SMALL SCALE TRANSPORT MODELLING: THE CASE OF A ROAD
THROUGH SERENGETI NATIONAL PARK IN TANZANIA
María Díez Gutiérrez
Trude Tørset
Norwegian University of Science and Technology
Eirik Skjetne
Norwegian Public Roads Administration
James Odeck
Eivin Røskaft
Norwegian University of Science and Technology
ABSTRACT
This paper pursues to defend the use of simple transport models for noncomplicated but still difficult decisions related to infrastructure projects. The
Serengeti National Park is used as a case study. The Tanzanian government
has initiated work on upgrading the road between Arusha and Mara regions to
bitumen standards, including a stretch through the Serengeti National Park.
Some alternatives to the existing road alignment have been suggested, and
many local, national and international parties have strong interests in specific
solutions. The decision must thus balance between preserving, economic
development, social and health, and environmental issues. A simple transport
model is designed and applied to shed light of the demand, both current and
in the future. Having the distribution of traveller categories made it possible to
analyse the traffic volumes of the different road solutions to the different
interest groups.
1.
INTRODUCTION
Infrastructure projects are important investments whose precise assessments
are crucial for making the most efficient allocation of funds. Cost-benefit
analysis (CBA) is a tool used in the planning phase that acquires a strong
weight in the decision making process (Jones et al., 2014). CBA is an
international method that consists of giving monetary value to some of the
impacts generated by a project. These effects are mainly road user benefits,
operating costs, accidents, noise and pollution.
An important input in the CBA are the transport demands obtained by
Transport Models. Transport models are applied to forecast traffic volumes
from changes in the transport infrastructure. Models estimate traffic volumes
in a future situation on existing roads and on roads that are not built yet. They
rely on data about the current traffic and development trends in factors that
might change the demand.
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Traditionally transport models have been designed according to the four-step
method. To simplify the decision making process from the travellers, models
are structured in four steps reflecting the choices regarding traveling: (1) Make
a trip (or not), (2) where to go, (3) which mode to go by, and (4) which route to
follow (Ortuzar & Willumsen, 2011). Newer models are based on a wider
range of input data, and split the population in more segments. They
represent the link between the different choices and treat the variation in more
sophisticated ways than the traditional four-step method. In addition, some of
the models include the dynamic link between land use and transport demand,
and some models even take the choice of time for the trip into account.
Nevertheless, the underlying structure remains the same even if the transport
models are more developed to represent the complicated transport demand
and supply market.
Looking at the topics on conferences related to making and using transport
models, one might get the idea that all transport models are complex and
using them is restricted to complicated traffic systems. Nonetheless, models
could be really useful in simpler, easy-to-understand traffic situations. A
statement that leads us to the question: When is a transport model useful?
The quotation: “Essentially, all models are wrong, but some are useful” (Box
and Draper, 1987) indicates that it is not the accuracy of the model itself that
makes the model useful but how to reflect around the results from the model.
If a transport model is adequate to the task, it might help estimate a possible
traffic situation in the future and explain the impetuses behind the
development patterns. A model and its use should be transparent, objective
and meet the requirements of the analysis. It should be able to answer the
right questions about how the transport will change and represent the current
situation and the schemes as realistic as necessary. This is as valid in simpler
situations as in more complex ones.
The estimation of traffic volumes in urban areas have been in constant
development, leading to advanced models. However, estimations on low
volume roads have received little attention (Mohamad et al., 1998). Currently,
researchers have started to investigate low volume roads, as they are less
safe and have high operating costs. Kidner and Wingate (2013) stated that
improving the estimation of traffic volumes on those roads will lead to better
planning and more efficient operations.
This paper contributes to the literature by exploring the usefulness of a
simplified transport model designed in a simple way with limited resources in
the decision making process. Our research questions are: (1) is a simplified
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transport model a useful tool for uncomplicated transport flows? (2) Does a
simplified transport model help to clarify the causal relationships?
Our example is a game reserve with many valuable species regarded as
essential for maintaining the ecosystem. We simulate the future transport
demand in Serengeti National Park. The current traffic volumes in the Park
are based on traffic registrations at gates and airport terminals when visitors
arrive at the Serengeti. The future traffic volumes are estimated following the
actual growing trends for tourists and locals. We further account for the
transport diversity, namely: (1) tourist transport to/from the Park, (2) tourist
transport through the Park, (3) Tanzanian transport to/from the Park, (4)
Tanzanian transport through the Park, (5) heavy vehicles transport to/from the
Park, and (6) heavy vehicle transport through the Park.
