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

2. Overview of Dynameq - 1 -

3. - 2 - 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 I/O Data and Model of Dynameq

4. Outline - 3 - 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

5. Case Study1 - 4 - 분석시간 (Time span): 16:00-21:00 분석지역 (Spatial extent): - 경부고속도로, (Gyeong-bu Expressway) 판교 IC- 신탄진 IC (Pangyo-IC ~ Sintanjin-IC) Gyeong-bu Expressway Pangyo IC Sintanjin IC Suwon IC ( Exclusive Median Bus-Lane Policy )

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

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

8. Network Calibration - 7 - 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

9. Network Calibration (cont’d) - 8 - Time Slice 4:00-5:00 PM5:00-6:00 PM6:00-7:00 PM7:00-8:00 PM8:00-9:00 PM Error Rate(ER) (%) rate(%) Over Estimation ER>=3000.02.91.20.40.0 100<=ER<3006.13.35.36.66.1 60<=ER<1001.22.02.42.93.3 30<=ER<608.26.513.915.215.1 10<=ER<30 12.7 77.0 20.0 78.8 21.6 70.6 23.0 68.9 28.6 68.2 0<=ER<10 19.314.715.120.113.1 Under Estimation -10<=ER<0 22.123.321.215.615.1 -30<=ER<-10 23.020.812.710.211.4 -60<=ER<-303.72.9 2.53.7 -100<=ER<-603.7 ER<-1000.0 Sum100.0 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.

10. Network Calibration (cont’d) - 9 - OriginDestination 16:00-21:00 PM Estimated Average Travel Time (min) Observed Average Travel Time form TCS Data (min) SeoulNorthern Suwon3115 SeoulDaejon9676 SeoulDongdaego166176 SeoulGwangju172190 SeoulUlsan217234 SeoulSouthern Busan231280 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

11. Traffic Condition - 10 - 5:00-6:00 PM 6:00-7:00 PM 7:00-8:00 PM 8:00-9:00 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.

12. Policy scenarios - 11 - 1. Scenario1: - 판교 IC- 신탄진 IC (Pangyo-IC ~ Sintanjin- IC) 2. Scenario2: - 판교 IC- 수원 IC (Pangyo-IC ~ Suwon-IC) 3. Scenario3: - 수원 IC- 신탄진 IC (Suwon-IC ~ Sintanjin-IC) Scenario2 Scenario3 Scenario1 Pangyo IC Sintanjin IC Suwon IC - 승용차에서 버스로의 수단전환율 : (Mode shift ratio from auto to bus:) 10%, 20%, 30%, 35%

13. Measurement - 12 - 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

14. Result of Policy Scenarios - 13 - Mode Shift Ratio (Auto ⇒ Bus) Policy * Scenario’s Total Travel Time (A) Base Case’s Total Travel Time (B) (A) / (B) *100 (%) 10% Scenario11,729,140 1,179,474 + 46.60% Scenario21,617,246+ 37,12% Scenario31,481,528+ 25.61% 20% Scenario11,576,643+ 33.67% Scenario21,488,673+ 26.21% Scenario31,356,760+ 15.03% 30% Scenario11,424,146+ 20.74% Scenario21,360,100+ 15.31% Scenario31,231,991+ 4.45% 35% Scenario11,347,897+ 14.28% Scenario21.295,813+ 9.86% Scenario3 1,169,607 - 0.84% (Unit: Hour) * Scenario 1: Pangyo-IC ~ Sintanjin-IC * Scenario 2: Pangyo-IC ~ Suwon-IC * Scenario 3: Suwon-IC ~ Sintanjin-IC

15. Conclusion - 14 - 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.

16. - 15 - 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

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

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

19. Selected link Analysis ODTrips 28100 411300 79200 ːːː Current Condition < LOS D Ex) Density ≥ 19 (pc/km/lane) KHCM, 2004 Methodology (cont’d) Tollgate Booth Open-close Metering Reduction Selected OD matrix - 18 -

20. Before Case After Case (Time slice) Result - 19 - 07:3009:15 A Comparison of speed between before and after case on each time slice X axis: time slice Y axis: (Average travel speed – Designed speed(100 km/h) ) Before Case: 79 km/h After Case: 100 km/h (Km/h)

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

22. Result - 21 - OriginDestination BeforeAfter Difference (After-Before) Trips (Vehicle) Travel Time (Sec) Total Travel Time (Sec) Trips (Vehicle) Travel Time (Sec) Total Travel Time (Sec) Total Travel Time (Sec) 1211381.7789,240.9147581.7686,132.600-3,108.314 138879.1956,969.1605879.1754,592.150-2,377.010 ：：：：：：：：： 215781.5664,649.2623881.5643,099.432-1,549.830 238076.5346,122.7206176.5264,668.086-1,454.634 ：：：：：：：：： Total422,573680,815.20974,045,158.193427,811612,240.12963,468,636.051 -10,576,522.142 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

23. Conclusion - 22 - 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

24. Thank you for your attention !