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Assessing the Impacts of Traffic Mitigation Measures in Metro Manila through Multi-level Modeling Dr. Noriel Tiglao Citilabs 2012 Asia User Conference,

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Presentation on theme: "Assessing the Impacts of Traffic Mitigation Measures in Metro Manila through Multi-level Modeling Dr. Noriel Tiglao Citilabs 2012 Asia User Conference,"— Presentation transcript:

1 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

2 Outline Metro Manila Urbanization Travel Demand Patterns Nature of Traffic Congestion Problem Transport Modelling Issues Need for Multi-level Modelling Benefits of Cube 2

3 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 3

4 Metro Manila 4 Land Area: 636 sq. km Population (2007): million Population Density (2007): 18,166 persons/ sq. km

5 5 Land Area: 38,544 sq. km Population (2000): 27.4 million Population Density (2000): 712 persons/ sq. km 100-Km radius ‘Mega’ Manila

6 Metro Manila Traffic Demand 6 Source: MMUTIS (1999)

7 Travel Demand Pattern Drastic increase in motorized trips in Metro Manila – From 10.6 million trips in 1980 to million trips in 1996 Serious increase in car ownership – From 10% in 1980 to 20% in % 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 7

8 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 8 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.

9 C1 C2 C3 C4 C5 NLEX SLEX CAVITEX Metro Manila Road Network 9

10 Traffic Congestion Problem Vehicle volume Pedestrian flows Truck traffic Stalled vehicles Roadside parking Oversupply of buses Motorcycle traffic Intersection treatment Poor pavement quality Uneven road geometry 10

11 Traffic Mitigation Measures 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 – ??? 11

12 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 12

13 Need for Multi-level Modelling 13

14 Multi-level Modelling - Macroscopic 14 Subarea extraction

15 Multi-level Modelling - Mesoscopic 15 Packet log analysis Packet log animation

16 Multi-level Modelling - Microscopic 16 Proposed Grade Separation

17 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 decision- making – Perform sensitivity analysis, share data across various data formats 17

18 Thank you! 18


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