2177

A STUDY ON THE IMPACT AND EFFECTIVENESS OF
THE TRUCK BAN SCHEME IN METRO MANILA
Jun T. CASTRO
Research Associate
Dep’t of Social and Environmental Eng’g
Hiroshima University
1-4-1 Kagamiyama, Higashi Hiroshima-shi,
Hiroshima, 739-8527 Japan
Fax: +81-824-22-7194
E-mail: junc@naoe.hiroshima-u.ac.jp

Hirohito KUSE
Professor
Department of Info Engineering & Logistics
Tokyo University of Mercantile Marine
Etchujima, Koto-ku, Tokyo
135-8533, Japan
Fax: +81-3-5620-6462
E-mail: kuse@ipc.tosho-u.ac.jp

Tetsuro HYODO
Associate Professor
Department of Info Engineering & Logistics
Tokyo University of Mercantile Marine
Etchujima, Koto-ku, Tokyo
135-8533, Japan
Fax: +81-3-5620-6492
E-mail: hyodo@ipc.tosho-u.ac.jp
Abstract: This paper discusses the impact of the existing truck ban on Metro Manila’s urban
transport system and evaluates the effectiveness of the truck ban by comparing traffic patterns
in the form of loaded traffic flows in the road network for different truck ban schemes. Fiveschemes are examined, namely; existing truck ban, without truck ban, peak-hour truck ban
only, all-day truck ban at the primary road, and all day truck ban at the primary road and othermajor arteries. The paper also estimates the vehicle emissions of each alternative truck ban
scheme and compares them with the existing truck ban measure. General findings indicatethat additional restrictions significantly increase total vehicle-kilometers, total vehicle-hours,
and total pollutant emissions.
Key Words: freight transport, urban logistics, traffic assignment, truck ban
1. INTRODUCTION
As traffic congestion continues to worsen in most cities throughout the world, increasing
attention has been directed towards policies designed to improve the operational efficiency ofurban streets. Among the several alternatives available, restricting large trucks has been one of
the most popular in the motoring public’s view. Large trucks are often perceived as slow
moving and occupy a large amount of road space, thus hampering the smooth flow of traffic,
especially during peak-periods.
The truck ban scheme in Metro Manila has become a very feasible form of rationing scarceroad space because of the city’s insufficient road capacities. And because Metro Manila relies
on road-based public transport due to the lack of an efficient rail-based public transportsystem, the government enforces restraints on large trucks with the intention that buses andother public transport modes will not have to compete for the limited road space. However,
although large truck restrictions represent a rational solution to the problems of traffic
congestion, such measures may present negative and harmful effects if not fully understood.
The truck ban was introduced in Metro Manila by the then Metropolitan Manila Authority(MMA) on 21 August 1978 as a measure to alleviate the worsening conditions of road traffic
congestion. The ban applies to trucks with gross weights of more than 4.5 tons and prohibits
truck movements along eleven specific routes, mostly primary arterial roads. There arepresently two types of truck ban. One is the all-day truck ban, which prohibits trucks fromusing the circumferential road Epifanio delos Santos Avenue (EDSA), the most heavily usedthoroughfare, from 6AM to 9 PM during weekdays. The other is the peak hour truck ban,which prohibits trucks from using 10 major thoroughfares from 6-9 AM and 5-9 PM except
Saturdays, Sundays and holidays (Figure 1).

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2178

2. METHODOLOGY
An understanding of the transport characteristics of Metro Manila is essential in identifying
possible changes and consequences of the truck ban measure, particularly the following: a)volume increase of truck ban-exempt trucks; b) reduction of truck loading factors; c) increaseof truck traffic during the night; and d) changes in traffic distribution during the ban and noban periods. The data from a comprehensive truck survey conducted under the Metro Manila
Urban Transportation Integration Study (MMUTIS) provide a vital source of input tounderstand not only the city’s freight transport conditions but also the changes brought about
by the implementation of the truck ban.
Traffic assignment is performed to estimate the probable effects of the truck ban in terms ofvehicle-kilometers and vehicle-hours. The shortest path method is utilized to achieve thispurpose. It assumes that the goal of the driver is cost minimization that follows the minimum
cost path between the origin and the destination. This is a reasonable assumption for truck
drivers as their primary objective is to transport goods in the most efficient manner possible.
A study of 100 truck drivers in Brisbane revealed that most drivers found the shortest route tobe the most important factors affecting route choice (Bitzios and Ferreira, 1993). The shortest
path approach results in the loading of all trips onto the single minimum cost route between
nodes.
Environmental assessment is then carried out by synthesizing the results of traffic assignment
which identifies truck volume, truck type, and average speed of vehicles at road links. The
environmental emissions are computed by multiplying traffic volume by the respective
emission factors for each environmental pollutant. Percentage changes in vehicle emissions
are then calculated by comparing alternative truck ban schemes with the existing truck ban.
3. FREIGHT TRANSPORT CHARACTERISTICS IN METRO MANILAIn October 1996, MMUTIS conducted atruck survey to determine the flow of goods
in Metro Manila. Cargo vehicles entering andleaving the metropolis were surveyed atselected cordon line stations (Figure 1).
There were two types of surveys conducted,
as follows: truck volume counts and truck
driver roadside interviews, including origin
and destination (OD) data. The surveys wereconducted for 16 hours from 6 AM to 10 PM.
A 24-hour survey count is ideally preferred
but visual limitations and safety and healthconcerns of surveyors precluded a morethorough collection of data.
Truck traffic volume was counted by vehicle
type and direction. The roadside interview
survey was conducted on the same day as thetruck traffic volume survey. This is importantas the expansion factors will be computedfrom the ratio of truck traffic volumes andthe number of samples taken during thedriver interview survey. The expansion
factors will differ according to vehicle type,direction, and time period. Information on thetruck’s origin and destination, truck type,loading capacity, commodity type, andloading factor were obtained through directdriver interviews and visual inspection.
Driver interviews should be as many as
possible to obtain a more realistic data set.
The OD data will be used in the traffic
assignment process.

