Building a TDM Impact Analysis System
for the Introduction of a Short-Term
Congestion Management Program in Seoul
Jin-Ki Eom, Kee-Yeon Hwang, Ikki Kim
Researcher of Seoul Development Institute (SDI)
San4-5, Yejang-dong, Jung-ku, Seoul 100-250, Korea
E-mail : eom@sdi.re.kr
Seoul Development Institute
Outline
 SCMP(Sort-term Congestion Management Program)
 SECOMM(Seoul Congestion Management Model)
 SECOMM Case Study
 Conclusion
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Why Seoul needs SCMP ?
Short-term Congestion Management Program
 Seoul has been known for the notoriety of its severe traffic
congestion. In order to mitigate the congestion problems, the
transportation policy of Seoul Metropolitan Government had
been mainly focused on the supply of transportation systems
until the early 1990’s.
 The sharp decrease of investment on transportation
infrastructures followed by recent economic recession.
 The massive implementation of subway system does not
reduce auto rider-ship as much as we expected.
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2. SCMP
(Short-term Congestion Management Program)
1. Setting up Short-term Target of Traffic Management
2. Selecting TDM Programs to Reduce the Excessive Auto
Demand
3. Building a Methodology for Forecasting the Expected
Impacts of Programs(ex. SECOMM)
4. Monitoring Traffic Conditions Regularly
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3. SECOMM
(SEoul COngestion Management Model)
 Assumption
 Structure of the SECOMM
 Mode Split Model
 Assignment Model
 Link Travel Speed Adjustment Function
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Assumption
The assumptions of SECOMM are as follows
 Mode split and route choice are variable
while trip generation and trip distribution
are not in short-run
 Investment is fixed in the short-run
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Structure of the SECOMM
Household Travel
Survey
Travel Speed
Survey Data
in Seoul
Data Clearance
Short-term Traffic
Management Target
Modal Split Model
(A-Logit)
EMME/2
Macro
Setting TDM
Alternatives
Forecasting
Prospective
Implement
Alternatives
Monitoring/Analysis
If Satisfy the goal
of Strategy
No
Yes
END
Seoul Development Institute
Data Requirements
Data
Format
Date
Source
’96 Seoul Metropolitan O/D by Modes
- Peak Hour and Non-Peak Hour
Day/hour
1997.10 –
1998.3
’96 Household Travel Diary Survey
- Travel Preference and Individual 1 week
Trip Data (Wednesday)
Day
1997.10 –
1998.3
Seoul Metropolitan Road Network Data
Transit Route and Network Data
Hour
1996.11 –
1998.4
A Day Traffic Volume at Namsan #1.3
Tunnel
Hour
1996.11 –
1998.4
Bus & Subway Ridership Data &
Traffic Volume Survey Data in Seoul
Hour
1996,
1997
Traffic Speed Survey Data in Seoul
Hour
1996,
1997
Seoul
Development
Institute
Seoul
Development
Institute
Seoul
Development
Institute
Seoul
Metropolitan
Government
Seoul
Metropolitan
Police
Seoul
Metropolitan
Government
Index
Trip
/Travel
Pattern
Network
Traffic
Volume
Speed
Seoul Development Institute
Process of Building Mode Split Model
Variable Selection
Data Clearing
Generating Unknown Travel
Time & Cost
of Alternative Travel Modes
Mode Split of Cleared Data Set
Building Data File
(Convert Data into A-Logit Format)
Building Control File
1: MNL Method / 2 : Nested Method
Variables
Adjustment
A-Logit Run
Satisfy Stopping Criteria
No
Yes
Utility Functions and Parameters are Set
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Logit Model (1)
Nested Tree for Each Alternative


Car
Bus
Subway
Taxi
Car
Taxi
Bus


Car
Bus
Subway
Subway
Taxi
Car
Bus
Subway
Taxi
Ui  Vari  1TTimei   2TCosti
Ui
Var i
Ttime i
TCost i
1 2
:
:
:
:
:
utility function of mode i
constant(except bus) of mode i
total travel time(min) of mode i
total travel cost(won) of mode i
Coefficient of independent variable
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Logit Model (2)
The Parameter Values and T-Values of Nested-Logit Models
Variable
Cost
Time
Auto
Dummy
Parameter
Value
-0.000175
-0.03417
-0.6845
T-Value
-5.6
-21.9
-5.9
-14.3
-8.7
21.6
-
Parameter
Value
-0.00023
-0.03988
-0.9568
-0.9200
-1.979
0.5084
0.3897
T-Value
-5.0
-20.9
-4.1
-14.1
-6.4
7.9
5.1
Parameter
Value
0.000083
-0.03753
0.0717
-0.8515
-1.7600
0.0445
11.61
T-Value
2.8
-21.8
0.3
-14.2
-8.4
2.8
9.9
Parameter
Value
-0.000124
-0.03472
-0.6525
-0.8361
-2.173
0.8391
-
T-Value
-3.3
Value




