lane) - LOS F Density (pc/km/lane) After Case: - 13 (pc/km/lane) - LOS C (Time slice) 11:45 06:00 • X axis: time slice • Y axis: Density (pc/km/lane) - 20 -

  22. Result • Comparison of total travel time between before and after case • Measurement: Total Travel Time Difference between before and after case • Result: -2,938 Hour (or -10,576,522 Sec) • Thus, the highway entrance-exit control policy is efficient - 21 -

  23. Conclusion • This study shows the effect of access control on tollgate under DTA with virtual network • In the result of simulation using Dynameq, we can find out the improvement effects of speed and density on link when access control on tollgate is adopted • The following study needs to analyze the actual large network with real time data such as TCS - 22 -

  24. Thank youfor your attention !

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