5 transportation volumes – analysis and projectionsmot.gov.lr/doc/cap_5.pdf · 2014. 10. 10. ·...

5 Transportation volumes – analysis and projections

71

5 Transportation volumes – analysis and projections

5.1 Road transport

5.1.1 General

Present distances and travel times by road between the various county capitals are shown on Table 5-1 and Table 5-2 below. Figure 5-1 gives an overview of the road traffic relating to the traffic census 2009/2010. The information derives from the on-site GPS and “Comfortable Driving Speed” surveys. The distances should replace any previously gazetted24.

Average comfortable-driving times derived from these first two tables are shown on Table 5-3 below. The deficiencies in the network are particularly clear from the lengthy driving times and low com-fortable speeds. Particularly bad are speeds and travel times between, and to, centres in the east. The easternmost parts of the country are presently suffering most from a lack of road infrastructure.

The chapter goes on to show:

the results of the dry and wet season classified traffic counts: with recommendations for future supplementary counts – detailed information is provided in appendix A (see attached CD);

the results of the dry and wet season roadside interviews – more detailed information is provided in appendix C (see attached CD);

the forecasts for growth in base road traffic – typical motorisation rates for other countries in Africa are shown in appendix B (see attached CD); and

some important conceptual conclusions concerning the effects on traffic with certain specific improvements to the network (see details in appendix G on CD), are discussed viz:

– were the south coast road from Monrovia to Harper to be rehabilitated along its entire length; and

– were the road from Buchanan to Tappita to be rehabilitated/completed25.

This data and the above network effects have been central to the economic modelling and the prior-ity ordering of rehabilitation projects that is described in chapter 6.

24 LISGIS maps do not always show the correct locations for roads – the problem is most acute in the east of Liberia.25 Maps show a secondary road running in almost straight line between these two points – the project on-site surveys were unable to locate

a road on the southernmost section of this link, over this section traffic must divert to the railway service road.

72Ta

ble

5-1:

Dis

tanc

es in

kilo

met

er -

coun

ty c

apita

ls

Tubm

an-

burg

Rob-

erts

port

Mon

rovi

aKa

kata

Gba

rnga

Voin

jam

aSa

n-ni

quel

lieZw

edru

Fish

tow

nH

arpe

rBu

chan

-an

Rive

r C

ess

Gre

en-

ville

Barc

lay-

ville

Tubm

anbu

rg0,

010

5,2

59,2

121,

224

5,7

445,

834

5,8

520,

069

0,6

776,

818

3,5

332,

138

8,4

863,

5Ro

bert

spor

t0,

012

1,2

183,

230

7,7

507,

840

7,8

582,

075

2,6

838,

824

5,5

270,

145

0,4

925,

5M

onro

via

0,0

62,0

186,

538

6,6

286,

646

0,8

631,

471

7,6

124,

321

0,9

329,

280

4,3

Kaka

ta0,

012

4,5

324,

622

4,6

398,

856

9,4

655,

613

4,2

272,

939

1,2

866,

3G

barn

ga0,

020

0,1

100,

127

4,3

444,

953

1,1

258,

739

7,4

515,

799

0,8

Voin

jam

a0,

030

0,2

474,

464

5,0

731,

245

8,8

597,

571

5,8

1190

,9Sa

nniq

uelli

e0,

024

5,8

416,

450

2,6

358,

849

7,5

615,

810

90,9

Zwed

ru0,

017

0,6

256,

853

3,0

671,

779

0,0

343,

5Fi

shto

wn

0,0

86,2

703,

684

2,3

960,

617

2,9

Har

per

0,0

789,

892

8,5

1046

,810

0,3

Buch

anan

0,0

86,6

204,

992

8,6

Rive

r Ces

s0,

019

1,1

1015

,2G

reen

ville

0,0

1133

,5Ba

rcla

yvill

e0,

0So

urce

: Con

sulta

nts’

GPS

Fie

ld S

urve

ys

Tabl

e 5-

2: T

rave

lling

tim

e in

min

utes

- co

unty

cap

itals

Tubm

an-

burg

Rob-

erts

port

Mon

rovi

aKa

kata

Gba

rnga

Voin

jam

aSa

n-ni

quel

lieZw

edru

Fish

tow

nH

arpe

rBu

chan

-an

Rive

r C

ess

Gre

en-

ville

Barc

lay-

ville

Tubm

anbu

rg0

8149

107

221

493

324

532

739

855

157

265

417

1034

Robe

rtsp

ort

096

154

268

540

371

579

786

902

204

312

464

1081

Mon

rovi

a0

5817

244

427

548

269

080

610

821

636

898

5Ka

kata

011

438

621

742

563

274

813

224

039

292

7G

barn

ga0

272

103

311

518

634

246

388

506

813

Voin

jam

a0

375

582

790

906

518

660

778

1085

Sann

ique

llie

033

454

165

734

949

160

983

6Zw

edru

020

732

359

169

881

750

2Fi

shto

wn

011

679

890

610

2429

5H

arpe

r0

914

1022

1140

181

Buch

anan

010

826

010

93Ri

ver C

ess

025

512

01G

reen

ville

013

53Ba

rcla

yvill

e0

Sour

ce: C

onsu

ltant

s’ G

PS F

ield

Sur

veys

and

Com

fort

able

Driv

ing

Spee

d su

rvey

s

Transportation volumes – analysis and projections

73

Tabl

e 5-

3: A

vera

ge c

omfo

rtab

le d

rivin

g sp

eeds

(km

/hr

) - b

etw

een

coun

ty c

apita

ls

Tubm

an-

burg

Rob-

erts

port

Mon

rovi

aKa

kata

Gba

rnga

Voin

jam

aSa

n-ni

quel

lieZw

edru

Fish

tow

nH

arpe

rBu

chan

-an

Rive

r C

ess

Gre

en-

ville

Barc

lay-

ville

Tubm

anbu

rg77

,83

72,3

068

,06

66,7

354

,28

64,0

758

,70

56,0

754

,51

69,9

675

,1755

,87

50,1

0Ro

bert

spor

t75

,66

71,4

668

,91

56,4

565

,98

60,3

657

,45

55,8

072

,08

51,9

358

,23

51,3

7M

onro

via

64,4

665

,1352

,28

62,6

057

,31

54,9

153

,43

68,9

058

,60

53,6

849

,00

Kaka

ta65

,47

50,4

662

,10

56,3

454

,04

52,5

860

,95

68,2

859

,90

56,0

6G

barn

ga44

,1658

,37

52,9

951

,53

50,2

663

,04

61,4

961

,1673

,11

Voin

jam

a48

,06

48,8

748

,99

48,4

353

,1454

,35

55,2

265

,86

Sann

ique

llie

44,17

46,16

45,8

861

,67

60,8

460

,69

78,2

6Zw

edru

49,3

447

,64

54,14

57,7

158

,05

41,0

2Fi

shto

wn

44,5

952

,89

55,7

956

,29

35,16

Har

per

51,8

454

,52

55,1

033

,28

Buch

anan

48,2

547

,34

50,9

7Ri

ver C

ess

44,9

550

,72

Gre

envi

lle50

,27

Barc

layv

ille

Sour

ce: T

able

s 1-

1 an

d 1-

2.


74Figur e 5-1: Traffi c census 2009/2010


75

5.1.2 Analysis of dry and wet season classified traffic counts

A summary of the volume of traffic on the major links in the network is provided on appendix D (see attached CD). A composite picture of traffic (using dry season data from all the counting sites com-bined) and showing the typical hourly distribution of movements and the composition of the counted traffic between 6 am and 6 pm is provided on Figure 5-226.

It is noted that in line with the priorities of the Master Plan both the dry and wet season counts mostly took place on primary roads and that, except for some moving- observer information, little data has yet been collected for secondary roads. However, after the presentation of the preliminary draft of the Master Plan on 24 November 2010, a status quo analysis of available agricultural data for production and consumption (surplus/deficit) of certain food and cash crops (in tons/year) was carried out to estimate traffic volumes in districts mainly or only connected to the primary road net-work by secondary roads. The results (see section 5.1.4) will be useful to prioritise secondary roads for further surveys in the future.

It is also noted that the counts were neither as comprehensive as originally intended27, nor as com-prehensive as the 1981 counts, and that at some stations, particularly during the first-round counts, the descriptions regarding actual survey locations provided by the field surveyors on their data col-lection forms were unclear. In future it is suggested that forms are pre-printed with the “link name” and the “traffic direction”28 in order that surveyors know exactly which links are to be surveyed.

A summary of the counts made is compared with the originally-intended programme on appendix E on CD. For a fully prioritised rehabilitation programme, it will be necessary to have counts on all links. The programme as now presented has to be refined at later stages. On the more important sec-ondary roads and on the primary roads where there were no counts, some moving-observer counts were undertaken simultaneously with the “comfortable driving speed” surveys. It was, thus, pos-sible to incorporate preliminary findings for these roads into the RED modelling. Modelling conclu-sions are reported in chapter 6.

26 The dry and wet season on-site patterns of traffic at each of the counting stations as well as the night counts (reduced scope) are shown in the attached appendix A on CD.

27 The data in appendix E (see attached CD) suggests that only 60% of the intended/potential/planned counting days occured.28 For example six forms would be provided for Gbangra Counting Station – each prelabelled as appropriate: (i) “Voinjama towards Gbangra”;

(ii) “Gbangra towards Voinjama”; (iii) “Monrovia towards Gbangra”; (iv) “Gbangra towards Monrovia”; (v) “Ganta towards Gbangra”; and (vi) “Gbangra towards Ganta”. Descriptions such as “Ganta to Monrovia” are not useful as it is not clear which side of Gbangra the count took place.