We model different network scenarios by using the current road network as
the "do-nothing" scenario and adding two alternatives; a road improvement
through the northern Serengeti and a new southern road outside the Park. In
addition, we create a new scenario simulating the new international airport
proposal in Mugumu.
2.
SERENGETI NATIONAL PARK
The Serengeti is a world heritage site in the northwest of Tanzania, with an
extension of nearly 14.750 km2. The wildlife of the Park is known around the
world, receiving approximately 235.000 tourists per year, many of them from
Europe or North America.
2.1. Current infrastructure
Figure 1 shows the location of the Park, the entrances by road (gates) and by
air, as well as part of the transportation network.
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Serengeti
National
Park
Figure 1: The Serengeti Network Map
The road network in the Serengeti consists of more than 1.000 km of safari
roads, and 350 km of main roads. These are the connection between the Lake
District (Musoma and Mwanza) and Arusha/Kilimanjaro region, and the
touristic access to the National Park. The road between the Kilimanjaro
international airport and the Serengeti Central, which is one of the worst
geometrically designed, is also the most popular road between Arusha and
Lake Victoria. This might explain the long travel times on the road vehicles
and the increase in air traffic.
The Park is also accessible through each of the five airstrips located inside.
Nevertheless, only 20% of the tourists accessing the Park are using air mode,
potentially due to the high price of the plane tickets in the tourist packages
(Sekar et al., 2014). In contrast, the air share for locals reaches almost 45%.
2.2. Future infrastructure
Tanzania is facing a double-edged sword. The increasing traffic volumes by
the human development and the tourist demand might threaten the wildlife
habitats in the Serengeti but also represent economic growth. There are
already evidences of mass tourism in the Ngorongoro crater road, which led to
a population decline in wildebeest and gazelles (Estes et al., 2006). The roads
inside the Serengeti are unpaved. Consequently, as traffic volumes grow, so
does the dust pollution, becoming more harmful for animals and plants
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(Ndibalema et al., 2008). Conversely, improving road conditions, travel time
and cost, will expand the market access for agriculture and will generate new
economic activities (Haule, 2005). Moreover, it will improve access to socioeconomic services (TANROADS, 2010). Accessibility to good roads affect not
only local poverty (Haule, 2005), but also the attractiveness of the tourist
accommodations (Bayliss et al., 2014).
The actual roads cannot take the increasing traffic volumes and hence, the
network needs to be expanded. Different alternatives have been proposed
and are shown in Figure 2. These alternatives are a southern road, a northern
road, and a new international airport in Mugumu.
Figure 2: The Serengeti network improvements alternatives
The decision process for the selection of the best alternative is complex and
arises many debates. Several parties, local, national, and international, want
to contribute, as the Serengeti is part of World Heritage Site. Dependent on
where the new road comes, different local citizens will have new opportunities.
If the northern alternative is chosen the villages north east of the park will
have better access to the western public services. Røskaft et al. (2012)
conducted interviews in the area concluding that this alternative would benefit
more than 80% of the inhabitants in the north. However more people live
south of the Park, and thus the southern alternative is perhaps serving more
people (Grant et al., 2015). The North alternative might spread the traffic
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effects in the Park (Fyumagwa et al., 2013), although it might also cause
habitat fragmentation since it is within the migration path for the wildebeest
(Dobson et al., 2010).
Tanzanian government has supported the northern alternative and made an
Environmental and Social Impact Assessment (ESIA) (TANROADS, 2010).
ESIA evaluated the consequences of the road in the phases of design,
construction and operation using a cost benefit analysis. It considered
environmental and social impacts widely covering potential impacts. In
addition, it suggested how to mitigate the negative effects, giving monetary
values. However, when it comes to traffic volumes in the CBA it was only
considered the volumes on the new road.
3.
METHODOLOGY
Changes in a road lead inevitably to variations in the network as people might
change their routes and there may be new road users. Therefore, we build a
transport model to simulate the traffic volumes throughout the network. We
developed a base scenario with the existing network and origin-destination
(OD) data. We evaluated the traffic volumes for the different alternatives in the
modelled year (2013) and in the future (2030).
The traffic evolution between 2006 and 2013 represented an annual increase
of about 10 %. If such rate is maintained, the number of vehicles will be
doubled in 7 years. Nonetheless, we assumed a more conservative evolution,
considering a linear increase instead of exponential, meaning that the traffic
volumes in 2030 will be 1.75 times higher than in 2013.
