Assessing the Impacts of
Traffic Mitigation Measures
in Metro Manila through
Multi-level Modeling
Dr. Noriel Tiglao
Citilabs 2012 Asia User Conference, Sept.4-7
Plaza Athénée Bangkok, A Royal Méridien Hotel, Bangkok, Thailand
Outline
•
•
•
•
•
•
Metro Manila Urbanization
Travel Demand Patterns
Nature of Traffic Congestion Problem
Transport Modelling Issues
Need for Multi-level Modelling
Benefits of Cube
2
Metro Manila Urbanization
• High population growth rates and in-migration
• 13 percent of the country’s population are packed
in only about 0.2 percent of the country’s land
area
• Metro Manila dominates the economy accounting
for 43.5 percent of the country’s GDP in 2000
• The effect of rapid urbanization of the metropolis
spilled over the adjoining municipalities
• Comprised of 17 cities and municipalities
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Metro Manila
Land Area: 636 sq. km
Population (2007): 11.55 million
Population Density (2007): 18,166 persons/ sq. km
4
‘Mega’ Manila
100-Km radius
Land Area: 38,544 sq. km
Population (2000): 27.4 million
Population Density (2000): 712 persons/ sq. km
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Metro Manila Traffic Demand
Priva te
Se m i
Pu b lic
Pu b lic
M od e
M oto rc yc le
1/
C a r/Jee p + U V
T ru c k
S u bto tal
Taxi
H O V Taxi
P riv a te B u s
S u bto tal
T ric yc le
Jee p ne y
B us
LR T
PNR
S u bto tal
T ota l
Person T rip s
N o . (0 0 0 )
(% )
1 25
0 .7
3 ,2 89
1 8.5
4 22
2 .4
3 ,8 36
2 1.6
8 62
4 .9
2 26
1 .3
4 40
2 .5
1 ,5 28
8 .6
2 ,3 73
6 ,9 52
2 ,6 53
4 09
6
1 2 ,394
1 7 ,758
1 3.4
3 9.1
1 4.9
2 .3
0 .0
6 9.8
1 0 0 .0
A v era g e
O c c u pa n c y
1 .1
2 .5
2 .1
2 .2
4 .7
2 2.3
2 .5
1 5.1
4 6.5
-
N o. (0 00 )
111
1 ,3 3 0
203
1 ,6 4 3
396
48
20
464
938
461
57
1 ,4 5 6
3 ,5 6 4
V e hic le T rip s
(% V eh ic le)
3 .1
3 7.3
5 .7
4 6.1
1 1.1
1 .4
0 .6
1 3.0
2 6.3
1 2.9
1 .6
4 0.9
1 00 .0
2/
(% P C U )
5 .7
3 4.3
1 3.1
5 3.2
1 0.2
1 .9
1 .0
1 3.1
1 2.1
1 7.9
3 .7
3 3.7
10 0 .0
S o urc e : M M U T IS P ers o n T rip S u rv e y
1 / U V – u tility v e h ic le
2 / P C U – P asse nge r C a r U n it: c o n v e rsio n o f d iffere nt s ize s of v e h ic le s in ter m s o f ca r siz e fo r co m p a r is o n.
Source: MMUTIS (1999)
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Travel Demand Pattern
• Drastic increase in motorized trips in Metro
Manila
– From 10.6 million trips in 1980 to 16.95 million trips in 1996
• Serious increase in car ownership
– From 10% in 1980 to 20% in 1996
• 6.75% average growth rate in vehicle registration
for Metro Manila from 1990 to 2005
• Higher vehicle registration growth rates in
adjacent regions
• Alarming increase in motorcycle share since
2005, accounting for almost half of the vehicle
fleet
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Economic Cost of Traffic
Congestion
• 100 Billion Pesos (in 1996 values) is lost each
year due to road congestion
– Roughly 2% of GDP
– Based on travel time delays and 50% of hourly income
across different occupation groups
– In addition, reduction in the urban quality of life increases
health and living costs
Source: Economic Impact of Traffic Congestion in Metro Manila,” A Study conducted by University of the Philippines National Center for
Transportation Studies (NCTS) for the NEDA Legislative Executive Development Advisory Committee (LEDAC), 2000.
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Metro Manila Road Network
NLEX
Historical Travel Speed along EDSA, 2004-2012
45
40
C3
C1
C4
C5
Travel Speed (kph)
35
30
25
NB
20
SB
15
Average
10
C2
5
0
2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Historical Corridor Traffic, 2006-2011
Area
CAVITEX
SLEX
Year
2006
EDSA
176,343
East of EDSA
587,273
West of EDSA
982,863
Source: MMDA-TEC
2007
2008
2009
2010
2011
201,495
583,325
960,521
205,242
684,028
999,294
218,310
751,610
1,146,993
224,938
713,042
1,100,605
219,553
834,982
1,141,813
Traffic
Growth Rate
(2006-2011)
4.48%
7.29%
3.04%
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Traffic Congestion Problem
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Vehicle volume
Pedestrian flows
Truck traffic
Stalled vehicles
Roadside parking
Oversupply of buses
Motorcycle traffic
Intersection treatment
Poor pavement quality
Uneven road geometry
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Traffic Mitigation Measures
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Vehicle volume – Color coding, UVVRP
Pedestrian flows – Pedestrian barriers, Footbridges
Truck traffic – Truck ban
Stalled vehicles – Emergency bays
Roadside parking – (Curbside development)
Oversupply of buses – (Yellow lanes)
Motorcycle traffic – (Motorcycle lanes)
Intersection treatment – (U-turns, grade separation)
Poor pavement quality – (Pavement rehabilitation)
Uneven road geometry – ???
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Transport Modelling Issues
• Nowadays, agencies require thorough Traffic
Impact Analysis as part of the Feasbility
Study
– Important to consider economic and social
considerations
• In heavily built-up areas/congested areas,
justifying (costly) isolated interventions is a
challenge
– Case in point: a flyover/underpass for through traffic
• Greater need to analyze a combination of
mitigation projects at various level
– e.g. network expansion in one area and intersection
treatment in another
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Need for Multi-level Modelling
Macroscopic
• Region-Wide analysis
• Evaluation of
network effects
Mesoscopic
• Dynamic Traffic
Assignment
• Queuing Analysis
Microscopic
• Traffic operation level,
e.g. Signal design
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Multi-level Modelling - Macroscopic
Subarea extraction
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Multi-level Modelling - Mesoscopic
Packet log animation
Packet log analysis
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Multi-level Modelling - Microscopic
Proposed Grade
Separation
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Benefits of Cube
• Consistency and flexibility in terms of
modelling networks, flows, time, and travel
behavior
• Increase response rate, cost-effective
– Analyze many scenarios in shorter time periods
• Results are easily accessible for decisionmaking
– Perform sensitivity analysis, share data across various
data formats
17
Thank you!
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