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Economic Impact of Traffic Congestion in Metro Manila

4. DATAANALYSIS

4.1 Source of Data

The Metro Manila Urban Transportation Integration Study (MMUTIS) hasbeen undertaken from 1996 to 1999. A reliable and comprehensivedatabase was established.

Most of the data can be plotted or interpreted on a map in a form of anarea, line or a point where GIS is appropriate. As such, the data areclassified according to their shape of their figure. In this classification, thedata classifications are as follows:

• Zone information data• Line information data• Point information data• 00 information data (Inter-zonal information data)• Area information data• Other non-geographical data

The principal sources of data used in the analysis are the 1996 MMUTISHousehold Interview Survey, and digitized zone boundaries on GeographicInformation System (GIS).

4.2 ZoningSystem

MMUTIS adopted different zoning systems for different analyses. Thelowest level consists of 394 zones as shown in Table 7. The correspondingzones can be aggregated to accommodate different planning levels.

Table 7.MMUTIS Detailed Zoning System

Study Area 316 zonesMetro Manila (265)Adjoining Areas (51)

External Area 78 zonesTotal 394 zonesSource: MMUTIS, 1996

For the purpose of this project, the 32-planning zone system was adopted.Figure 4 below shows the zoning system. The study area covers the entireMetro Manila and additional municipalities in the provinces of Bulacan,Rizal, Cavite and Laguna. Zone numbers 1 to 12 comprise Metro Manila,zones 13 to 20 comprise Bulacan, zones 21 to 26 comprise Rizal, zones27 to 30 comprise Cavite and the remaining zones 31 and 32 in Laguna.

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Figure 4.Zoning System

4.3 Data Structure

The 1996 MMUTIS HIS Data is organized into distinct information levelsaccording to the conduct of the interview. There are six levels of data usedin the study according to the interview form, namely

• Form 1 - Household Information• Form2 - Household Member Information• Form 3 - Trip Information• Form 4 - Additional Questions for Vehicles Users• Form 5 - Additional Questions for the Specially Abled and Elderly

People• Form 6 - Additional Questions for 1/10 Households (Holiday Traffic,

Living Environment, etc.)

More specifically, forms 1 to 3 has been extensively utilized in this study.

The household information includes household-specific data like number ofhousehold members by age group and gender, total monthly household


income, number of vehicles rented and owned, and ownership of houseand land.

The household member data includes member-specific data like age,gender, occupation, employment sector, monthly income, driver's licensetype and number of trips in a typical day.

The member trip data contains detailed information on the trips of ahousehold member on a typical day to include origin and destination, trippurpose, travel mode, starting and arrival times, and number of transfer.

The Household Interview Survey covered a sampling rate of about 2.5% ofthe total population in the study area. A total of about 30.4 million trips wasgathered for the entire sample.

4.4 Profile of Trip-makers

Figure 5 shows the total number of trips by income level of the household.It is observed that a high percentage of households fall within the P3,000 toP15,000 level. Almost 50 percent of the trip-makers have undeclaredincomes which consist of students, unemployed and persons who do notdeclared their incomes.

Number of Trips by Income Level

16.000.000

14,000,000

:!t 12,000,000

~ 10,000,000'0.8 8,000,000

E 6,000,000:::JZ 4,000,000

2,000,000

orI' ~~ OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J OJ<f/J <f~

.r'i! ...,,,:>. ...,(';). ..J:1I. ~. "OJ' fiJI' ,,:>OJ' «:,OJ· ..J:1IOJ· ~. O:JOJ· ,r:!>VJ" I..... ,.... ,'(- ~ ~ '(' ~ q '(- q~ 4' ~~

v ~~lJ5p,f~-K>~ ~~' ~~' ~~' ~~' ~~' ~~' ~~' ~~' A~''(- '(- ,,~, ,,(';)' ..rS>' p,~' ~. -'b~' ~.~. '('

~ ~ '(' x: q '(-~" q"

Income Level

,-

'.i!, 'l,l, r ! ; , ,

"'

-'~

"

1"- i-r 'l

m,: ,

lfl "I n _

Figure 5.Number of Trips by Income Level

Figure 6 shows the number of trips by occupation type. About one-third ofthe trip-makers are students. Another one-third consists of professionalsand workers in government and private sector. The remaining consists oftrips made by skilled workers, housewives, and jobless individuals.