76Figure 5-2: Composite picture of traffic (data from all counting sites com-

bined): Hourly distribution of movements between 6 am and 6 pm and composition of traffic flow


Mix

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

30,0%

35,0%

40,0%

45,0%

Trail

erTr

uck

Pick-u

p/Jeep

Bus

Mini-b

usTa

xi Car

Moto

rcycle

Mix

Percent of Traffic

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

6,0%

7,0%

8,0%

9,0%

10,0%

06:00-07

:00

07:00-08

:00

08:00-09

:00

09:00-10

:00

10:00-11

:00

11:00-12

:00

12:00-13

:00

13:00-14

:00

14:00-15

:00

15:00-16

:00

16:00-17

:00

17:00-18

:00

Percent of Traffic

77The data collected, nonetheless, does provide useful information and consequently is a significant improvement compared to the situation in the previous years. It is clear that, there has, since 1981, been only a slight increase in total movements – overall, the equivalent of about 1.2% per year, though:

around major centres -Monrovia, Buchanan and Ganta: despite the civil war, on some links there have been quite substantial increases of traffic;

in many rural areas where the condition of the roads has declined: movements have fallen; and

volumes during the wet season are, on average, some 10% below AADT and during the dry sea-son are a similar proportion above AADT: as these are quite substantial variations, it is deemed important that subsequent counting exercises are undertaken in a manner that allows the varia-tions in month-by-month traffic flows to be studied more carefully.

From the hourly distributions of movement for all vehicles it is clear that a number of other counting deficiencies remained:

the 12-hour counts have not been sufficient to capture the full daily volume - and from 20% to 50% of daily movements, depending on site29, may have been missed: it was, therefore, recom-mended that some night counts were conducted as soon as possible – these were undertaken in July. The results are shown in Appendix A. While there is considerable variation between sites, the data suggests that the inclusion of traffic moving from 18:00 to 06:00 hrs would, on average:

– add 58% to day-time overall volumes; but

– the increase in truck volumes may be much higher – up to two to three times the day-time truck volumes (now that security has improved, drivers clearly prefer the cool of the night).30

It is consequently recommended that on subsequent counts, the counting period is extended to 18-hours, 06:00 to midnight, and that supplementary sample counts are undertaken at a selec-tion of sites at varying distances from Monrovia in order to calculate 18 to 24 hour correction factors (see Appendix M on CD for detailed recommendations);

the day-on-day variations of traffic throughout the week remains unknown: it is therefore recom-mended that at two-sites, one close to Monrovia and the other far from Monrovia, there should also be 7-day, 18-hour, counts and that for two of these seven days the counts should be 24-hour: with this additional data, correction factors can be applied to all the stations to account for the additional movements at night and to daily variations.

From the distribution of movements by vehicle type over the counting period, it is noted that:

motorcycles predominate: these are the vehicles of first choice in remote parts and their use in-creases as the condition of the road deteriorates – motorcycles are both cheap and able to access roads that other vehicles find difficult;

most inter-urban public transport is by service-taxi (on average 18.1% of traffic flow – with minibuses and buses only 4.0% and 1.2%): on the assumption that taxis carry a maximum of five passengers and a minibus a maximum of fifteen, the taxi remains the clear mode of passenger-preference – this was unexpected, as taxis are more expensive than minibuses and the qualities of service are similar;

truck movements are, by West African standards, low and are dominated by the movement of pick-ups/small trucks: current economic activity is clearly centred on the city of Monrovia, which is where most imports arrive and from where they are deconsolidated and distributed – and although, in the hinterland, there is much nascent agricultural activity, logging potential and a

29 The initial under-recording of traffic was probably most serious close to Monrovia and other big towns – for the same reason, the July supplementary night-time counts, if applied uniformly throughout the country, may overstate night-time traffic in parts of the country further from Monrovia.

30 cf the RED assumptions that an addition of 33% applied to all vehicle categories would correct for 12 to 24 hour movements.


78 soon-to-restart mineral ore extraction industry, these activities are not currently placing heavy

demand on the roads. The large mineral ore mines will moreover rely on the rehabilitated rail-ways.

The county-to-county average daily movements are shown in appendix L on CD.

5.1.3 Analysis of dry season and wet season road user surveys

The first (dry season) and second (wet season) round road-side interview and origin and destina-tion surveys were based on random sampling at the same stations as where classified counts were undertaken. Expansion factors, for converting the sample data to AADT, were derived for each sta-tion and for each vehicle-type using the classified count data. Separate analyses were used to derive average values for the following (detailed results are shown in appendix C on CD).

average trip times and the distribution of trip times – by vehicle type: although motorcycle trips were on average the shortest, the average trip for these vehicle types even in the dry season was still 94 minutes and over 10% of dry season trips were longer than three hours (equivalent to over 240 km). This, amongst other things, implies that motorcycles are used for both long and short distance trip making. The implied average trip distances by each vehicle category are shown below:31

Table 5-4: Average trip times and distributions by vehicle type

Dry season Wet season Wet season lengths as % of dry sea-

son

Average trip time (mins)

Assumed speed

(km/hr) 31

Aver-age Trip Length (km)

Average trip time (mins)

Assumed speed

(km/hr) 32

Aver-age trip length (km)

Motorcycles 94 79 123 65 74 80 65%

Taxis 122 82 166 124 77 159 95%

Cars 130 82 177 86 77 110 62%

Pick-Ups 229 83 316 157 78 204 64%

Minibuses 230 77 295 143 72 172 58%

Buses 188 71 222 96 66 106 47%

Trucks 438 77 560 240 72 288 51%

Trailers 405 77 245 72 294 57%

Except for taxis, wet-season journey lengths are clearly much shorter than dry-season journeys.32

average vehicle occupancies – by vehicle type; average vehicle occupancies as well as trip purpos-es are shown below by vehicle type and for the dry and wet seasons all vehicle types show high occupancies and the data indicates that maximum passenger-use is being made of the available fleet. The clear overcrowding suggests that most travellers are prepared to sacrifice comfort for a lower fare. Occupancy levels are noticeably lower on trucks during the wet season (uncovered transport is not welcome) and higher on all other forms except buses.

31 RED derived average speeds for gravel surfaced roads of roughness 5 m/km in flat terrain.32 RED derived average speeds for gravel surfaced roads of roughness 5 m/km in flat terrain reduced by 5 km/hr.


79Table 5-5: Average vehicle occupancies and trip purpose by vehicle type33

Dry season33 Wet seasonOccupancy

(pass.)Business trips (%)

Leisure trips (%)

Occupancy (pass.)

Business trips (%)

Leisure trips (%)

Motorcycles 2.1 80% 20% 2.3 87% 13%

Taxis 5.4 99% 1% 5.8 99% 1%

Cars 5.1 79% 21% 5.8 71% 29%

Pick-Ups 6.6 76% 25% 7.1 80% 20%

Minibuses 14.2 96% 4% 13.9 100% 0%

Buses 22.8 89% 11% 17.3 96% 4%

Trucks 14.1 89% 11% 6.2 90% 10%

Trailers 6.1 91% 9% 3.5 79% 21%

On the non-public modes typically only about 20% of trips were stated to be for leisure; and about 80% of trips were stated to be for business.

proportions of vehicles carrying freight – by vehicle type: all vehicle types, except motor-cycles, were used for freight - though the proportion of freight-carrying trips varied considerably be-tween vehicle types. The average amount of freight34 varied by vehicle type: taxis, 1.7 tonnes; private cars, 2.2 tonnes; pick-ups, 5.3 tonnes; minibuses 3.7 tonnes; large buses 5.3 tonnes; and trucks, 14.1 tonnes – these are relatively heavy loads and confirm the similar finding regarding attitudes to cost described above for passengers;

Table 5-6: Proportions of vehicles carrying freight (by vehicle type)

Dry season Wet seasonProportion of

vehicles carrying freight (%)

Average load carried by

vehicles carrying freight (tonnes)

Proportion of vehicles carrying

freight (%)

Average load carried by ve-hicles carrying freight (tonnes)

Motorcycles

Taxis 52% 1.7 66% 1.4

Cars 38% 2.2 51% 2.3

Pick-Ups 58% 5.3 73% 5.8

Minibuses 78% 3.7 76% 2.6

Buses 67% 5.3 68% 4.9

Trucks 80% 14.1 79% 11.0

Trailers 88% 25.0 54% 23.0

Greater proportions of the smaller categories of vehicles carry freight in the wet season. For other vehicle types there is no major difference in the proportions of dry and wet season loaded vehicles. For all vehicle types (except cars and pick-ups) the average wet season load is lower than in the dry season.

nationality: 98% of movements recorded were vehicles registered by Liberians –only 2% were foreign – there was no significant difference in dry and wet season data.

33 The data shown for business and leisure trips for those travelling by public transport is not reliable as the driver, not passengers, was asked the purpose of his trip. There has also been no differentiation between chauf-feurs, passengers and “passenger-drivers” (a chauf-feur being a passenger at the same time).

34 The data is the average load when carrying freight.


80In the dry season, almost 50% of recorded movements were within counties – the remainder was between counties. In the wet season the proportion of within county recorded movements fell to about 40%.

5.1.4 Analysis of agricultural transport volumes on district level

MPW defines primary roads as the major connections between counties incl. county capitals and well used international border crossings and secondary roads as connection between District Capi-tals and Primary roads. Despite the fact that the focus of the Master Plan was clearly on Primary Roads, it was possible to refine a few conclusions about some secondary roads, serving as link be-tween primary roads. The necessary data was collected through moving-observer counts carried out during speed surveys; systematic traffic counts for secondary roads were not undertaken.

Other secondary roads are connecting important present or former agricultural production areas with the primary road network and it was understood that in those regions traffic volumes are probably higher than the threshold of 50 AADT above which roads are usually included in RMS. Additional information about traffic volumes on secondary roads were derived from socio-economic assessments by a spatial surplus/deficit analysis of agricultural production on district level with support of a Geographical Information System (GIS).