#!
#

#%

& '( )
$
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#

"

#

*

"
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Figure 1. Truck banned routes andlocation of survey stations

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2179

3.1 Truck traffic characteristics
Truck traffic characteristics at cordon stations are shown in Table 1. It reveals that the South
Cordon stations have the highest volume of trucks entering and leaving the city during the
16-hour count survey period with 12,500 truck movements. This is not surprising as majorityof freight traffic generators, such as factories, warehouses and industrial parks, are located in
the southern periphery of Metro Manila. Additionally, products coming from the southern
provinces are usually transported via the southern arteries, such as the South Superhighway(R3) and Manila-Cavite coastal road (R1). Compared to the South Cordon Stations, the North
Cordon stations have relatively lower truck movements, with a total of 11,200 truck
movements. Products from the northern regions are generally transported via McArthur
Highway (R9) and the North Luzon Expressway (R8). Truck flows to and from Metro Manila
via the East Cordon stations account for some 7,500 truck movements.
The overall share of truck traffic relative to the total vehicle traffic at cordon stations isaround 10.2 percent. Light trucks form the majority of truck traffic with 26.7 percent for lightcargo and 19.3 percent for cargo jeepneys and vans. This could imply the popular use of lighttrucks as a way to avoid restrictions of the truck ban.
The average loading weight for all commodities per vehicle is 2.0 tons per vehicle. The
seemingly low average weight of commodities per vehicle may be due to either 1) small
trucks with lower loading capacities are widely used for freight transport and delivery, or 2)
majority of the vehicles are running empty.
Table 1 also shows that the volume of trucks entering Metro Manila is greater than thoseleaving the city for the 16-hour survey period. This is indicative of the fact that most of thefreight facilities are located outside the city and that goods are generally delivered inside thecity during daytime when majority of the freight receivers are open. A look at the percentage
of inbound truck types substantiates this condition. Small trucks composed of cargo jeepneys,vans and light trucks, which are exempt from the ban, account for a high 50.5 percent share of
inbound truck traffic. Thus, it can be inferred that majority of delivery operations inside thecity are performed by small trucks.
Table 1. Truck traffic characteristics at cordon stations

Truck Traffic
VolumeNorth Cordon
In
Out
2-dir
6.1
5.1
11.2
10.1
10.8
10.5

East Cordon
In
Out
2-dir
4.5
3.1
7.5
9.6
9.1
9.3

South Cordon
In
Out
2-dir
7.2
5.3
12.5
10.7
9.9
10.3

Cordon Total
In
Out
2-dir
17.7
13.5
31.2
10.3
10.0
10.2

16hr (000 veh.)% to total 16hr
traffic volume
Truck Type
Jeepney, van
14.8
6.3
11.0
48.6
45.6
47.4
10.1
9.6
9.8
21.4
16.5
19.3
% to total
Light cargo
29.1
5.8
18.5
25.4
26.6
25.9
31.4
38.5
34.4
29.1
23.5
26.7
volume2-axle truck
28.4
48.7
37.6
10.8
11.0
10.9
21.6
24.2
22.7
21.2
30.4
25.2
3-axle truck
10.2
23.1
16.1
4.0
4.7
4.3
15.8
9.4
13.1
10.9
13.5
12.0
Dump truck
9.7
5.5
7.8
6.8
6.3
6.6
7.4
4.8
6.3
8.1
5.4
6.9
Trailer/Contain
3.8
5.6
4.6
1.5
0.9
1.3
8.5
6.5
7.6
5.1
4.9
5.0
Trailer Head
0.6
0.8
0.7
0.1
1.0
0.5
0.9
1.2
1.0
0.6
1.0
0.8
Tank Lorry
3.1
3.8
3.4
1.6
2.7
2.1
3.9
5.1
4.4
3.0
4.1
3.5
Mixer
0.3
0.4
0.3
1.0
1.0
1.0
0.5
0.7
0.6
0.5
0.7
0.6
Commodity
Agricultural
2.2
2.7
2.4
1.5
3.0
2.4
3.5
4.0
3.7
2.7
3.2
2.9
Type
Manufacturing
1.6
2.3
2.0
1.6
2.2
1.8
3.5
3.0
3.2
2.0
2.4
2.2
weight/veh
Forest/Mining
1.9
4.0
2.6
1.1
4.3
2.7
4.3
2.5
3.2
1.8
3.9
2.8
Construction
2.8
1.5
2.1
1.8
1.4
1.6
1.5
1.5
1.5
2.1
1.5
1.8
Ave ton/veh
2.5
1.8
2.2
1.7
1.8
1.7
2.2
2.1
2.1
2.1
1.9
2.0
Notes: North Cordon includes survey stations CH04, CH05, and EX01, East Cordon includes CH08 and CH09, and South Cordon includes
CH12 and EX02. Agricultural commodities include unprocessed cereals, agricultural foodstuff/crops, and processed crops/cereal products,
Manufacturing commodities include foodstuff and manufactured goods, Forest/Mining commodities include forestry products, mining
products, and mineral oil products, Construction/Other commodities include construction materials, producers goods and miscellaneous.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2180