-21.8
-5.5
Subway
Dummy
Taxi
Dummy
1
2
-0.8317
-2.211
0.9065
-
-14.3
-9.1
16.8

_
2
0.2434
0.1078
0.2489
0.1143
0.2469
0.1119
0.2440
0.1085
2
-
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Assignment Process
Traffic Index
Adjustment
Pre-Network
Pre-Network
Build
Build
Trip Index
Trip Index
Highway
Highway
Network
Network
Transit Network
Network
-Transit
Bus, Subway
- Bus, Subway
`96 Traffic Census
Traffic
Census
- `96
Peak
hour O/D
- Peak
hour O/D
-1
Day O/D
- 1 Day O/D
Management Region
Management
Region
- Link, Zone
- Link, Zone
Link Group
LinkVolume
Group
- Speed,
- Speed, Volume
Traffic
Traffic
Management
Management
Index
Confirm
Index Confirm
Model Calibration
All Mode OD
Mode
(PeakAll
Hour
/ 1 OD
Day)
(Peak Hour / 1 Day)
Auto OD
Auto OD
Taxi OD
Taxi OD
Vehicle Occupancy
Vehicle
Occupancy
for Each
Mode
for(PCU
Each)Mode
(PCU )
Highway
Highway
Assignment
Assignment
Assignment Result for
Assignment
Result for
Each Link
Each
Link
(Volume,
Speed,
Time)
(Volume, Speed, Time)
No
Subway OD
Subway OD
Transit OD
Transit OD
Link
Link
Performance
Performance
Function
Function
Highway OD
Highway OD
Highway
Highway
Network
Network
Bus OD
Bus OD
To Apply
To Apply
Weight
Weight
Factor
Factor
(Wait
time
(Wait
time
Access
time,
Access time,
Boarding/
Boarding/
Alighting
Alighting
time)
time)
To Compare Present
To Compare Present
Transit
Transit
Network
Network
Transit
Transit
Assignment
Assignment
Assignment Result
Assignment
Result
for Each Route
for Each
Route
(Travel
Time,Cost)
(Travel Time,Cost)
No
Yes
STOP
STOP
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Process of Predicting Link Travel Speed
Using the adjustment factor, We can predict link travel speed
S Lobs
 f
est
SL
Slpre  Slest  f
Where, S Lobs : Observed Average Speed of Link Group L
S Lest : Estimated Average Speed of Link Group L to Use
Estimated Link Volume from Assignment Result
S lpre : Predicted Average Speed of Link l
f : Adjustment Factor
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4. SECOMM Case Study
Study Title : Impact Analysis of Gasoline Tax Increase
 Study Process
 Structure of Emme/2 Macro
 Study Results
Seoul Development Institute
Study Process
0. Initiation
Building Mode Split
Model
Network Calibration
Before Tax Increse
Variable Parameter
Input
Network Calibration
Results Saving
1. Gasoline Tax Increase
2. New Mode Split Ratio Calculation Using
Parameters of Mode Split Model
3. Rebuilding Trip O/D of
Each Mode
4. Run Assignment
Auto-Assignment
Transit-Assignment
Saving Automobile
Travel Time
Saving Transit
Travel Time
Adjust factor
5. New Mode Split Ratio Calculation Using
Parameters of Mode Split Model
6.Saving Difference of Mode
Split Ratio
7.If Satisfy Stoping
Conditions
No
Yes
S TOP
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Structure of Emme/2 Macro
Emme/2 Macro TREE
Emme/2 Macro TREE
0) Main Macro
1) Initialized
Macro
2) Assigment
Macro
1)
Submacro
3) Time/Cost
Calclation
Macro
4) Mode Split
Calibration
Macro
5) Rebuild Trip
Macro
2)
3)
Submacro
4)...
Submacro
Sub-submacro
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Case Study Results (1)
The Speed Changes by Gasoline Tax Increase
Average Speed (km/h)
Category Current
Speed
Mileage Tax Charging
$0.1 / l l Increase
$0.2 / l l Increase
$0.3 / l l Increase
Speed
Change
Rate
Speed
Change
Rate
Speed
Change
Rate
CBD
20.18
20.36
+0.2
0.9
20.39
+0.2
1.0
20.61
+0.4
2.1
Arterial
23.97
24.21
+0.2
1.0
24.34
+0.4
1.5
24.64
+0.7
2.8
Urban
Highway
42.54
43.08
+0.5
1.3
43.53
+1.0
2.3
44.18
+1.6
3.9
Total
Average
In Seoul
25.19
25.33
+0.1
0.6
25.72
+0.5
2.1
26.10
+0.9
3.6
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Case Study Results (2)
Auto-Mode Split Ratio Changes Resulting from Gasoline Tax Increase
21.0%
Before
After (the stable stage)
20.5%
$0.1/L
$0.2/L
20.0%
$0.3/L
19.5%
19.0%
18.5%
iteration 0
After (the first stage)
1
2
3
4
5
6
Seoul Development Institute
Monitoring Data
Response to Oil Price Increased
200
0.6
100
0.4
0
0.2
-100
0
-200
-0.2
-300
-0.4
0
5
10 15 20 25 30 35 40 45 50 55 60
<Response of Car to OIL_P>
0
5
10 15 20 25 30 35 40 45 50 55 60
<Response of SPEED to OIL_P>
Seoul Development Institute
Case Study Results (3)
Peak Hour Auto Volume Changes Resulted by Gasoline Tax Increase
Before
$0.1 / l l
Increase
$0.2 / l l
Increase
$0.3 / l l
Increase
Peak Hour
Automobile
Volume
(Vehicle/hour)
275,491
270,493
268,329
262,420
Decreasing
Volume
(%)
-
-4,998
(-1.8)
-7,162
(-2.6)
-13,071
(-4.8)
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5. Conclusions
 SECOMM is a TDM impacts analysis system integrating mode
choice model and trip assignment model in a module and iterating
the interactions between them until the stop conditions are
accomplished.
 Using SECOMM, we can quickly forecast the impacts of TDM
therefore, we can implement SCMP in Seoul.
 To enhance the usefulness of SECOMM, there are several things
to be done:
 checking the estimated results of SECOMM through
continuous monitoring on traffic situation in Seoul
 updating the O-D data at least every 5 years
 updating the network and travel behavior data
Seoul Development Institute