Out of the total of 136 districts 8 are not connected to a secondary road at all. For the purpose of this deficit-surplus-analysis only traditional farms in 48 districts which have only or mainly access to the secondary road network and are (almost) completely cut off from the primary road network have been considered.

Agriculture

Agriculture and forestry have recently been the largest sectors of Liberia’s economy. The Liberian agriculture counts for around two thirds of the GDP and 42% of employment and can roughly be divided into three different patterns of production:

concessions (rubber & oil palm);,

commercial farms (rubber, oil palm, fruits, vegetables and coffee); and, and

traditional farms/small scale farmers.

These vary considerably in organization, efficiency and output regarding value (USD/year) and required transport volume (tons/year) for transportation

Concessions and the vast majority of industrial farms are directly connected to the primary road network as also shown in Figure 5-3. Their transport volume was captured by the systematic traffic counts in 2009 and 2010 which were carried out on the primary road network.

The production system of traditional farms rests mainly upon rice, cassava, root crops, sugar cane, banana and plantain ,oil palm, and ,to a small extent (not really relevant regarding the total trans-port volume), on groundnut, coconut, coffee and cocoa. In many cases those small-scale farmers are linked only through the secondary road network to the next markets or to the primary road network.


81 Figure 5-3: Secondary roads, markets and land use


82Agricultural production data in Liberia is in most cases only available at a national level, with the exception of rice and cassava from organizations as FAO and the United Nations. Recent production and consumption figures for the whole of Liberia are presented by FAOSTAT (2008):

production figures of certain food or cash crops nationwide in tons per year (FAO 2008);

consumption figures of certain food or cash crops nationwide in tons per year (FAO 2008); and

consumption figures in kg of particular food and cash crops per person and year (FAO 2008).

Table 5-7: Agricultural statistics Liberia 2008 (tons/year)

Commodity Production Consumption Export ImportRice (Paddy Equivalent) 295,150 397,069 145,660

Cassava 560,000 550,000

Other roots and tubers 71,000 61,200

Maize, green 19,500

Groundnuts (in Shell Eq) 5,400 4,631

Sugar Cane 265,000 40,000

Banana & Plantains 163,000 149,974

Palm Oil 43,160 53,951 692 16,581

Oil palm fruit (183,000 t/y)

Coconuts 7,200 6,480

Coffee 3,000 3,296 93

CocoaBeans 3,000 1,933 1,410

Rubber Nat Dry 56,245

Totals 1,435,410 1,268,534 58,440 162,241Source: FAOSTAT 2008: FAO Country Profiles - Liberia

In addition, the National Population and Household Census of 2008 (LISGIS) is another relevant data source which includes:

urban and rural population on county/district level (LISGIS 2008);

number of urban and rural households per county/district (LISGIS 2008); and

percentage of rural households per county/district growing certain food or cash crops (LISGIS 2008).

Additional data about average farm size, average household size, and cultivated area in total and split by farm/cash crop on County level are also available from the National Population and House-hold Census (LISGIS 2008).


83Table 5-8: County profiles 2008

County Urban house-holds

Rural house-holds

Rural popu-lation

Popu-lation total

Area in sqkm

Area in ha

Av. Farm-size/

house-hold in ha

Culti-vated area in ha

Bomi 4,113 16,395 67,089 84,119 1,909 190,900 0.73 11,968

Bong 20,729 49,081 230,772 333,481 8,653 865,300 1.42 69,695

Gbarpolu 1,640 12,893 75,620 83,388 9,837 983,700 0.93 11,990

Grand Bassa 12,280 35,160 162,737 221,693 7,723 772,300 1.54 54,146

Grand Cape Mount 1,925 22,025 118,931 127,076 4,727 472,700 1.13 24,888

Grand Gedeh 6,925 11,218 83,585 125,258 10,730 1,073,000 1.13 12,676

Grand Kru 604 8,365 54,214 57,913 3,850 385,000 0.77 6,441

Lofa 14,567 35,075 193,713 276,863 9,867 986,700 2.18 76,464

Margibi 17,813 27,282 121,055 209,923 2,661 266,100 1.21 33,011

Maryland 7,650 11,604 88,957 135,938 2,271 227,100 1.13 13,113

Montserrado 213,781 18,804 82,114 1,118,241 1,858 185,800 1.54 28,958

Nimba 19,300 61,434 356,691 462,026 11,418 1,141,800 1.05 64,506

River Gee 2,552 7,270 49,270 66,789 5,053 505,300 0.77 5,598

River Cess 487 13,494 69,120 71,509 5,589 558,900 1.70 22,940

Sinoe 2,594 13,235 89,021 102,391 9,652 965,200 1.09 14,426

Totals 326,960 343,335 1,842,889 3,476,608 95,798 9,579,800 1.22 450,821Source: LISGIS 2008, National Population and Household Census

Estimation of agricultural production on county/district level

The following method was used in this analysis to disaggregate the national level statistics of avail-able food and cash crop production to county and district level:

The assumption was made that all rural households in a county/district growing a particular food or cash crop have the same average yield. Therefore, by knowing the number of farming households in a county/district and the percentage of households that grow a particular crop, the contribution of the county/district to the whole production of a country can be calculated.

As an example, there are10,001 rural households (61% out of 16,395 rural households) growing rice in Bomi County (LISGIS 2008). That number divided by the total number of households growing rice in Liberia (274,388) gives the percentage of rice-growing households in Bomi County from all rice growing households in Liberia (3.8%). This percentage will then be multiplied by the country’s rice production of 295,150tons/year (FAO 2008) to give the production of rice in Bomi County (11,335 tons/year).


84With available consumer data from FAO (2008) concerning the consumption in kg of particular food and cash crops per person and year and assumed that this composition will be the same for the total population in Liberia, it is possible to estimate the surplus (households producing more of a certain food or cash crop than they consume) or deficit production (vice versa) of a certain county. Follow-ing the calculation, the rice consumption of Bomi County must be 7,345 tons/year. That results in a surplus production of rice in Bomi County of 4,628 tons/year. The same method was then used to disaggregate these numbers to district level.

Subsequently, the results have been imported into the project’s GIS containing layers (and their attributes) of the primary and secondary road network in Liberia and of the administrative units (both based on LISGIS categories) to verify those districts which are mainly or only connected to the primary network through a secondary road. The spatial distribution of these districts and their estimated agricultural surplus production in tons/year for selected food and cash crops is shown in Figure 5-4.

The likely total agricultural transport volume in tons/year (surplus plus deficit production) inside the selected districts is shown in Figure 5-5. The obvious deficit production in Lofa is mainly caused by insufficient cassava production. This region also shows a quite high market density along the concerned secondary roads.

Similar information is given for districts in the north-east Nimba region bordering Ivory Coast. A high market density prevails, although here combined with surplus production.


85 Figure 5-4: Agricultural production areas served only by secondary roads


86F igure 5-5: Agricultural production areas and total transport volume –

secondary roads


87

Estimation of AADT on district level

For the further calculation of the ADDT in the affected districts also findings from the two traffic censuses in 2009 and 2010 have been used. It was assumed that the vehicle mix (truck, pick-up/jeep, bus, mini-bus, taxi, car and motorcycle) on these secondary roads will be the same as found on the surveyed primary roads; except for trailers which are not included in the calculations since most secondary roads are not suitable for trailer traffic.

Table 5-9: Vehicle mix and transport volume on secondary roads

Typ ofVehicle

Vehicle mix in % on pri-mary roads

(Census 2009/10)

Corrected vehicle mix

in % on secondary roads (w/o

trailer)

Average load in

tons/ve-hicle type corrected by % of

loaded trips

Average no of pax (dry & wet

season)

Number of vehicles by type for the transport

of 100 tons (one direc-

tion)

Number of vehicles by type for the transport

of 100 tons (both direc-

tions)Trailer 1.1 0.0

Truck 4.9 4.9 10.0 10.2 3.3 6.5

Pick-up/Jeep 13.4 13.5 3.6 6.9 8.9 17.9

Bus 1.2 1.2 3.5 20.1 0.8 1.6

Mini-bus 4.0 4.0 2.4 14.1 2.7 5.3

Taxi 18.1 18.3 0.9 5.6 12.1 24.1

Car 16.1 16.3 1.0 5.5 10.7 21.5

Motorcycle 41.3 41.7 0.2 2.2 27.5 55.1

Totals 100.0 100.0 66.0 132.0Source: Traffic Censuses 2009 and 2010 and further statistical analysis

According to the corrected vehicle mix on secondary roads and their average load (also derived from the two traffic censuses), 66 vehicles are needed to transport a volume of 100 tons of agricultural goods in one direction on secondary roads within the concerned districts. For both directions 132 vehicles are required. These figures have been balanced by the yearly agricultural production of the district in tons and cut down to the daily freight.


88Figure 5-6: Estimated AADT in districts mainly served by secondary roads


89The results show that there are two major regions with estimated AADT above 50. The first one cov-ers districts in Grand Bassa County along the secondary road and railway corridor from Buchanan to Saint John River. At Saint John River a split is recognized. One group of districts follows the second-ary road to Gbarnga, the other one follows the link (not included in the LISGIS road register) to Tap-pita. This link therefore belongs, from Buchanan ahead, entirely to the suggested completion of the basic primary road network (see “missing links” in section 5.1.7).

The second region with its districts and secondary roads is recognized in the north-east of Nimba County bordering Ivory Coast.

Conclusions

Secondary roads in those two regions with concerned districts should be integrated into the MoT traffic survey data base as soon as possible. Additionally “comfortable driving speed” surveys should be carried out.

With the extracted data it will also be possible to incorporate those secondary roads into the network planning exercise at a later stage. Especially the secondary road from Buchanan to Saint John River and then – not classified – further to Tappita as one of the two proposed new links to complete the base primary road network could be given priority.

