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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
Department of Transportation Engineering, Hanyang University
Ph.D. Student, Hyoung-Chul Kim
Professor, Ikki Kim
Overview of Dynameq
-1-
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-
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-
Case Study1
( Exclusive Median Bus-Lane Policy )
•
Pangyo IC
분석시간(Time span):
Suwon IC
16:00-21:00
•
분석지역(Spatial extent):
- 경부고속도로,
Gyeong-bu
Expressway
(Gyeong-bu Expressway)
판교IC-신탄진IC
(Pangyo-IC ~ Sintanjin-IC)
Sintanjin IC
-4-
Network
• Network:
- 요금체계가 폐쇄형(Close
System)인 23개 고속도로
• Centroid:
- 256개 영업소(Tollgate)
Centroid:
영업소(Tollgate)
< Dynameq Network>
-5-
Time Slice OD
•
•
•
Data: TCS(Toll Collection System) Data
Collection Period:
14:00-21:00,
February 4, 2005
Vehicle Classification
Car1
Car2
Car3
Car4
Car5
Car6
-6-
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-
Network Calibration (cont’d)
Time Slice
4:00-5:00 PM
5:00-6:00 PM
6:00-7:00 PM
7:00-8:00 PM
8:00-9:00 PM
Error Rate(ER) (%)
rate(%)
rate(%)
rate(%)
rate(%)
rate(%)
ER>=300
0.0
2.9
1.2
0.4
0.0
100<=ER<300
6.1
3.3
5.3
6.6
6.1
Over
60<=ER<100
1.2
2.0
2.4
2.9
3.3
Estimation
30<=ER<60
8.2
6.5
13.9
15.2
15.1
Under
10<=ER<30
12.7
0<=ER<10
19.3
-10<=ER<0
22.1
-30<=ER<-10
23.0
Estimation
Sum
•
20.0
21.6
14.7
77.0
23.0
15.1
78.8
23.3
20.8
28.6
20.1
70.6
21.2
12.7
13.1
68.9
15.6
10.2
15.1
68.2
11.4
-60<=ER<-30
3.7
2.9
2.9
2.5
3.7
-100<=ER<-60
3.7
3.7
3.7
3.7
3.7
ER<-100
0.0
0.0
0.0
0.0
0.0
100.0
100.0
100.0
100.0
100.0
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.
-8-
Network Calibration (cont’d)
Comparison of average travel time between origin and
destination using simulation results and observed TCS data
16:00-21:00 PM
Origin
•
Destination
Estimated Average Travel Time
Observed Average Travel Time
(min)
form TCS Data (min)
Seoul
Northern Suwon
31
15
Seoul
Daejon
96
76
Seoul
Dongdaego
166
176
Seoul
Gwangju
172
190
Seoul
Ulsan
217
234
Seoul
Southern Busan
231
280
Estimated average travel time is similar to the observed average travel time
so that DTA model can be applied in real transportation policies
-9-
Traffic Condition
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.
- 10 -
Policy scenarios
Pangyo IC
1. Scenario1:
Scenario2
- 판교IC-신탄진IC (Pangyo-IC ~ Sintanjin-IC)
Suwon IC
2. Scenario2:
- 판교IC-수원IC (Pangyo-IC ~ Suwon-IC)
3. Scenario3:
Scenario1
- 수원IC-신탄진IC (Suwon-IC ~ Sintanjin-IC)
<Assumption>
- 승용차에서 버스로의 수단전환율:
(Mode shift ratio from auto to bus:)
10%, 20%, 30%, 35%
Scenario3
Sintanjin IC
- 11 -
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 -
Result of Policy Scenarios
(Unit: Hour)
Mode Shift Ratio
(Auto ⇒ Bus)
10%
20%
30%
35%
Policy *
Scenario’s
Total Travel Time
(A)
Base Case’s
Total Travel Time
(B)
Scenario1
1,729,140
+ 46.60%
Scenario2
1,617,246
+ 37,12%
Scenario3
1,481,528
+ 25.61%
Scenario1
1,576,643
+ 33.67%
Scenario2
1,488,673
+ 26.21%
Scenario3
1,356,760
(A) / (B) *100 (%)
+ 15.03%
1,179,474
Scenario1
1,424,146
Scenario2
1,360,100
+ 15.31%
Scenario3
1,231,991
+ 4.45%
Scenario1
1,347,897
+ 14.28%
Scenario2
1.295,813
+ 9.86%
Scenario3
1,169,607
- 0.84%
* Scenario 1: Pangyo-IC ~ Sintanjin-IC
* Scenario 2: Pangyo-IC ~ Suwon-IC
* Scenario 3: Suwon-IC ~ Sintanjin-IC
+ 20.74%
- 13 -
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 -
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 -
Network, OD
•
This sample network and OD are designed for traffic
condition of special traffic period
- 16 -
Methodology
Start
Tollgate Booth Open-close Metering
Historical Data
New Time
Slice OD
Time Slice OD
DTA
Selected Link
Analysis
DTA
LOS > D ?
Observed Link
Flows
No
H=H+1
Yes
H=T ?
No
Yes
End
- 17 -
Methodology (cont’d)
Reduction Selected OD matrix
Tollgate Booth Open-close Metering
KHCM, 2004
Current Condition < LOS D
Ex) Density ≥ 19 (pc/km/lane)
Selected link Analysis
O
D
Trips
2
8
100
4
11
300
7
9
200
ː
ː
ː
- 18 -
Result
A
Comparison of speed between before and after case on each
time slice
07:30
09:15
(Time slice)
After Case:
100 km/h
Before Case:
79 km/h
(Km/h)
Before Case
After Case
X axis: time slice
Y axis: (Average travel speed – Designed speed(100 km/h) )
- 19 -
Result
A
Comparison of density between before and after case on each
time slice
Density
(pc/km/lane)
Before
After
Before Case:
- 59(pc/km/lane)
- LOS F
After Case:
- 13 (pc/km/lane)
- LOS C
(Time slice)
06:00
11:45
X axis: time slice
Y axis: Density (pc/km/lane)
- 20 -
Result
Comparison of total travel time between before and after case
Before
Origin
Difference
(After-Before)
After
Destination
Trips
(Vehicle)
Travel Time
(Sec)
Total Travel Time
(Sec)
Trips
(Vehicle)
Travel Time
(Sec)
Total Travel Time
(Sec)
Total Travel Time
(Sec)
1
2
113
81.778
9,240.914
75
81.768
6,132.600
-3,108.314
1
3
88
79.195
6,969.160
58
79.175
4,592.150
-2,377.010
：
：
：
：
：
：
：
：
：
2
1
57
81.566
4,649.262
38
81.564
3,099.432
-1,549.830
2
3
80
76.534
6,122.720
61
76.526
4,668.086
-1,454.634
：
：
：
：
：
：
：
：
：
422,573
680,815.209
74,045,158.193
427,811
612,240.129
63,468,636.051
-10,576,522.142
Total
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 -
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 -
Thank you
for your attention !