3.2 Shift to small trucks
Trucking companies have apparently shifted to using small trucks not covered by the ban. A
sudden increase in utility vehicle registration can be observed just before and after the
implementation of the truck ban in 1978, as shown in Figure 2. Metro Manila, in fact, hasbeen experiencing high annual growth increases of 14 percent in the registration of small
freight vehicles in the last decade. Therefore, it may be that the effect of the truck ban has
been to worsen congestion during peak hours due to the increase of small freight vehicles.
Furthermore, a survey of the trucking fleet used by 29 medium sized freight forwarders withaverage employment of about 52 workers and 12 large-sized freight shipping companies withaverage employment of about 1,100 workers in Metro Manila reveals that around one-third(32 to 36.6 percent) of the total truck fleet used for distribution belongs to four-wheel trucks(Table 2). This truck type is truck ban-exempt having gross weight of less than 4.5 tons. Thus,
the effect of truck restrictions is to distort the size distribution of the fleet towards small
trucks, such that the commercial trucking industry are not able to take advantage of theeconomies of scale of larger vehicles or the technology of detachable trailers and load
consolidation.
Table 2. Distribution of truck fleet

Number of Registrations

200000Total Trucks
Utility Vehicles ( < 4.5 T)
Private Trucks ( > 4.5 T)
For Hire Trucks ( > 4.5 T)

150000

Truck type4-wheel truck (<4.5T)
2-axle 6-wheel truck (8-16T)
3-axle 10-wheel truck (25T)
3-axle semi-trailer truck (20T)
4-axle semi-trailer truck (20T)
5-axle semi-trailer truck (27T)
Truck-trailer (34T)
Total

100000

50000

0
1975

1976

1977

1978

1979

1980

1981

Freight
forwarders
(%)
36.6
14.0
8.0
19.1
2.0
16.8
3.5
100

Freightshippers
(%)
32.0
24.0
24.0
3.0
3.0
6.0
9.0
100

1982

Y ear

Figure 2. Truck registration after thetruck ban implementation
3.3 Truck loading factors
The loading factors of trucks at cordon stations classified by direction are given in Table 3.
Table 3. Loading factor of trucks

Full
3/4 Full
1/2 Full
1/4 Full< 1/4 Full
Empty

In
Out
Both
In
Out
Both
In
Out
Both
In
Out
Both
In
Out
Both
In
Out
Both

Jeep/
Van
32.6
41.0
36.6
6.2
11.2
8.6
12.9
12.4
12.7
7.9
12.4
10.0
2.8
2.5
2.7
37.6
20.5
29.5

Lt
Cargo
22.7
22.7
22.7
9.0
6.7
7.9
14.8
11.2
13.0
11.3
18.4
14.8
6.4
6.1
6.3
35.8
34.8
35.3

2-axle
truck
27.6
29.3
28.5
8.6
10.5
9.6
13.0
14.1
13.6
8.3
10.8
9.6
3.2
2.4
2.8
39.4
32.9
36.1

3-axle
truck
23.8
40.9
32.8
2.8
11.8
7.6
6.6
10.3
8.6
3.3
6.4
4.9
1.1
1.0
1.0
62.4
29.6
45.1

Dump
truck
44.2
29.2
37.7
9.3
1.5
6.0
9.3
6.2
7.9
3.5
1.5
2.6
0.0
0.0
0.0
33.7
61.5
45.7

Trailer
truck
16.6
55.6
34.4
1.8
4.6
3.1
1.7
8.9
5.0
0.5
1.4
0.9
0.0
1.4
0.6
79.4
28.2
56.0

Trailer
head
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
100.0
100.0
100.0

Tank

Mixer

44.0
27.3
38.9
4.0
0.0
2.8
8.0
18.2
11.1
0.0
0.0
0.0
0.0
0.0
0.0
44.0
54.5
47.2

57.1
50.0
53.3
0.0
0.0
0.0
14.3
12.5
13.3
0.0
0.0
0.0
0.0
0.0
0.0
28.6
37.5
33.3

All
veh
22.5
35.7
28.9
5.3
7.2
6.2
8.4
10.4
9.3
5.3
8.7
6.9
2.4
2.6
2.5
56.1
35.4
46.2

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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In general, majority of the vehicles entering Metro Manila are running empty with a 56.1
percent overall share, while those leaving Metro Manila is only 35.4 percent. Inbound large
trucks account for higher percentages of running empty, particularly 2-axle trucks with 39.4
percent, 3-axle trucks with 62.4 percent, and trailer trucks with 79.4 percent. This isreasonable because large trucks are mainly used for the collection of freight from the port andtransport them to places outside Metro Manila. It is, thus, expected that outbound traffic willregister high percentages from these three vehicle types running fully-loaded. This can beverified from Table 3, which shows that fully-loaded outbound 2-axle trucks account for 29.3
percent, 3-axle trucks for 40.9 percent, and trailer trucks for 55.6 percent.
There is, however, a surprising result in small inbound trucks such as cargo jeepneys, vans,and light cargo trucks because they likewise account for high percentages of running empty.
This implies that small trucks may have also been utilized to collect freight from the port andthen deliver them outside the city. The high percentages of fully-loaded outbound jeep andvans, and light cargo trucks support this argument. This, therefore, corroborates what waspresented earlier, which claims that ban-exempt smaller trucks are now used in place ofbanned larger trucks in transporting freight from the ports.
3.4 Increase of truck traffic at night
Apart from the 16-hour truck volume count and roadside truck driver interview surveys, acontinuous 24-hour classified volume count survey for all vehicles was also conducted in four
cordon stations for one-week. These are: CH04 in the north, CH09 in the east, and CH012 andEX02 in the south. By calculating the ratio of the 24-hour count to the 16-hour count, theresulting ratio can be considered as some kind of an indicator to represent the rate of increaseof truck traffic at night. Table 4 shows the increase of vehicle traffic for each cordon station.
Table 4. Increase of vehicle traffic at night
Station
CH04