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Number of Trips by Occupation

6,000,000

5,000,000IIICL

4,000,000;::•..0 3,000,000~E 2,000,000"z

1,000,000

Occupation

Figure 6.Number of Trips by Occupation

Figure 7 shows the number of trips by type of employment. A largepercentage of trip-makers have classification under undeclared!unemployment which are mostly students. The other larger sectors ofemployment include wholesale and retail trade and transport andcommunication,

Figure 8 shows the number of trips by trip purpose. A total of 46 percent ofthe trips are To-Home. The To-Work and To-School trips stand at 16percent each.

Figures 9 and 10 show the number of trips by institution of origin anddestination, respectively. The Residence is the single highest institution oforigin and destination consisting almost 50 percent of all origins anddestinations. The next highest are institutions of learning (i.e. schools,universities), then offices and wholesale and retail shops and factories.

It is surprising to note that commercial institutions seem to record lownumber of trips. On the contrary, a relatively large portion of trips underother institutions types.

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Number of Trips by Employment

18,000,000 "l;r!,,:':') ,16,000,000 +=:----"---'-'-...:.:.c..:.,..:.......,.;.,.,.,:.:.-J:~..".:.,.,.,.....:..~'~';'''~~~~.,.....:..----~-:14,000,00012,000,00010,000,0008,000,000 +-1,1-----------------------;6,000,0004,000,000 n2,000,000 I-----~---I r 1....

o ~~~~--,I~I~-_.~~~I,~...•~I,...•~~~~~~~...•~~-~n~-~__.

a'I:I-

o!E=z

Emplyment Sector

Figure 7.Number of Trips by Employment Sector

Number of Trips by Trip Purpose

16000000

14000000

VI12000000

a.'t: 10000000I-

~ 8000000!E 6000000,.z 4000000

2000000

i

.: ::

: , ',F' Ii ,:,::,1 F \ "

_Ff---

- f--,..., I I ,...,_ n ,...., -

Trip Purpose

Figure 8.Number of Trips by Trip Purpose

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Number of Trips by Institution of Origin

f-, ,

, ! ",,'i!' i,' " ii" ')'i, i ii i" ,

f- ,f-

f- ~f-

~ --•.....• I I 11 - I I -- I r

Origin Institution

Figure9.Number of Trips by Institution of Origin

Number of Trips by Institution of Destination

~

-'-- t :

i :'i i'" ir,--- - -- --- I I ,...., I I - I r

Destination Institution

Figure10.Number of Trips by Institution of Destination

Figures 11 and 12 show the starting and arrival times of the trips,respectively.

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4000000350000030000002500000200000015000001000000

500000o

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\:),,\5 \:)".J\5 \:)<f;)\5 ~\5 \:)0:>\5 ",,\5 ~\5 ,,<f;)\5 ~\5 ,,0:>\5 "v,,\5 ~\5 \:)\:)\5

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~f;;)\:)\5 n\:)\5 _b.~ r<p\5 &;,\:)\5\:)\:)\5 n\:)\5 b<\:)\5t;o\:)\5 <o\:)\5 ~\:)\5 n\:)\5 b<~

\5 \:)V ~' \5 \S ~ "V ~ ~ ~ ~ 0/ ~Starting Time

35000003000000

:t 2500000'Ct- 2000000'0! 1500000E'"z 1000000

500000

Number of Trips by Starting Time

'. . " '"

mir n1"111 n n

,

Figure 10.Number of Trips by Starting Time

Number of Trips by Arrival Time

, "Y";:' u ,., !