5.1.5 Traffic forecasts

Growth in base road traffic has been forecast in terms of:

growth in GDP: the IMF forecasts of very substantial increases in GDP are shown on Table 5-10 below – this increase in wealth should cause a large increase in:

– the numbers of road vehicles available; and

– the ability of Liberians to afford travel – current registration/licensing data indicates that there are only about 20,000 vehicles in the country – many other vehicles, particularly motorcycles, may however be in use35 - when motor-cycles are excluded, there is nationally just over one vehicle per 100 people – a very low base36;

for passengers - an elasticity of demand between growth in GDP and growth in the vehicle fleet: the assumptions have been that nationally growth in the use of passenger vehicle and multi-purpose vehicles (pick-ups and 4WDs) is initially at a rate that is 1.2 times the growth in “real GDP” and that this elasticity declines to 1.0 by the year 2025. The national assumptions have, however, been modified in order to take account of:

– the faster than average population growth in and around Monrovia: growth in this area has been increased so as to be in line with the forecast difference between national population growth and population growth in and around Monrovia; and

– the lower growth in more rural areas: growth in these areas has been decreased so as to be in line with the forecast difference between national population growth and the population growth in rural areas;

for freight - an elasticity of demand between growth in the contributions of agriculture, logging and industry to GDP and growth in the truck fleet: in this case the assumed elasticity is 1.0.

35 The number of motorcycles registered/licensed each year is not consistent with the recorded numbers in the classified counts. It is suspected that there is much use of non-licensed vehicles in all categories, but particularly of motorcycles.

36 From traffic data it is known that in 1981 there were about 415 million veh-kms of rural road travel. It is also known that traffic has grown at an average rate of about 1.2% per year during the period from then until 2009. This would imply total present rural movements of about 580.0 million veh-km, which if the the rural fleet is two-thirds the 20,000 national fleet, implies an average utilisation of about 43,500 km per year (cf the 45,000 annual kilometerages estimated for taxis, buses and trucks for use in RED and 30,000 km per year estimated for motorcycles) – a figure which is quite high for light vehicles. Heavy trucks and buses do, of course, have much higher annual kilometrages and private cars much lower kilometerages, but these vehicle categories are estimated to comprise only 7% and 16% of present road traf-fic movements.


90Estimates for the increase in travel are also shown on Table 5-10. It can be seen that by 2030

passenger-vehicle movements in and around Monrovia could be 6.64 times present values;

passenger-vehicle movements in more rural areas could be 4.43 times present values; and

base freight-vehicle movements could be 2.17 times present values – on certain routes where there is substantial growth in the production of produce that has a low value per unit weight, the growth could be much higher.

These forecasts imply an increase in the national vehicle fleet, by 2030, to about 90,000 vehicles, if motorcycles are excluded – or to about 1.6 vehicles per 100 people. The increase in vehicle numbers, when translated into vehicles per person, is diluted by the forecast growth in population.

The number of vehicles per head will have increased over the twenty-year period by a factor of five.


91

Tabl

e 5-

10: F

orec

ast i

ncre

ase

in ro

ad tr

affic

bas

ed o

n G

DP

grow

th (2

010

to 2

030)

19

8719

9720

0620

0720

0820

0920

1020

1120

1220

1320

1420

1520

2020

2520

30 G

DP

(USD

bill

ions

) - c

urre

nt

pric

es(1

)0.

970.

300.

610.

731.

101.

612.

373.

334.

45

GD

P (U

SD b

illio

ns) -

con

stan

t 20

07 p

rices

0.78

0.82

0.88

0.97

1.08

1.19

1.30

1.42

2.09

2.93

3.92

GD

P/ca

pita

(USD

) - c

onst

ant

2007

pric

es22

523

024

326

228

530

933

135

446

859

271

4

GN

I/C

apita

(US

D)

170

Popu

latio

n (m

illio

ns) (

2)2.

242.

763.

333.

403.

476

3.54

93.

624

3.70

03.

777

3.85

73.

938

4.02

04.

461

4.94

95.

491

Gro

wth

rate

(2)

Nat

iona

l2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%2.

1%

Mon

rovi

a an

d en

viro

ns3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%3.

5%

Rest

of C

ount

ry1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%1.

4%

Vehi

cles

in C

ount

ry (b

ased

on

curr

ent L

iber

ian

Regi

stra

tion

Dat

a)(5

)

inc

mot

orcy

cles

1938

921

664

2444

127

468

3050

533

708

5092

770

985

9455

2

exc

mot

orcy

cles

1793

520

087

2276

325

685

2861

431

704

4833

467

791

9072

0

Vehi

cle

per h

ead

of p

opul

atio

n (in

c m

otor

cycl

es)

0.00

540.

0059

0.00

650.

0071

0.00

770.

0084

0.01

140.

0143

0.01

72

of w

hich

: tru

cks

(6.3

% in

201

0 -

see

clas

sifie

d co

unts

)(7)

0.00

030.

0004

0.00

040.

0004

0.00

040.

0004

0.00

040.

0005

0.00

05

of w

hich

: oth

er v

ehic

les

0.00

500.

0055

0.00

610.

0067

0.00

740.

0080

0.01

100.

0139

0.01

67


92

19

8719

9720

0620

0720

0820

0920

1020

1120

1220

1320

1420

1520

2020

2520

30Ve

hicl

es p

er h

ead

of p

opul

atio

n (e

xc m

otor

cycl

es)

0.00

490.

0054

0.00

600.

0067

0.00

730.

0079

0.01

080.

0137

0.01

65

Real

GD

P gr

owth

(%/

year

)(3)

9.4

7.1

4.6

7.7

10.0

11.1

10.7

9.5

9.0

8.0

7.0

6.0

Gro

wth

in G

DP

Inde

x10

010

9.4

117.

212

2.6

132.

014

5.2

161.

317

8.6

195.

521

3.1

313.

243

9.2

587.

8

Sect

oral

Con

trib

utio

ns(3

)

Agric

ultu

re -

GD

P gr

owth

(%/

year

)8.

96.

03.

57.

08.

25.

05.

05.

05.

04.

53.

53.

0

Fore

cast

Con

trib

utio

n of

Sec

tor

to G

DP

(Inde

x)60

.065

.369

.371

.776

.783

.087

.191

.596

.110

0.8

125.

714

8.9

172.

2

Fore

stry

- G

DP

grow

th (%

/ye

ar)

1.0

29.4

8.1

18.9

4.9

5.5

3.7

3.7

3.5

3.0

2.5

2.0

Fore

cast

Con

trib

utio

n of

Sec

tor

to G

DP

(Inde

x)7.

07.

19.

19.

911

.812

.313

.013

.514

.014

.516

.819

.020

.9

Min

ing

and

Pann

ing

- GD

P gr

owth

(%/

year

)28

9.8

109.

47.

9- 2

5.3

238.

216

0.6

75.3

40.0

25.0

19.0

14.0

9.5

Fore

cast

Con

trib

utio

n of

Sec

tor

to G

DP

(Inde

x)0.

20.

92.

02.

11.

65.

313

.924

.434

.242

.710

1.9

195.

730

8.1

Man

ufac

turin

g - G

DP

grow

th

(%/

year

)12

.9- 1

6.7

- 3.8

1.3

7.9

5.0

5.0

5.0

5.0

4.0

3.0

2.0

Fore

cast

Con

trib

utio

n of

Sec

tor

to G

DP

(Inde

x)6.

47.

26.

05.

85.

96.

36.

67.

07.

37.

79.

410

.812

.0


93

19

8719

9720

0620

0720

0820

0920

1020

1120

1220

1320

1420

1520

2020

2520

30Se

rvic

es -

GD

P gr

owth

(%/

year

)10

.47.

67.

26.

77.

36.

45.

15.

25.

04.

03.

02.

0

Fore

cast

Con

trib

utio

n of

Sec

tor

to G

DP

(Inde

x)26

.429

.131

.333

.635

.838

.440

.943

.045

.247

.557

.866

.973

.9

Fore

cast

Bas

e Tr

affic

Gro

wth

Ra

tes

Pass

enge

r Tra

ffic

(mot

or-c

y-cl

es, c

ars,

taxi

s, b

uses

, pic

k-up

s/4W

DS)

Elas

ticity

with

Gro

wth

in T

otal

G

DP(

6)1.

21.

21.

21.

21.

21.

21.

11.

01.

0

Nat

iona

l Ind

ex (2

010=

100)

100.

011

2.0

126.

914

3.2

159.

517

6.8

269.

537

8.0

505.

8

Mon

rovi

a an

d en

viro

ns In

dex

(201

0=10

0)10

0.0

113.

513

0.4

149.

216

8.5

189.

230

8.8

463.

766

4.2

Equi

vale

nt a

nnua

lised

gro

wth

ra

tes

13.6

%10

.3%

8.5%

7.5%

Rest

of C

ount

ry In

dex

(201

0=10

0)10

0.0

111.

312

5.3

140.

415

5.4

171.

025

2.3

342.

444

3.4

Equi

vale

nt a

nnua

lised

gro

wth

ra

tes

11.3

%8.

1%6.

3%5.

3%

Frei

ght (

truc

ks)

Fore

cast

Con

trib

utio

ns o

f Agr

i-cu

ltura

l, Fo

rest

ry a

nd M

anuf

ac-

turin

g Se

ctor

to G

DP

Inde

x(4)

7380

8487

9410

210

711

211

712

315

217

920

5

Elas

ticity

with

Gro

wth

in A

gri-

cultu

ral,

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0


94

19

8719

9720

0620

0720

0820

0920

1020

1120

1220

1320

1420

1520

2020

2520

30Fo

rest

ry a

nd M

anuf

actu

ring

GD

P

Inde

x (2

010=

100)

100.

010

7.8

113.

211

8.7

124.

513

0.4

161.

018

9.5

217.