Period24hr
16hr
24/16hr

CH09

24hr
16hr
24/16hr

CH012

24hr
16hr
24/16hr

EX02

24hr
16hr
24/16hr

Dir
Inbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
OutboundInbound
Outbound

Bus
355
400
287
284
1.2
1.4
460
440
367
384
1.3
1.1
1593
1373
1399
1246
1.1
1.1
2954
3501
2477
3125
1.2
1.1

Jeep
3211
3093
2624
2591
1.2
1.2
3088
3068
2334
2545
1.3
1.2
3748
3768
3006
3253
1.2
1.2
3393
3699
2903
3252
1.2
1.1

Car
5897
4723
4816
3893
1.2
1.2
13441
12767
10378
10338
1.3
1.2
20242
16700
16945
13633
1.2
1.2
28267
28197
25003
24130
1.1
1.2

Truck
1156
979
830
723
1.4
1.4
2246
2116
1576
1460
1.4
1.4
2593
2343
1658
1272
1.6
1.8
6159
6284
4652
4116
1.3
1.5

Others
1587
1597
1407
1386
1.1
1.2
1639
1641
1406
1424
1.2
1.2
1085
856
975
775
1.1
1.1
27
20
17
15
1.6
1.4

Total
12206
10793
9965
8876
1.2
1.2
20874
20032
16061
16151
1.3
1.2
29260
25040
23983
20180
1.2
1.2
40799
41703
35053
34637
1.2
1.2

It is evident from the Table that the 24/16-hour count ratios for trucks are comparably higher
than the other transport modes. On the average, trucks have a ratio of 1.4, as compared withthe other modes with a ratio of 1.2. This verifies the proliferation of trucks at night. The
highest ratios are found in the southern cordon station, CH012, wherein the inbound andoutbound ratios are 1.6 and 1.8, respectively.
In addition, the volume of inbound trucks is generally greater than that of outbound trucks, as
can be seen from the 16-hour truck counts. This situation, however, reverses at night when the
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2182

volume of outbound trucks becomes greater than that of inbound trucks for the 8-hournighttime period (10PM-6AM), as manifested by the two south cordon stations CH012 andEX02. This finding is consistent with the previous sections that it may be possible thatdelivery trucks enter Metro Manila at daytime to perform deliveries and return back to theirdepot located outside the city at nighttime. Trucks may be forced to wait at the ports due toinefficient loading operations. It must be noted that loading operations of trucks are usually
done during the day and that any delay will result in late truck departures. Truckers may alsochoose to travel at night to avoid the truck ban in Metro Manila.
3.5 Temporal distribution of traffic
To clarify the effect of the truck ban on traffic volumes, it is necessary to examine thetemporal distribution of traffic at the cordon stations. Figure 3 presents the hourly distributionof traffic for all cordon stations. The upper chart shows vehicle volumes in each time period,
while the lower chart shows the percentages of these volumes to the 16-hour traffic volume.
The figures evidently confirm that the effect of the truck ban on truck traffic volume issignificant. There is minimum truck movements observed during the AM and PM truck ban
periods as shown in the lower chart.

+

+

,

,

+

,

+

,

Figure 3. Temporal distribution of traffic at cordon stations
3.6 Truck trips
Figure 4 represents the total origin-destination truck flows by time period, namely from 6 to 9
AM for the AM peak, 9 AM to 5 PM for the off-peak, and 5 to 9 PM for the PM peak. The
figures were prepared using the Desired Line Viewer function of JICA STRADA (1997).

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2183

- ../

(0

(0

(0

Figure 4. Desire lines of truck trips by truck type and time period

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2184

Most truck trips occur during the off-peak period when trucks can use the major roads except
EDSA to conduct operations. Truck trips during peak-hours are significantly lower comparedto the off-peak hours. The heaviest truck flows are those that emanate or end at Manila andCavite. Truck flows are also significant throughout the Metro Manila area, especially in theradial direction and the north-south axis. As earlier said, small trucks account for the major
truck flows.
4. TRAFFIC ASSIGNMENT
The required inputs for traffic assignment are the origins, destinations, quantity of movements,
details of the road network, and rules for the selection of paths. It is usual to use a minimum
path rule for selecting the route because of its wide applications in real-life large-scale
network models. The link record contains length, or cost, or some other measure of movement
impedance. A shortest path algorithm is used to build route trees from each origin to each
destination. The tree lists the nodes in sequence that makes up the path.
Once the shortest paths are found, the trips between zones are loaded onto the links making
up the shortest paths. This technique is referred to as all or nothing, since all trips between agiven origin and destination is loaded on the links comprising the shortest path and nothing isloaded on the other links. The result is an estimate of volume on each link in the network.
This method can cause some links to be assigned more travel volume than the link capacity.
But this approach is useful in indicating where the major bottlenecks are likely to occur.
A program developed under the Visual Basic environment is utilized to perform shortest pathassignment for the truck movements in Metro Manila. The program uses Dijkstra’s algorithm
to calculate for the shortest path. Studies suggest that when the goal is to obtain a one-to-one
shortest path or one-to-some shortest paths, the Dijkstra algorithm offers some advantages
because it can be terminated as soon as the shortest path distance to the destination node is
obtained (Pallottino and Scutella, 1997). The algorithm offers fast implementation since one
pass of the algorithm can produce the minimum path from one single node to another. Thisshould be contrasted with other algorithms wherein for one pass, the minimum paths from asingle node to all nodes of the network are produced, which obviously will take a longer timeto process.
4.1 Data requirements
The major inputs are: 1) road network including link characteristics, such as coordinates ofstart and end nodes, distance, travel speed, and coordinates of zone centroid, and 2) truck OD
classified by truck type and time period.
The node coordinates, including the characteristics of each link and node, were manuallyobtained from MapInfo maps of Metro Manila. In the preparation of the road network, small
local streets were not included as freight trucks do not use them. Streets that are insideexclusive villages and subdivisions were also not considered. A list of street links that are
affected by the truck ban in different time periods was also prepared. These links as well aschanges in the road network due to alternative truck ban schemes were integrated in the final
road networks.
OD tables were prepared from the actual truck driver roadside interview survey conducted
after applying the necessary expansion factors. Truck OD tables were classified into small orlarge vehicles. Small vehicles include jeepneys, vans, and light cargo trucks, while large
vehicles include 2-axle trucks, 3-axle trucks, and container trucks, among others. The OD
Tables can be easily extracted using any database or spreadsheet application software.
There were only three time periods included for traffic analysis, which are the AM peak (6-9AM), the daytime off-peak (9 AM-5 PM), and the PM peak (5-9 PM). This is because thetruck driver roadside interview survey was only conducted for a period of 16-hours, and there
was no information available for truck movements during nighttime off-peak period (9 PM- 6
AM). This may tend to underestimate the results but should a more comprehensive analysis isperformed in the future, nighttime off-peak truck movements should be collected as well.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