•..•_ ...n nl n n

Figure 12.Number of Trips by Arrival Time

20

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4.5 Analysis and Results

Figure 13 below shows the zone assignments of the Metro Manila regionfor analyzing origin-destination characteristics.

Figure 13.Metro Manila Zones

The analysis for cost of congestion can be analyzed based on delaysexperienced in commuting. The delays in commuting can be extracted fromactual travel times compared with ideal travel times for major origin-destination pairs. It is further argued that the delays experienced incommuting can be utilized to more productive uses. Furthermore, valuationof costs varies from individual to individual depending on the correspondingproductivity level or hourly income of the trip-maker.

There are 14 major classes of occupations adopted in the MMUTIS HIS.However, for the subsequent analyses, five main occupations areconsidered, namely:

• Government Officiall Executive Managers• Professionals• Technicians• Clerical Workers• Service Workers

Under each occupation type, major origin-destination pairs have beenidentified and ideal travel times are established by delineating a majorroute and an idealized travel speed of 35 kph.


Gov't Official/Exec. Managers

Clerical Workers

Professionals Technicians

Service. Workers .

Figure 14.Desire Lines by Occupation Type

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Five 0-0 pairs were considered for detailed calculations, namely: 5-3, 5-4,7-3, 7-5, and 3-11. The corresponding major trip-ends are Manila, QuezonCity (EOSA), Makati, Mandaluyong/ Pasig and Muntinlupa/ Las Pinaswhich correspond to trips from the southern part of Metro Manila and tripscoming from Cavite and Laguna. These major trip-ends also correspond tomajor heavily-used corridors like EOSA, South Superhighway, QuezonAvenue and Gil Puyat Avenue.

Table 8 below shows the average actual travel times by modecorresponding to each occupation type.

Table 8.Average Actual Travel Times

I

I

Public Transport Taxi

Gov't OfficialJeepney Mini-Bus Std. Bus

1.48 1.6 0.910.9 1.49 1.57Professionals

Technicians 0.93 1.65 1.62Clerical Workers 0.94 1.45 1.51

0.94:+---~-=+----:--=-=I1.05:t-----:o-::c=t----;--::-;i1.12

:t-----:o-::c=t----;--=i1.03

:t----::-::c=t----;-~0.92

Car Taxi HOVTaxi0.91 1.130.99 1.250.93 1.240.99 1.460.92 1.290.8 1.49 1.35

Table 9 shows the computed average airline distance and ideal traveltimes for the five identified major 0-0 pairs. A weighted ideal travel time of0.47 hours is established for the major trip-ends.

Table 9.Ideal Travel Times for Major 0-0 Pairs

Service Workers

Major Trip Pair Airline Distance(km)

5-3 10.405-4 6.367-3 20.107-5 10.623-11 15.18

Table 10 shows a summary of travel times and calculated average delays.Across all modes, the average travel time is calculated at 1.15 hours forthe identified trip-ends. The average delay is calculated at 0.68 hours.

Table 11 shows the calculation of total costs for congestion using averagemonthly incomes under each occupation type. The congestion costs isevaluated at 50 percent the hourly rate multiplied by the delay.

Ideal TravelTime (hrs.)

0.35---I

0.21c:o---i

0.67---I

0.35--~0.51-_....•

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Table 10.Calculation of Average Travel Time and Average Delay

Actual Travel Time (hrs.) Average AveragePT Car Taxi Travel Time Delay

Gov't Official 1.33 0.94 1.02 1.10 0.63(37.9%\ (34.6%) (6.2%\

Professionals 1.32 1.05 1.12 1.16 0.69(40.6%\ (33.5%) (7.6%\

Technicians 1.40 1.12 1.09 1.20 0.73(51.9%) (21.7%\ (5.9%\

Clerical Workers 1.30 1.03 1.23 1.19 0.72(57%) (15.8%\ (6.2%\

Service Workers 1.21 0.92 1.11 1.08 0.61(55.7%) (10.1%\ (3.6%)