4

Equi

vale

nt a

nnua

lised

gro

wth

ra

tes

5.5%

4.3%

3.3%

2.8%

Mem

oran

dum

Item

: Exp

ecte

d Ve

hicl

es in

Cou

ntry

(Bas

ed o

n Av

erag

e A

fric

a-w

ide

Dat

a)

inc

mot

orcy

cles

2651

129

162

3239

935

866

3927

342

808

6289

888

218

1180

56

exc

mot

orcy

cles

2563

028

193

3132

334

674

3796

841

385

6080

985

287

1141

34N

ote:

(1)

sou

rce:

198

7-20

07 d

ata,

Wor

ld B

ank

- cur

rent

val

ues

(2) s

ourc

e: 1

987-

2008

dat

a - „

2008

Fin

al N

atio

nal P

opul

atio

n an

d H

ousi

ng C

ensu

s - C

onso

lidat

ed“,

LIS

GIS

.(3

) sou

rce:

200

6 - 2

009

data

and

pro

ject

ions

to 2

014

- IM

F - 2

015-

2030

- C

onsu

ltant

s‘ e

stim

ate

base

d on

adv

ice

from

IMF.

(4) m

inin

g se

ctor

exc

lude

d on

the

grou

nds

that

mos

t min

ing

rela

ted

traf

fic w

ill tr

avel

by

rail.

(5) s

ourc

e: 2

010

data

= e

stim

ates

for y

ears

ther

eaft

er b

ased

on

num

bers

requ

ired

to s

atis

fy e

stim

ated

gro

wth

in tr

affic

(6) i

n re

cent

yea

rs a

vera

gre

real

GD

P gr

owth

of a

bout

9%

, ave

rage

incr

ease

in v

ehic

le re

gist

ratio

ns 1

1% (r

atio

1.2

2) -

also

, tra

ffic

grow

th 1

981-

2009

was

equ

ival

ent t

o 1.

4% p

er a

nnum

des

pite

a m

ajor

dro

p in

GD

P.(7

) con

sist

ent w

ith 6

.5%

of v

ehic

le re

gist

ratio

ns.


95

5.1.6 Traffic in Liberia compared to other African countries

In order to put the situation in Liberia in a wider context, the most recently available motorisation data for most of the nations of Africa, including supplementary supporting data for the years 2002 to 2007 is provided in appendix B (see attached CD).

When “Gross National Income per capita” is plotted against “Vehicles for 1,000 Population” (see ap-pendix B) a clear linear relationship is revealed. The estimated numbers of vehicles, were Liberia to be typical of all African countries, is shown in Table 5-11 below.

Table 5-11: Actual vs. expected vehicles in Liberia


2010 2011 2012 2013 2014 2015 2020 2025 2030Vehicles in Country (based on current Liberian Registration Data

inc motor-cycles 19389 21664 24441 27468 30505 33708 50927 70985 94552

exc motor-cycles 17935 20087 22763 25685 28614 31704 48334 67791 90720

Expected Vehicles in Country (Based on Average Africa-wide Data)

inc motor-cycles 26511 29162 32399 35866 39273 42808 62898 88218 118056

exc motor-cycles 25630 28193 31323 34674 37968 41385 60809 85287 114134

While there are presently a few less vehicles registered/licensed in Liberia than might be expected given average incomes37, the Liberian fleet is expected to grow, to 2030, at a rate that brings it closer to the expected average.

5.1.7 Network effects

Traffic to the East of Liberia and from Monrovia to Tapita and onwards into Ivory Coast currently uses the central highway to Ganta and, thence, moves East via Tapita either to the Ivory Coast or onwards to Zwedru, Fishtown, Harper and Barclayville. The present pattern of movements could radically change if either (or both) of the following links were rehabilitated, viz:

the south coast road from Monrovia to Harper: road distances would reduce, for example:

– from Grand Cru/Barclayville to Monrovia from the present 804 km to 481 km; and

– from Maryland/Harper to Monrovia from the present 717 km to 586 km.

These are very significant savings in distance.

the road from Buchanan to Tapita38: the road distance from Monrovia to Tapita would reduce from 364 km to 249 km – which saving would benefit not only the Liberian domestic traffic travelling to/from this area but also traffic along the ECOWAS West African corridor into Ivory Coast39.

37 A conclusion that supports the consultants’ earlier hypotheses regarding the under registration/licensing of vehicles.38 Maps show a secondary road running in almost straight line between these two points – the consultants on-site surveys were unable to

locate a road on the southernmost section of this link - over this section traffic must divert to the railway service road.39 The main crossing point from Liberia into the Ivory Coast is close to Tappita.

96The case for the development of these two links must clearly incorporate the benefits from the diver-sion of traffic from the more northerly route and an informed decision would require further inves-tigations of other potential routes in this part of the country. The likely volume of this traffic was derived from the Origin and Destination data.

Details on these links are provided in appendix G on CD.

5.2 Rail transport

5.2.1 Analysis

Nimba Railway

Capacity considerations

With the present layout but a reinstalled comprehensive signalling system, the ultimate capacity of the railway is estimated as 12 train pairs, equivalent to 24 mtpa. The blue lines in Figure 5-7 below illustrate that with such line utilisation no time-paths for other trains can be considered feasible (due to unacceptable waiting periods and a general increase of complexities).

Figure 5-7: Buchanan-Tokadeh operating programme for 12 ore train pairs


Thus, the line could cope with the ArcelorMittal traffic. A joint venture with BHP Billiton might, however, require a substantial upgrade. It cannot therefore be certain that the concessionaire would have excess capacity at any stage. Notwithstanding, considerable spare capacity would be available if a doubling of the track was required.

97Financial viability of non-ore traffic

Assuming a mixed train carrying 200 passengers in each direction, 21 freight wagons with a pay-load of 80 tonnes each would be required to achieve financial viability. This corresponds to 505,680 tonnes p.a. However, the amount of non-ore traffic in the pre-war period remained at approx. 61,000 tonnes p.a. Thus, even if spare capacity could be made available, the possibility of such traffic seems much unlikely.

Possible developments

At present, there are no data to support the idea of extending the railway into Guinea .

Bong


As can be seen from the diagram at Figure 5-8 after rehabilitation the capacity of the railway will be 10 ore train pairs, equivalent to 12 mtpa, plus 1 mixed train pair (shown in red) for passenger and non-ore traffic.

Figure 5-8: Monrovia – Bong Town operating programme for 10 plus 1 train pairs


Financial viability of non-ore traffic

In view of age and condition, all rolling stock would have to be replaced. Assuming a mixed train carrying 500 passengers in each direction, 29 freight wagons with a payload of 55 tonnes each would be required in addition to achieve financial viability. This corresponds to 480,095 tonnes p.a. However, during recent years, GEOSERVICES carried up to 1,800 tonnes of iron ore pellets per day but no considerable non-ore traffic. Accordingly, even though spare capacity would be available, it seems not very likely that such traffic potential will occur.

Possible developments

At present, there are no data to support the idea of extending the railway into Sierra Leone.

98

Mano River


Fragments of a station have been found in km 15. Accordingly, it has been concluded that block sections between Monrovia and Tubmanburg could be 15 to 20 km long and about 23 km further towards Mano River. The operating programme has been based on a maximum speed of 45 kph. As can be seen from the diagram at Figure 5-9 below, this would result in a line capacity of

11 ore train pairs plus 1 mixed train pair Mano River – Monrovia plus ; and

4 ore train pairs Tubmanburg – Monrovia.

Depending on gauge and axle load chosen for reconstruction, these figures would be equivalent to

8 mtpa from Mano River plus 3 mtpa from Tubmanburg with narrow gauge and 18 tonnes axle load; or

11 mtpa from Mano River plus 4 mtpa from Tubmanburg with standard gauge and 22 tonnes axle load.

Figure 5-9: Monrovia – Mano River operating programme


Financial viability of non-ore traffic

There is no decision as yet about gauge and axle load for reconstruction of the railway. Accordingly, two options – 1,067 mm gauge with 18 tonnes axle load, and 1,435 mm with 22 tonnes respectively – had to be considered. Assuming a mixed train carrying 200 passengers in each direction with an average travelling distance of 67 km, the number of additional freight wagons required to achieve financial viability would be

44 wagons with a payload of 42 tonnes each equivalent to 556,248 tonnes p.a. for narrow gauge; or

32 wagons with a payload of 55 tonnes each equivalent to 529,760 tonnes p.a. for standard gauge.

Thus, even though spare capacity would be available, it seems not very likely that such traffic poten-tial will occur.

99

5.2.2 Projection

Line capacity

The number of ore trains and resulting excess capacity are summarised in Table 5-12 below. Accord-ingly, spare capacity would be available except for the Nimba railway where it cannot be certain that the concessionaire would have excess capacity at any stage.

Table 5-12: Estimated rail line capacities

Ore Traffic Non-ore traffic capacity

Railway Section Payload-per wagon

Wagons per train

Train pairs per day

Annual capac-ity

[tonnes][m

tonnes]Train pairs

per dayNimba Tokadeh - Buchanan 95 70 12 24.0 0

Bong Bong Town - Monrovia 65 60 10 11.7 1

Mano River (1435 mm)

Mano River - Tubmanburg 65 60 11 12.9 1

Tubmanburg - Monrovia 65 60 15 17.6 1

Mano River (1067 mm)

Mano River - Tubmanburg 50 60 11 9.9 1

Tubmanburg - Monrovia 50 60 15 13.5 1

Financial viability

The following table shows for each railway and option respectively the minimum amount of traffic that would be required to achieve financial viability.