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Ideally, passenger traffic flows should be included in the traffic assignment analysis. The lackof passenger traffic OD data for the three time periods and the unknown speed-flow
relationship for each link, however, preclude a thorough assessment. Thus, only freightvehicles were considered for the traffic assignment procedure. The lack of speed-flow formulafor each link compels the use of the simple shortest path assignment method. This means that
only large trucks will be affected by changes in the truck ban implementation. This is because
private vehicles and small trucks, which are ban-exempt, are motivated to use the shortest
routes. Large trucks then are forced to use the available routes. However, if these data can bemade available, it is recommended that a more advanced traffic assignment model, which also
considers demand-capacity relationship, be used for a better traffic analysis.
4.2 Alternative truck ban schemes
Five truck ban schemes are examined to determine the probable effects of the truck ban interms of vehicle-kilometers and vehicle hours, as follows:
1.

Existing truck ban scheme (base condition): This alternative retains the existing truck
ban in its present form. This also serves as the base case scenario from which
comparisons are made. In this alternative, large trucks are prohibited to use majorroutes including EDSA during the morning (0600-0900 hrs) and the afternoon(1700-2100 hrs) peak periods.

2.

Without truck ban scheme: This option is a total elimination of the ban, where large
trucks freely use all routes anytime. This will be a reversion to the pre-1978 conditions.
This would only be appropriate when there is compelling evidence that the truck ban
produces minimal benefits or when there are too much economic costs involved.

3.

Peak-hour only truck ban at all major roads (EDSA and major routes): This case is acompromise solution that relaxes the ban at the major roads during off-peak periods.
This is based on the premise that the operating cost of trucks due to the ban is higher
on the primary route than the other major routes. For this option, truck restrictions at
EDSA during off-peak hours are lifted, thereby permitting large trucks to use thecircumferential road from 0900-1700 hrs.

4.

All day EDSA truck ban only scheme: This scheme is another compromise solution that
relaxes the ban on some other major routes during certain time periods. This is basedon the condition that operating costs of trucks due to the ban falls more heavily onsome routes than on others. For this option, truck restrictions on the major routes
except for EDSA are lifted so that large trucks are permitted to use these routes at all
times. In EDSA, however, large trucks are not allowed during daytime from 0600-2100
hrs.

5.

All day truck ban at EDSA and other major roads scheme: This scheme extends thetruck ban period to routes presently not covered. If the truck ban proves to be effective
along banned routes, then presumably the same benefit maybe achieved in adding otherrestricted routes. Large trucks will not be permitted at all major routes including EDSA
from 0600 to 2100 hrs.

5. TRAFFIC ASSIGNMENT RESULTS
A summary of the results of the traffic assignment for the five cases is given in Table 5. To
illustrate, routes that would be most frequently used by both large and small trucks for the
existing and no truck ban schemes, are shown in Figures 5 and 6, respectively.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2186

Table 5. Traffic impacts of alternative traffic control schemes
AM Peak(6-9 HR)

Off-Peak(9-17 HR)PM Peak(17-21 HR)

Total(6-21 HR)

27166
580
18.7
0.39

520250
10478
18.2
0.37

74949
1519
21.2
0.43

622365
12577
18.6
0.38

21861
480
15.1
0.33

512507
10215
18.0
0.36

66338
1324
18.8
0.38

600706
12019
17.9
0.36

27166
580
18.7
0.40

512507
10215
18.0
0.36

74949
1519
21.2
0.43

614622
12314
18.3
0.37

22193
484
15.3
0.33

520250
10478
18.2
0.37

67105
1348
19.0
0.38

609548
12310
18.2
0.37

27166
580
18.7
0.40

591311
11918
20.7
0.42

74949
1519
21.2
0.43

693426
14017
20.7
0.42

Existing truck ban scheme
VKM
VHR
Ave. Km.
Ave. Hr
No truck ban scheme
VKM
VHR
Ave. Km.
Ave. Hr
Peak-hour only truck ban scheme
VKM
VHR
Ave. Km.
Ave. Hr
All-day truck ban at EDSA only
VKM
VHR
Ave. Km.
Ave. Hr
All-day truck ban at EDSA and major routes
VKM
VHR
Ave. Km.
Ave. Hr