Note: Values In parentheses Indicate modal share across occupation

Table 11.Calculation of Congestion Cost

Ave.Average Ave. Hourly Congestion Total Number Total Cost

Delay Income Cost! day of Tripsl day (in Plday)(in P) (in P)

Gov't Official 0.63 93.75 29.38 3,427,860 100,693,388Professionals 0.69 187.50 65.00 1,460,326 94,921,190Technicians 0.73 62.50 22.86 853,063 19,504,930Clerical Workers 0.72 50.00 17.88 1,134,083 20,271,734Service Workers 0.61 62.50 19.05 2,194,432 41,793,262

Table 11 indicates the total cost per day under each occupation group.The values multiplied by the number of days in the year (&65 is used in thisstudy) yields a yearly cost of around 100 Billion Pesos.

The present calculation is still crude at this stage, however, it presents agood picture of the present congestion levels in Metro Manila. Firstly,actual travel times are more than twice the ideal travel times with delaysranging from 0.6 to 0.73 hours. These levels may hold true for the identifiedmajor trip-ends but times should be longer for farther trip-ends. Secondly,the trip-ends of the pre-identified occupation types can be easilyestablished through a visual interpretation of the desire lines. This isbecause workplaces and residences are easily recognized. Lastly, Taxisand HOV Taxi provide a strong alternative to the private car with modalshares almost uniform across occupation types.

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Table 10.Calculation of Average Travel Time and Average Delay

Actual Travel Time (hrs.) Average AveragePT Car Taxi Travel Time Delay

Gov't Official 1.33 0.94 1.02 1.10 0.63(37.9%) (34.6%\ (6.2%)

Professionals 1.32 1.05 1.12 1.16 0.69(40.6%) (33.5%\ (7.6%)

Technicians 1.40 1.12 1.09 1.20 0.73(51.9%) (21.7%) (5.9%)

Clerical Workers 1.30 1.03 1.23 1.19 0.72(57%) (15.8%\ (6.2%)

Service Workers 1.21 0.92 1.11 1.08 0.61(55.7%) (10.1%) (3.6%)

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Note: Values In parentheses Indicate modal share across occupation

Table 11.Calculation of Congestion Cost

Ave.Average Delay Congestion Total Number

per Trip Ave. Hourly Cost of Trips Total Cost(hrs) Income (PhP per trip) (per day) (PhP per day)

Gov't Official 0.63 93.75 29.38 3,427,860 100,693,388Professionals 0.69 187.50 65.00 1,460,326 94,921,190Technicians 0.73 62.50 22.86 853,063 19,504,930Clerical Workers 0.72 50.00 17.88 1,134,083 20,271,734Service Workers 0.61 62.50 19.05 2,194,432 41,793,262

Total 9,069,764 0 277,184,5030x 365 days 101,172,343,741

Table 11 indicates the total cost per day under each occupation group.The values multiplied by the number of days in the year (265 is used in thisstudy) yields a yearly cost of around 100 Billion Pesos.

The present calculation is still crude at this stage, however, it presents agood picture of the present congestion levels in Metro Manila. Firstly,actual travel times are more than twice the ideal travel times with delaysranging from 0.6 to 0.73 hours. These levels may hold true for the identifiedmajor trip-ends but times should be longer for farther trip-ends. Secondly,the trip-ends of the pre-identified occupation types can be easilyestablished through a visual interpretation of the desire lines. This isbecause workplaces and residences are easily recognized. Lastly, Taxisand HOV Taxi provide a strong alternative to the private car with modalshares almost uniform across occupation types.


5. Recommendations

A new methodology has been proposed to establish the cost for congestionin Metro Manila. The model prescribed requires rather detailed anddisaggregated data from the MMUTIS Household Information Survey. Themodel is also sensitive with space data as detailed origin-destination dataare required as model inputs.

The study attempted to operationalize the model, however, severalsimplifications and assumptions were made in lieu of time and dataprocessing constraints. Although the study provides some monetary figuresto quantify congestion costs, there are still many factors that have to beincluded in the analysis. A further development of the analytical procedureis therefore proposed.

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