Table 5-13: Minimum rail traffic levels for financial viability

Railway

Minimum annualPassen-

gers FreightInvest-ments NPV FIRR

[Nos.][net

tonnes] [USD] [USD]Nimba 120,400 505,680 9,480,000 710,033 11%

Bong 301,000 480,095 11,220,000 114,706 10%

Mano River (1435 mm) 120,400 529,760 11,460,000 364,405 10%

Mano River (1067 mm) 120,400 556,248 13,620,000 548,042 11%

Monrovia Network (Bong Line) 301,000 430,43015,930,000 1,024,192 11%Monrovia Network (Mano River

Line) 120,400 430,430

Traffic data for the past are fragmentary. However, it is known that e.g. the LAMCO railway carried approximately 60,000 net tonnes p.a. non-ore traffic in the pre-war period. Thus, assuming that the estimated potential of passenger traffic is realistic, freight traffic would have to be about eight times the amount of pre-war traffic. But the GDP is not likely to exceed the pre-war level within the next 20 years, so that such traffic seems most unlikely in the mid- to long-term.


1005.3 Seaport and coastal shipping cargo volumes and forecast

5.3.1 Introduction

Given Liberia’s geographic situation and prevailing trade relations as well as the type of commodi-ties it trades, maritime transport plays a vital role in its economy.

Before the war Liberia’s main exports were iron ore, logs and rubber. Although iron ore and log ex-ports have not yet been resumed, both of these traffics are expected to restart soon. Exports of latex and other types of rubber are still well below their pre-war level but should increase significantly in 5-6 years’ time when replanted trees and new plantation areas come into full production. Ben-tonite exports, which were associated with mining activity, have ceased, and it is not clear whether they will be required for the ore-processing activities envisaged in future. Scrap metal exports have already resumed, but do not show up in the table below because they are now largely containerised. Cocoa and coffee exports, still small, are also largely containerised.

Imports of cement and clinker shipments are already well above their pre-war level as a result of reconstruction needs, and imports of rice are higher than before the war because of urbanisation, the collapse of commercial production of foodstuffs, and internal distribution problems.

Petroleum imports, on the other hand are still only 55% of their 1989 level because of the reduc-tion in road transport caused by deterioration of the road network, the damage to electricity supply networks, and a lower level of manufacturing activity.

Container traffic is now almost double its pre-war level. This is partly because of the greater security which containers provide for project cargoes and consumer goods, and the logistics systems used by the military forces and international aid agencies.

In addition, traffic volumes are still too small to justify the use of chartered vessels for high volume/low value imports such as steel and construction materials. The existence of many small importers who do not have the financial resources to purchase goods in more than a single container load has also increased dependence on container services.

Almost all of this traffic moves through the port of Monrovia, the main exceptions being small vol-umes of project cargo for the mines and cement for local building projects imported through Buchan-an in 2009, and even smaller volumes of coastal traffic moved through Harper for UNMIL.


101Table 5-14: Comparison of NPA traffic volumes in 1989 and 2008 (‘000 tons)

1989 2008 Change (%)Dry bulkIron ore 7,087 - DisappearedBentonite 34 - DisappearedClinker 68 94 38%Cement (in bags) 7 112 1493%Limestone 7 18 151%Scrap metal 5 - DisappearedSub-Total: 7,208 223 -97%Liquid bulkPetroleum Product 378 210 -44%Latex 61 20 -68%Vegetable oil - 3 New Sub-Total: 439 233 -47%Commercial break bulkLogs 178 - DisappearedSawn timber 7 - DisappearedRice 142 292 106%Frozen Product 21 23 9%Vehicles 7 9 25%Rubber Crates 28 24 -14%Break-bulk cargo international 69 13 -81% coastal 12 3 -77%Sub-Total: 464 363 -22%Relief suppliesBulgur Wheat - 13 NewRice - 7 NewOther foodstuffs - 2 NewSub-Total: - 21 NewContainersCargo 272 524 93%Tare weight 73 112 53%Sub-Total: 345 636 84%Grand Total: 8,456 1,476 -83%Key statisticsPopulation (m) 2.56 3.49 36%GDP (US$m) 786 826 5%GDP per capita (US$) 307 237 -23%


Sources: Royal Haskoning Monrovia Master Plan Report – Final, March 2006; NPA web site, IMF statistics

102

5.3.2 Seaports

Monrovia

The base case traffic forecasts for Monrovia are shown in Table 5-15 below. They have been based on the following assumptions:

For petroleum products the same forecasts have been used as those prepared by Royal Haskon-ing/LPRC for the new Fuel Unloading Facility in March 2010.

For heavy fuel oil forecasts have been based on preliminary assumptions about the future power generation strategy which is still far from being agreed. The outcome will be strongly influenced by the government’s decision on BRE’s proposed woodchips-fuelled power station at Kakata. If this does not go ahead and there are delays in rehabilitating the Mount Coffee HEP station, heavy fuel imports could be significantly higher than those shown in Table 5-15.

Bitumen imports will be mainly for the road reconstruction and maintenance programme. The figure of 30,000 tons p.a. in 2014-20 is based on a peak rate for new construction of 150km p.a., using 150 tons of bitumen per km in conjunction with cement stabilisation. The remainder is a nominal allowance for road maintenance needs.

Imports of liquid chemicals will cover a wide range of products such as acids and alkalis, pes-ticides, paints & dyes, lubricants, drilling moods etc. The figures are nominal allowances only, based on other ports of similar size.

Vegetable oil imports will increase at 4% p.a. throughout the forecast period because of consumer preferences for imported products such as soya and sunflower oils. However there could be some replacement of imports by locally produced palm oil as production levels increase.

Palm oil exports have been based on the potential output of the three major producers already contracted/in discussion with Government.

Latex (and crated rubber) exports have been falling significantly, but are assumed to stabilise at their current relatively low levels. This assumption needs to be checked with Firestone as their investments materialise over time.

The figures for clinker and cement imports include bagged cement, which is likely to have a relatively fast handling rate if imported in big (1 ton) bags.


103Table 5-15: Monrovia base case traffic forecasts (‘000 tons)

2009 2010 2011 2012 2013 2014 2015 2020 2025 2030Liquid bulk

Petroleum products 230 256 286 319 356 370 384 462 562 682

Heavy fuel oil 0 0 0 15 17 18 20 45 50 55

Bitumen 0 0 10 20 25 30 30 30 25 25

Chemicals 0 0 3 4 5 7 10 15 17 20

Vegetable oils 8 8 9 9 9 10 10 12 15 18

Palm oil 0 0 0 0 0 0 20 100 200 450

Latex 20 20 20 20 20 20 20 20 20 20

Total: liquid bulk 258 285 328 387 432 454 494 684 889 1,270

Dry bulk

Clinker/cement 246 275 300 350 400 432 467 686 875 1,117

Wheat 13 20 40 43 47 50 54 80 107 143

Fertilizer 0 0 10 15 20 30 40 54 72 96

Iron Ore 0 0 0 0 0 0 3,000 12,000 17,000 17,000

Total: dry bulk 259 295 350 408 467 512 3,561 12,819 18,054 18,356

Break bulk

Rice 148 150 150 150 150 125 100 50 0 0

Frozen products 23 24 26 27 29 31 33 44 58 78

Vehicles 9 10 11 12 14 16 17 26 36 48

Crated rubber 20 20 20 20 20 20 20 20 20 20

Logs 0 20 70 120 120 110 100 56 56 56

Sawn timber 0 0 0 10 20 30 40 76 76 76

Other 12 12 13 13 14 14 14 17 19 22

Total: break-bulk 212 237 290 353 366 345 324 288 266 300

Containers 546 599 674 768 841 881 919 1,481 2,268 3,294

GRAND TOTAL 1,275 1,415 1,641 1,916 2,106 2,193 5,298 15,272 21,477 23,221

of which

Containers (‚000 TEU) 51.0 55.9 62.9 71.7 78.5 82.3 85.8 138.3 211.9 307.7

Wheat imports become established at around 40,000 tons p.a. by 2012, and then grow at 8% p.a. up to 2020 and 6% p.a. thereafter.

Fertilizer imports are assumed to become established at around 40,000 tons p.a. by 2015, and then grow at 6% p.a.

Iron ore exports seem unlikely to start before 2015 because of the amount of rehabilitation work required, the fall in world iron ore prices from their 2008 peak, and contractual delays. The figure of 12.0m tons for 2020 assumes that only the Bong Mines have been brought into produc-tion, but that they are exporting the maximum amount allowed by the China Union MDA. The figure for 2030 adds in a further 5.0m tons p.a. from the western cluster, on the assumption that rehabilitating the railway to Monrovia is more cost effective than building a new rail route to a new export terminal near Robertsport.

Rice imports will be replaced gradually by local production

Cargoes of frozen products (including fish) will grow at 6% p.a., although there could be some replacement of fish imports by fish exports in the second half of the period.


104 Imports or vehicles will increase in line with the vehicle ownership forecasts made in chapter

5.1.5.

Exports of logs and sawn timber have been based on similar assumptions to those set out in the IBRD Diagnostic Trade Integration Study, updated to take into account decisions since then on Forest Management Contracts and Trade Sales Contracts. Logging activities are likely to take place over a very large area, and the distribution of exports between ports will depend to some extent on the improvements which are made to the road network.

For the purposes of the Master Plan, it has been assumed that 20% of timber exports would be routed through Monrovia, 10% through Buchanan, 55% through Greenville, and 15% through Harper. This broadly reflects the geographical distribution of the contracted areas.

Other breakbulk cargoes are assumed to increase at 3% p.a. The relatively low growth rate reflects the fact that most of the general cargo growth will be in containerised cargo.

The container forecast is the average of the high and low forecasts set out in Table 5-16 below.

Table 5-16: Container traffic forecasts (‘000 TEU)


2009 2010 2011 2012 2013 2014 2015 2020 2025 2030Low 51 55 60 67 74 81 89 130 183 245High 51 57 65 76 83 83 83 146 241 371

Buchanan

There is considerable commercial interest in the port of Buchanan, although very little traffic has been handled during the short post-conflict period. The main cargoes that could be handled there are shown in Table 5-17.