As expected, the “no truck ban” scheme has the least total vehicle kilometers andvehicle-hours among the five cases with 600,706 veh-kms and 12,019 veh-hrs for the 15-hour
analysis period. Since trucks are allowed in all the major arterial roads, meaning both
circumferential and radial roads, they do not have to use circuitous routes to get to their finaldestinations. Figure 5 shows that during off-peak period, large trucks mostly use thecircumferential roads C2 and C4 (EDSA) and the southbound R1 and northbound R8 routes.
During AM and PM peak-periods, however, large trucks are forced to take the circumferential
road C2, the radial roads R1, R8, and the eastbound routes of R5 and R6. Small trucks mainly
utilize C2, EDSA, southbound R1 and northbound R8 routes.
The second most optimal scheme is the “all-day truck ban at EDSA only” with 609,548
vehicle-kilometers and 12,310 vehicle-hours. Large trucks optimize transport operation bytaking advantage of the radial roads where they are allowed to pass all day. Radial roads are
popularly used by large trucks in the southbound routes of R1 and R3, eastbound routes of R5
and R6, and northbound route of R8. Small trucks still preferr to use EDSA during off-peakhours.
The “peak-hour only truck ban” scheme ranks third with 614,622 vehicle-kilometers and12,314 vehicle-hours. As the truck ban is not in effect in EDSA during off-peak hours, large
trucks are able to utilize the circumferential road. EDSA thus functions as the main distributorof traffic to the radial roads located outside the area bounded by EDSA. During off-peakhours, large trucks fully utilize the southbound R1 and northbound R8 routes. During peak
hours, however, large trucks are forced to take the circuitous alternate truck routes set bygovernment as they as they are not allowed to pass the major arterial roads.
The “existing truck ban” scheme only ranks fourth in terms of minimizing vehicle kilometersand vehicle-hours with 622,365 veh-kms and 12,577 veh-hrs. The present scheme shows thatlarge trucks widely use the available alternate routes to transport freight during the morningand afternoon peak-hour ban. Except in EDSA, large trucks take advantage in passing throughall radial and circumferential roads, as allowed during off-peak periods. Roads mostcommonly utilized by large trucks are the southbound R1 and R3, eastbound R6, thenorthbound R8, and the circumferential roads C2 and C5. Small trucks mainly use C2 and
EDSA, and the southbound R1 and northbound R8 routes.
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2187

AM Peak+

Off Peak+*3

Small trucks

*3

+

1 2'

1 2'

Large trucks

PM PeakLarge trucks

*3

1 2'

Small trucks

Large trucks

Small trucks

Figure 5. Assignment of trucks for the existing truck ban scheme
AM Peak+

Off Peak+*3

Small trucks

*3

+

1 2'

1 2'

Large trucks

PM PeakLarge trucks

Small trucks

*3

1 2'

Large trucks

Small trucks

Figure 6. Assignment of trucks for the “no truck ban” scheme

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2188

Finally, as anticipated, the scheme with the highest vehicle-kilometers and vehicle-hours is
the “all-day truck ban at EDSA and major routes” with 693,426 and 14,017, respectively.
Since the truck ban is extended to cover all major roads, except those identified as alternate
truck routes, large trucks are compelled to utilize the alternate truck routes at all times. This
results in longer travel distances and travel times, and ultimately increases vehicle-kilometersand vehicle-hours.
6. ENVIRONMENTAL ASSESSMENT
Environmental assessment is performed by aggregating the results of the traffic assignment
which identifies volume, type, and average speed of vehicles in each link. Environmental
emissions are calculated by multiplying traffic volume by the respective emission factors foreach environmental pollutant (i.e. CO, NOx, SOx, SPM). The changes in vehicle emissions
are determined by comparing each truck ban scheme with the existing truck ban scheme.
Pollutant emissions are assumed to be dependent on travel distance, travel speed, andemission factors for each vehicle type.
Emission factors depend on a variety of factors including speed, age of vehicle, altitude,
temperature, operating conditions, and others (Campbell, 1995). It is very difficult, however,to find a single data source containing all the factors for all conditions. The emission factors
calculated by MMUTIS are thus utilized for different vehicle classes. Given a table ofemission factors in Table 6, pollutant emissions can be estimated as:
Pollutant (i) = travel distance × emission factor at average travel speed(veh-km)(g/veh-km)
Table 6. Emission factors (unit: g/km)
PollutantType
CO

Gasoline
DieselNOxGasoline
DieselSox

Gasoline
DieselSPM

Gasoline
DieselVehicleType
Car
Small truck
Car
Small truck
Large truck
Car
Small truck
Car
Small truck
Large truck
Car
Small truck
Car
Small truck
Large truck
Car
Small truck
Car
Small truck
Large truck< 10 km/h
27.57
47.58
7.85
8.02
8.12
2.75
4.7
5.65
8.95
11.24
0.013
0.015
0.14
0.18
0.2
0.07
0.07
1.2
1.8
2.3

Average Speed
10 - 20 km/h
23.5
52.2
6.54
6.8
7.11
2.76
3.59
4.28
7.66
10.59
0.011
0.011
0.08
0.121
0.15
0.05
0.06
0.07
0.9
1.5

> 20 km/h
18.7
41.14
5.94
6.2
6.5
2.78
3.53
3.89
7.01
9.22
0.011
0.01
0.07
0.11
0.1
0.05
0.05
0.07
0.81
0.80

The table of emission factors indicates that lower vehicle speeds yields higher amounts ofemissions. Gasoline and diesel engines vary in emission profiles. Smaller vehicles typicallyhave lower emission rates than the large ones. The largest amounts of emissions in terms ofunit (i.e. grams per kilometer) are observed for carbon monoxide (CO), ranging from aminimum of 6.2 to a maximum of 52.2 g/km for both gasoline-powered and diesel-powered
vehicles. Oxides of nitrogen (NOx) emissions vary from 2.75 to as high as 11.24 g/km.
Gasoline-powered vehicles emit more CO than their diesel-powered counterparts.
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2189

Diesel-powered vehicles, though, emit more NOx than their gasoline-powered counterparts. It
can then be inferred that more gasoline-powered vehicles, usually private cars, would result inhigher CO emissions, or more diesel-powered vehicles, generally large trucks, would result inhigher NOx emissions.
In the calculation of pollutant emissions, it is assumed that gasoline-powered small trucks
comprise 50 percent and that diesel-powered small trucks comprise the other 50 percent of
their total volume. Large trucks, on the other hand, are assumed to be 100 percent
diesel-powered.
Figures 7a and 7b show the results of the computations for the amount of pollutant emissions
for each scheme according to time period. Overall, the figures show that there is very littlechange in the amount of pollutant emissions for the four truck ban schemes, namely, existing
truck ban, no truck ban, peak-hour only ban, and all-day ban at EDSA only. Considerable
increases in pollutant emissions, though, are observed for the all-day truck ban scheme at all
major routes including EDSA. The off-peak time period comprises the majority of amount ofemissions as trucks have the freedom to use all the major roads except EDSA.

10000.0
8000.0PM Peak

6000.0Of f -Peak

4000.0A M PeakCO

All-day
ban all

All-day
EDSA

Peakban

No banExisting

All-day
ban all

No banExisting

0.0

All-day
EDSA

2000.0PeakbanAmount of emission (kg)

12000.0

NOx

500.0
400.0
PM Peak

300.0

Off-Peak
AM Peak

200.0
100.0

SOx

PeakbanAll-day
EDSA
All-day
ban all

No banExisting

PeakbanAll-day
EDSA
All-day
ban all

No ban0.0
Existing

Amount of emission (kg)

(a)

SPM

(b)
Figure 7. Amount of pollutant emissions in each time period

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2190

12000
Large trucks

10000

Small trucks

8000
6000
4000
2000

CO

NOx

SOx

All-day ban all

All-day ban EDSA

Peak hour ban only

No truck ban

Existing

All-day ban all

All-day ban EDSA

Peak hour ban only

No truck ban

Existing

All-day ban all

All-day ban EDSA

Peak hour ban only

No truck ban

Existing

All-day ban all

All-day ban EDSA

Peak hour ban only

No truck ban

0
Existing

Amount of emission (kg)Figure 8 shows the amount of pollutant emissions for each scheme according to type of truck.
Carbon monoxide has the highest amount of emission ranging from 9 tons per day, followedby NOx with about 4 tons per day. Very minimal amounts of emission are observed for SOx
and SPM. What proves to be the most important among all results is that small trucks cause
the majority of the amount of carbon monoxide emissions while large trucks cause themajority of nitrogen oxides emissions.

SPM

Figure 8. Amount of pollutant emission for each scheme
The results suggest that any switch to gasoline powered small trucks due to the truck ban willcause the following: a small effect in SOx and SPM, a moderate decrease in NOx emissions
but a large increase in CO emissions. A decrease in NOx emissions from large trucks,
especially heavy diesel, is beneficial because they account for a large percentage of the total
NOx vehicle emissions. Increases in SPM and CO emissions are detrimental, although heavy
truck emissions are a very small component of SPM and CO total vehicle emissions and total
overall emissions. Increases in SPM emissions may be most hazardous as they react in theatmosphere to form smog, which in turn worsens air quality. The imposition of the truck ban,therefore, from the viewpoint of improving air quality is somewhat counterproductive.
7. COMPARISON OF ALTERNATIVE TRUCK BAN SCHEMES
The changes in performance measures are calculated by comparing alternative truck ban
schemes with the existing truck ban scheme. For example, the percentage change in travel
distance is calculated D = Da – Db / Db, while the percentage change in pollutant emission iis computed Ei = Ea – Eb / Eb, where the subscripts a and b denote the two cases being
compared (i.e. a: “after” or revised truck ban scheme, b: “before” or existing truck ban
scheme).
A summary of the traffic and environmental impacts of the different traffic control schemes ispresented in Table 7.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2191

Table 7. Comparison of alternative traffic control schemes to the existing truck ban
Traffic control
schemeDistance
(km)

%

Time
(hrs)

%

CO
(kg)

%

NOx
(kg)

%

SOx
(kg)

%

SPM
(kg)

%

Existing truck ban

622365

-

12577

-

9186

-

4489

-

50

-

387

-

600706

-3.5

12019

-4.4

9046

-1.5

4289

-4.5

48

-4.4

370

-4.5

614622

-1.2

12314

-2.1

9136

-0.5

4417

-1.6

49

-1.6

381

-1.6

609548

-2.1

12310

-2.1

9103

-0.9

4371

-2.6

49

-2.6

377

-2.6

693426

11.4

14017

11.4

9648

5.0

5144

14.6

57

14.1

444

14.6

No truck ban
Peak hour truck
ban only at EDSA
and major routes
All-day truck ban
at EDSA only
All-day truck ban
at EDSA andmajor routes