They have been based on the following assumptions:

Petroleum products: 10% of the Monrovia throughput, mainly for use in the mines, and for local transport.

Palm oil: 50% of Equatorial Palm Oil production.

Iron ore: Phase 1 of the Arcelor Mittal expansion programme to be fully operational by 2013, and phase 2 by 2020. No allowance has been made for additional exports from BHP Billiton.

Wheat, fertilizers and cement: 10% of Monrovia throughput, based on population and income statistics (wheat), agricultural potential (fertilizers), and the potential road building programme (cement).

Woodchip exports: business plan of Buchanan Renewable Energy.

Logs and sawn timber: 10% of the national forecast.

105Table 5-17: Buchanan base case traffic forecast (‘000 tons)

2009 2010 2011 2012 2013 2014 2015 2020 2025 2030Liquid bulk

Petroleum products 0 0 29 32 36 37 38 46 56 68

Palm oil exports 0 0 0 0 0 5 25 100 200 300

Total: liquid bulk 0 0 29 32 36 42 63 146 256 368

Dry bulk

Iron ore 0 0 4,000 4,000 4,000 4,000 4,000 15,000 15,000 15,000

Wheat/fertili-zers/cement 0 0 0 41 47 51 56 82 105 136

Total: dry bulk 0 0 4,000 4,041 4,047 4,051 4,056 15,082 15,105 15,136

Breakbulk

Woodchips 80 300 450 700 1,000 1,500 2,000 2,500 2,500 2,500

Logs 0 10 35 60 60 55 50 28 28 28

Sawn timber 0 0 0 5 10 15 20 38 38 38

Other (inc containers) 0 0 5 7 10 11 11 14 18 23

Total: break-bulk 80 310 485 765 1,070 1,570 2,070 2,566 2,566 2,566

Grand total 80 310 4,514 4,838 5,152 5,663 6,189 17,794 17,928 18,070

Greenville

Once it has been re-opened, Greenville is likely to revert to its traditional role as the pre-eminent export port for timber. Business enquiries are already being received by logging companies, and it is assumed that around 55% of national timber exports will eventually move through the port.

In the longer-term Greenville could also develop as a significant export port for palm oil, depending on the extent to which the concessions currently under negotiation are actually developed. Based on the location of the plantations, the forecasts assume that it will handle around 35% of Golden VerO-leum exports and 50% of Equatorial Palm Oil exports, with the former splitting its exports between Greenville and Harper, and the latter between Greenville and Buchanan.

However this will depend on the economics of splitting export facilities between more than one port, which will in turn depend on the precise location and development sequence of the main produc-ing areas, road improvement priorities, and the institutional framework for developing and funding export terminals. A more detailed feasibility study is required before undertaking any port develop-ment work.

Table 5-18: Greenville base case international traffic forecast (‘000 tons)

2009 2010 2011 2012 2013 2014 2015 2020 2025 2030Palm oil 0 0 0 0 0 5 25 118 323 563

Logs 0 55 193 330 330 303 275 154 154 154

Sawn timber 0 0 0 28 55 83 110 209 209 209

Rubber 0 0 0 0 0 0 0 12 29 37

Total 0 55 193 358 385 390 410 492 715 963


106It is also possible, in the longer-term, that some rubber will be exported through Greenville, prob-ably in containers. At a stowage factor of 22 tons per TEU, the forecast of 37,000 tons in 2030 would be equivalent to around 3,400 TEU p.a. over the quay, including empty containers inwards.

Harper

The development pattern at Harper is likely to be similar to Greenville, with early dependence on logs and sawn timber followed by the build-up of palm oil and rubber exports. Timber exports are likely to be smaller than at Greenville, and rubber exports larger and more certain.

As in the case of Greenville, the palm oil exports are dependent on the plans of a large foreign inves-tor which is still at a very early stage in project development, and are conditional on satisfactory terms being reached with Government. The palm oil forecast should therefore be regarded as indica-tive rather than confirmed.

Table 5-19: Harper base case international traffic forecast (‘000 tons)

2009 2010 2011 2012 2013 2014 2015 2020 2025 2030Palm oil 0 0 0 0 0 0 0 33 228 488

Logs 0 15 53 90 90 83 75 42 42 42

Sawn timber 0 0 0 8 15 23 30 57 57 57

Rubber 0 0 0 0 0 0 15 35 58 74

Total 0 15 53 98 105 105 120 166 385 661

5.3.3 Coastal shipping

At present road transport experiences only limited competition from sea transport. Although there are some short-distance movements of passengers and freight by sea, usually in open boats with outboard motors, there are no regular commercial shipping services.

Around 12,000 tons p.a. of military and aid cargo is currently being carried by sea between Monro-via and Harper, mainly on the UNMIL ship (the MV Caterina). It has been used in the past for carry-ing troops, but does not have suitable accommodation for commercial passengers.

Some passengers are carried by coastal shipping, but the numbers are small at present – probably no more than 1-2,000 passengers p.a. - because of the absence of regular scheduled services.

It is extremely difficult to predict in advance coastal shipping’s share of the total market (road + sea). The evaluation of the current volumes of traffic moving along the coast by road and sea, goes on to consider:

The growth in road traffic, which is linked to future increases in GDP.

The growth in sea traffic, including international trade suitable for transhipment to/from coastal shipping in Monrovia.

Additional traffic generated by time savings or reductions in transport costs arising from the development of coastal shipping.

The share of the overall market than can be captured by coastal shipping.

The take-up rate for passenger ferry services will depend on two main factors: affordability and service frequency. The fares for a fast passenger ferry service are likely to be high if the vessel is expected to break-even, mainly because of the high fuel costs associated with increased speed. This may make the service unaffordable for most of the people it is intended to serve.


107A two tier market for passenger services is likely to exist, with business travellers, those on govern-ment business and people travelling for urgent or time-sensitive reasons (for example for medical care or during short public holidays) being willing to pay high fares, whilst people travelling routine-ly or just visiting family and friends would be more responsive to lower fares. The way in which the market is split between these different categories is not known, and requires further investigation.

Evidence elsewhere also suggests that an increase in service frequency can result in a more than proportionate increase in passenger numbers, by making it easier for people to get home again. As discussed in the next section, an increase in the number of sailings provided by one ship should also lower unit costs and break-even fares, as long as the additional capacity provided is taken up by the market.

Coastal shipping’s market share for freight will depend on the geographical distribution of cargo within the hinterland of each coastal port and door-to-door delivery costs, which includes double handling costs for sea transport as the cargo has to be transferred onto trucks in most cases for final delivery to the customer.

Some very rough estimates have been made of these additional costs by analysing the distribution of population within Sinoe and Maryland countries by District, and examining the quality of the road connection between each District centre and either the nearest port or the primary road network leading to Monrovia.

In the light of this evidence – and experience of similar situations in other countries - the following subjective assumptions about coastal shipping’s share of road and sea transport have been made.

Table 5-20: Working assumptions about shares of traffic using coastal shipping

Monrovia to Passengers FreightFast ferry Multi-purpose ship High Low

Sinoe (Greenville) 40% 10% 65% 40%

Maryland (Harper) 65% 20% 80% 60%

This assumes that coastal shipping will have a higher market share for freight than for passengers, and a higher share for traffic from Harper than from Greenville.

Forecasts of potential traffic

Forecasts of the potential traffic that could use a coastal shipping service are given in Table 5-21 and Table 5-22 below. The figures are no more than indicative of the order of magnitude of future traffic flows, and require further refinement/verification.

For passengers a distinction has been made between a high quality fast ferry service, and a slower, low quality (but low price) service provided by multi-purpose general cargo vessels. For freight, high and low forecasts have been prepared to indicate the likely range of traffic volumes that can be expected.


108Table 5-21: Potential traffic for a coastal shipping service passengers

(‘000 passengers p.a. each way)

2015 2020 2025 2030Fast ferry

Harper-Monrovia 65 70 95 125

Greenville-Monrovia 30 35 50 60

Other 20 25 35 45

Total 115 130 180 230

Multi-purpose general cargo ship



Other 5 5 5 10

Total 20 20 20 35

Table 5-22: Freight (‘000 tons p.a. each way)

2015 2020 2025 2030High forecast



Other 10 15 20 25

Total 130 180 235 285

Low forecast



Other 10 10 15 20

Total 95 125 165 210

5.4 Aviation

5.4.1 International air traffic – volumes and projections

Historical air traffic data in Liberia is generally lacking. Whatever data that is available relates mainly to ROB. In addition, data integrity is difficult to verify when inconsistencies appear between various sources of data.

For this reason, and for consistency throughout the analysis, the following assumptions are applied:

Traffic data (for ROB only) are extracted from Liberia‘s Vision for Accelerating Economic Growth - A Development Corridor Study (MoPEA and USAID) Republic of Liberia, and National Transport Policy and Strategy (November 2009).

Traffic forecast for DOM are extrapolated from 1980 data contained in “Republic of Liberia Plan-ning and Development Atlas, MoPEA and GIZ” (1983).

Traffic at DOM is assumed to be unchanged since 1980 – 2009 due to the effects of the civil war.


109 Traffic growth based on GDP growth rates between 2009 and 2030 (also see Table 5-10 above)

are sourced from:

– 2009 actual data;

– projections to 2014 from IMF; and

– extrapolation between 2015-2030 from consultants’ estimate based on advice from IMF.

Exceptional surge in air traffic in 2007 is attributed to influx of foreign advisors and facilitators during the period of the Liberian national elections.

Subsequent Peak-hour Passenger (PHP) figures are extracted from forecast traffic, calculated on 30th busiest hour principles that include weighting from average aircraft seat load factors.

Data for air freight is unavailable. Most airfreight transacted at Liberian airports (ROB in particu-lar) are “belly cargo” on passenger aircraft. Full freighter commercial flights tend to be excep-tional events and are statistically immaterial in the present context of study.