When compared with the existing truck ban scheme, the alternative with the highest reductionis the “no truck ban scheme”. The total travel distance is reduced by 3.5 percent, total travel
time by 4.4 percent, CO by 1.5 percent, and NOx, SOx, and SPM by 4 to 5 percent. Shorterand direct routes as provided by the major radial and circumferential roads resulted in lesstotal travel distances and travel times, which subsequently reduced pollutant emissions.
The “all day truck ban at EDSA only scheme” follows next with a reduction of 2.1 percent for
both total travel distance and travel time, 0.9 percent for CO, and around 2.6 percent for NOx,
SOx, and SPM. When this scheme is compared with the “no truck ban scheme”, this may be
more feasible since a fairly significant reduction in the amount of emissions can be obtainedby merely restricting along EDSA during the day and lifting the ban at the major routes duringpeak-hours.
The “peak hour truck ban only at EDSA and major routes scheme” resulted into reductions of
1.2 percent for total distance, and 2.1 percent for total travel time. Minimal reductions wereobserved for CO with 0.5 percent, and about 1.6 percent for the remaining pollutant emissions.
These results only indicate that this option offers very little reduction when compared with theexisting truck ban.
Finally, the scheme with the worst performance is the “all-day truck ban at EDSA and majorroutes scheme”. Total travel distance increased by 11.4 percent, total travel time also by 11.4
percent, CO by 5 percent, and NOx, SOx, and SPM by about 14 percent. These large
increases in total travel distances, travel time, and pollutant emissions observed were due tothe diversion of trucks in using the circuitous alternate routes.
8. CONCLUSION
The paper explained the impacts of the truck ban on the transportation system of Metro
Manila. Aggregated results of the MMUTIS truck surveys indicated the following:• Traffic volume of ban-exempt smaller cargo vehicles increased, which is attributable tothe utilization of smaller trucks in place of larger trucks that are banned from using majorroads in Metro Manila.
• Small inbound cargo trucks account for high percentages of empty vehicles, whichindicates that perhaps they are likewise utilized to collect freight from the port anddeliver them outside the city, an information complementing the preceding finding.
• Traffic volume of trucks at night is comparably higher than the other transport modes,
which implies the proliferation of nighttime deliveries.
• Temporal traffic distribution trends confirmed that the effect of the truck ban on truck
traffic volume is significant during the truck ban and the no truck ban periods. Minimum
truck movements are observed during the peak-hour truck ban periods while peakvolumes of trucks are observed during the middle hours of the off-peak period.
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

2192

Switching to small trucks is an accidental by-product of the truck ban implementation in
Metro Manila. The truck ban will be more successful if this switching is minimized oreliminated but companies may still need to use smaller trucks even if they could cause
increases in the level of congestion and the amount of pollution. These companies do so inorder to avoid the restrictions of the truck ban and also to fulfill quick response andjust-in-time freight transportation.
The truck ban is designed to shift traffic out of the congested peak periods to the lesscongested daytime or nighttime off-peak periods. Although nighttime operations could befavorable for air quality, the operations of large trucks can be quite troublesome. Noise
generated from shipping and receiving may be unpleasant near residential areas. Economic
impacts may be significant to many businesses if forced to do nighttime operations.
Although traffic assignment results may indicate that the “no truck ban” scheme is the mostfavorable way of improving the traffic environment in terms of reducing total travel distances,travel times, and pollutant emissions, this scheme may encounter political resistanceespecially from private motorists and residents who will be definitely affected by its lifting.
Add to this, the need to reduce emissions of NOx in local and sensitive areas, such as
residential subdivisions, leisure parks, and shopping districts near the banned routes may
warrant the implementation of the truck ban. Any call for improvement in the quality of air or
traffic in these areas is enough reason to leave the truck ban in place. On the other hand, the“all day truck ban at EDSA only” scheme offers comparatively encouraging reductions
without completely eliminating the truck ban. This scheme may thus be the most favorable
option if only the government and the different freight transport actors will compromise into
an agreement benefiting all sectors.
Additional analysis is needed to estimate the benefits of reduced congestion brought about by
the truck ban scheme, such as its impact on passenger trips and the environment. For example,
even if truck emissions and performance worsen due to truck restrictions, a large enoughimprovement in emissions from other vehicles, mainly private passenger cars, could result ina net improvement in air quality. However, the vague and poorly understood role of latentdemand in canceling out any traffic congestion reductions brought about by the truck ban
must be considered.
General findings indicate that additional restrictions significantly increase total
vehicle-kilometers, total vehicle-hours, and total pollutant emissions. These translate into
increased transportation costs as total distance and total travel time are directly proportional tocosts. These increases may add to the cost of goods to producers and eventually to consumers.
The additional fuel consumption caused by the inefficiencies could also be a significant factor.
These negative effects must be balanced against many economic, environmental, and social
benefits of truck restrictions. Additional analyses are needed to estimate these benefits.
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Bitzios, D. and Ferreira, L. (1993) Factors affecting route choice of commercial vehicle
drivers. Proceedings of Australian Transport Research Forum (ATRF) 18, pp. 27-40.
Campbell, J.F. (1995) Peak period large truck restrictions and a shift to off-peak operations:
Impact on truck emissions and performance. Journal of Business Logistics, Vol. 16, No.2, pp.
227-247.
Dijkstra, E.W. (1959) A note on two problems in connection with graphs. Numerische
Mathematik 1, pp. 267-271.
JICA STRADA (1999) JICA System for Traffic Demand Analysis. Japan International
Cooperation Agency, Tokyo.
MMUTIS (1999) Metro Manila Urban Transportation Integration Study, Department ofTransportation and Communications, Metro Manila.
Pallottino, S. and Scutella, M.G. (1997) Shortest path algorithms in transportation models:classical and innovative aspects. University di Pisa Technical Report TR-97-06, Pisa, Italy.
Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003