ROB’s traffic loading in terms of passenger throughput is estimated at 121,000 passengers per an-num (2009); a 4.6% increase from 2008 actuals of 116,000 pax per annum. With respect to aircraft movements, no significant increases were observed between 2008 and 2009 with 3,830 aircraft movements being recorded between the two intervening years. (see Table 5-23 below.)

Table 5-23: ROB - Actual traffic figures

Air Traffic Year A/c Mvts%

Change

A/c Mvts p.a.

(‚000)

Pax %

Change

Pax p.a. (‚000)

% Δ 2006 & 2008

Actuals 1 2006 4,31 88 ATM‘s Pax

2007 2 56% 6,72 87% 165 -11% 31%

2008 -43% 3,83 -30% 1161 Source: Liberia’s Vision for Accelerating Economic Growth - A Development Corridor Study (MoPEA & USAID); Republic of Liberia, National

Transport Policy & Strategy (November 2009)2/ Extraordinary surge in air traffic due to Liberian national elections occurring in the year.


110Applying the above assumptions, estimated passenger and aircraft movement peak loads are derived and described in Table 5-24 below:

Table 5-24: ROB - Estimates of passenger and aircraft movement peak loads

Forecast Year Mvts p.a.

(‚000)

Growth Rate1

Pax p.a.

(‚000)

Abs Av Peak/Day

Pax

PHP2 PHP Index

Av Pax Dist /

Hr

Design Hr

Mvts3

2009 4,01 4,6% 121

Phase 1 2010 4,32 7,7% 131 422 226 0,54 25 2,0

2011 4,75 10,0% 144

2012 5,28 11,1% 160

2013 5,86 10,7% 177

2014 6,51 9,5% 193

Phase 2 2015 7,24 9,0% 211 691 380 0,55 35 3,0

2016 7,89 9,0% 230

2017 7,89 250

2018 7,89 273

2019 7,89 297

Phase 3 2020 7,89 8,0% 321 1033 620 0,60 53 3,3

2021 8,52 8,0% 347

2022 8,52 375

2023 8,52 405

2024 8,52 437

Phase 4 2025 8,52 7,0% 468 1474 884 0,60 77 3,5

2026 9,12 7,0% 500

2027 9,12 535

2028 9,12 573

2029 9,12 613

2030 9,12 6,0% 650 1780 1068 0,60 107 3,61/ Source: Liberia’s Vision for Accelerating Economic Growth - A Development Corridor Study (MoPEA & USAID)Republic of Liberia, National Transport Policy & Strategy (November 2009)2/ Traffic growth rates applied between 2009 and 2030 sourced from - 2009 data and projections to 2014 - IMF - 2015-2030 - Consultants’

estimate based on advice from IMF.3/ Surge in air traffic due to Liberian national elections occurring in the year.


111The pragmatic future traffic scenario for ROB may be summarised as follows40:

4.6% - 9% straight-line growth over the intervening 10 years up to 2019;

8% straight-line growth between 2020 and 2024;

7% straight-line growth between 2025 and 2029; and

6% straight-line growth from 2030 onwards.

Based on preliminary traffic forecast above, future requirements for ROB are described by Table 5-25 below.

Table 5-25: ROB - Estimated Future PTB requirements

PTB Planning Parameters 1 2010 2015 2020 2025 2030Design Aircraft Mvts per hr 2 3 3 4 4

Pax Terminal PHP 226 380 620 884 1.068

PTB Size (sq.m) 2 7.910 13.300 21.700 30.940 37.3801/ All figures above are preliminary and indicative. They have to checked and verified at time of AMP and detailed design at time of imple-

mentation.2/ PTB sizing is based on 35m2 per PHP (IATA ADRM)

With increases in passenger throughput, air traffic movement is expected to correspondingly increase through increases in aircraft deployment and flight frequencies. This is the likely scenario rather than through the deployment of larger aircraft in the shorter-term. In the case of ROB, in-creases in aircraft movements are not runway and/or apron limited; ROB has the capacity to absorb the increases with minor maintenance works to its pavement surfaces.

5.4.2 Domestic air traffic – volumes and projections

There are about 30 other domestic or rural airports/airstrips (DOM) throughout Liberia. All are understood to have unpaved runways ranging between c.900m to c.1800m in length and cleared for VFR (Visual Flight Rules) only flight operations. For the present moment, they are principally understood to have sufficient runway and apron capacities to accommodate expectant low average traffic volumes of c.2-3 aircraft movements per day, and passenger throughput of c.47 per day (see Table 5-26 below).

A recent report on Liberian Airstrips issued by the LCAA (May 2008) suggests that many have poor runway conditions and/or non-existent passenger handling facilities that are in various states of disrepair. Even in their basic configuration, and given the prevailing adverse economic situation, it seems inconceivable that GoL can justifiably manage and support all 30 airports. Airport closures appear inevitable, and choices and priorities over such decisions need to be made. The usual mecha-nism for resolving such issues may be via an Airport Consultative Committee (ACC) or through an independent cost-benefit analysis, or both. This study should identify the core airports that the Government will maintain and may be the next step for resolving DOM issues.

40 Traffic growth rates applied between 2009 and 2030 sourced from 2009 GDP data and projections to 2014 - IMF - 2015-2030 - consul-tants‘ estimate based on advice from IMF.


112Table 5-26: Traffic throughput at domestic airports in Liberia

(excluding ROB & MLW)

Name of Airport Av Total Flts per day Abs Av Pax per day

Greenville 6 90

Harper 4 60

Zwedru 4 60

Kongbo 3 45

Yekepa 3 45

Grand Cess 2 30

Voinjama 2 30

Foya Kamala 2 30

Sasstown 2 30

Average Tfc 3 47Source: Republic of Liberia Planning and Development Atlas, MoPEA & GIZ (1983)Note: There are 29 other minor domestic airfields all handling less than 1 aircraft movement per day. These airfields are ignored in the

present traffic calculations

Presently all [short-range] aircraft operating to MLW nominate ROB as their alternate airport for [IFR] flight planning purposes.

In the absence of an alternative, ROB has become the default alternate airport for flight operating into MLW. While larger long-range aircraft flying into ROB have the ability to carry sufficient fuel to divert further afield, for example to Abidjan (ABJ, 720 air-km) or Freetown (FNA, 410 air-km), short-range commuter-type aircraft that operate to ROB from domestic airfields in Liberia’s hinterland do not have sufficient range to divert to ABJ or FNA. Furthermore, it is understood there are no refuel-ling facilities at nearby airports within reasonable range, if needed.

Whilst ROB is fully cleared for instrument approaches in marginal weather and/or IFR conditions, the proximity of ROB and MLW to each other, especially in poor weather conditions, make this ar-rangement principally unsuitable. This is simply because, in the event of bad weather, both airports are highly likely to face similar conditions at the same time. In such instance, the ability of ROB as a safe alternate to MLW becomes questionable. This condition can be mitigated by reinstating a suit-able existing airport elsewhere upcountry.

From a strategic aviation planning point of view, Liberia primarily needs one fully functional domes-tic airport upcountry. It should have the ability to function as a viable short-range alternative to ROB. Such an airport ideally needs to be 45 to 60 minutes flight time (or within 300 air-km) from ROB. Such an alternate airport does not presently exist.

The identification and development of a technical [short-range] alternate airport (DOMAlt) is there-fore a central proposal of this Master Plan. It should be identified in the study proposed above to identify the core airfields that will be maintained by the GoL

Based on preliminary traffic forecast, the future requirements for DOMAlt are then established as follows.


113Table 5-27: DOMAlt - Estimates of passenger and aircraft movement peak loads

Forecast Year Mvts per Day

Growth Rate3

Pax per day

Abs Av Peak/Day

Pax

PHP4 PHP Index

Av Pax Dist /

Hr

Design Hr

Mvts62009 3,11 4,6% 47

Phase 1 2010 3,35 7,7% 50 59 32 0,54 4 1,6

2011 3,68 10,0% 55

2012 4,09 11,1% 61

2013 4,55 10,7% 68

2014 5,05 9,5% 74

Phase 2 2015 5,61 9,0% 81 97 53 0,55 13 2,4

2016 6,12 9,0% 88

2017 6,12 96

2018 6,12 105

2019 6,12 114

Phase 3 2020 6,12 8,0% 123 145 87 0,60 20 2,5

2021 6,61 8,0% 133

2022 6,61 144

2023 6,61 155

2024 6,61 168

Phase 4 2025 6,61 7,0% 179 206 124 0,60 30 2,7

2026 7,07 7,0% 192

2027 7,07 205

2028 7,07 220

2029 7,07 235

2030 7,07 6,0% 249 249 150 0,60 41 2,81/ Source: Liberia‘s Vision for Accelerating Economic Growth - A Development Corridor Study (MoPEA & USAID)Republic of Liberia, National Transport Policy & Strategy (November 2009)2/ Traffic growth rates applied between 2009 and 2030 sourced from - 2009 data and projections to 2014 - IMF - 2015-2030 - Consultants‘

estimate based on advice from IMF.3/ Surge in air traffic due to Liberian national elections occurring in the year.4/ Based on principles of 30th busiest hour, with weighting from average aircraft seat load factors & 16.6 hrs per day.5/Distribution of aircraft movement at DOM, unlike distribution of peak pax, is expected to be suitably spread out. This can be expected to

change and needs to be addressed in a future AMP study.6/ Design hour peak is based on actual mvts per annum, divided by 365 days multiplied by a factor of 0.14.


Ministry of Transport Parker House / Broad Street Monrovia, Liberia

Ministry of Public Works Lynch Street Monrovia, Liberia

5 transportation volumes – analysis and projectionsmot.gov.lr/doc/cap_5.pdf · 2014. 10. 10. ·...

Documents