3.1. Network
The network used in the model corresponds to the northwestern Tanzania.
The area was divided into 21 zones (6 inside the Serengeti) with similar
characteristics. In addition, the 11 airports in the surroundings were also
considered as specific zones. The zones were represented by centroids and
joint to the network by connectors. The network was composed of links and
nodes, aiming to simulate the different categories of roads and the
intersections, respectively. The main characteristics of the network can be
seen in Table 1.
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Table 1: Links characteristics \ Nodes characteristics
Link type
National road
Main truck access road
Main road in protected area
Access road
Connector
Speed (km/h)
80
60
50
50
30
Node type
Zone centroid
Intersection
Airport
Gate
City
3.2. OD data
The data were collected at the gates and airstrips. At the registration points,
all the vehicles stop in order to pay the entrance fee and complete the
registration form. Some of the questions are the entrance name and
destination of the trip. The complete list of questions and the Naabi gate are
shown in Figure 3.
Figure 3: Naabi gate (retrieved from (Panoramio, 2015)) / Data registered at the gates and
airstrips
We divided the traffic into two main categories. The first one was transit. This
type of traffic had both the origin and destination outside the Park. It was
divided in three subcategories: tourists not visiting the Park using light
vehicles; locals travelling for commuting or business purposes also with light
vehicles; and, heavy vehicles up to 7 tons (buses and trucks) with, mainly,
commercial purposes.
The second transport group was non-transit. This type of traffic had either the
origin or the destination inside the Serengeti. It was also divided in three
subcategories: tourist with light vehicles, locals using also those vehicles, and
heavy vehicles for supplying the accommodations and services inside the
Serengeti.
The data was manually recorded, which might imply certain bias. Another
possible source of bias was that the transit traffic did not state its destination.
Therefore, we assumed a distribution of destinations depending on the
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registration point for the transit traffic. The data corresponded to the
registrations at the Gates towards the Park. We assumed that the traffic in the
opposite direction was the same.
Three non-consecutive days per season1 in 2013 were digitalised for this
study. We obtained the annual average daily traffic (AADT), as the average of
the twelve days, three per season. In doing so, we accounted for season and
weekdays variations.
3.3. Transport model
The Transport Model was built using the software CUBE. The main input were
the network and the fixed OD matrixes already divided by road user type. The
step carried out by the model was the route assignment. In this phase, the OD
matrixes were assigned to the links on the network. It was based on the
selection of the route that minimises the generalised costs for each road user
type.
Generalised cost is the sum of distance, time, and monetary value of a trip, as
shown in equation (1). Since the units of each of them differed, a linear
function was used, consisting of the attributes of the journey weighed by
coefficients that represent relative importance of the factors to the travellers
(Ortuzar & Willumsen, 2011).
GC = a1 Time + a2 Distance + a3 Costs
[ai : weighed coefficients]
(1)
Generalised costs can be measured either in monetary or in time values. If the
latter is agreed, the multiplier of time could be considered as the value of time
(VoT). This value varies widely among the literature, being constant in Norway
and other countries such as Chile or the UK, or varying by intervals like in the
USA. There is not a study on the VoT in Tanzania, so we assumed the values
in Table 2.
Table 2: Value of time assumed in Tanzania for this study
Tourist trips
Worker trips with light vehicles
Commercial trips with heavy vehicles
VoT
60
30
120
The weighed coefficient for the distance account for the cost of fuel and
vehicle maintenance, we assumed 0.1 and 0.15 for light and heavy vehicles
respectively.
‘Short dry season’: January, February. ‘Long rains season’: March, April, May. ‘Dry season’: June,
July, August, September, October. ‘Short rain season’: November, December.
1
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In addition to the generalised costs, every link has different level of service
(LoS) depending on the road user type, which is the impedance cost element.
We assumed distance, travel time, road geometry (curvature and slope),
possible penalties and costs. However, the fact that there are not many routes
to choose between leads to the same traffic volumes as considering only time
and distance.
3.4. Transport model output
The output of the model represented the traffic volume per link in total and for
each road user type. Moreover, it was possible to assess the total time and
distance travelled by the vehicles.
Figure 4 shows the vehicle kilometre (vkm) and total traffic volumes (AADT)
on the network in 2013 for the existing network (E.N.), northern alternative
(N.A.) and southern alternative (S.A.).
Vkm in a day
S.A.
N.A.
E.N.
0
200000
INSIDE PARK vkm
400000
OUTSIDE PARK vkm
Figure 4: Traffic volumes (AADT) and vkm in the different network scenarios in 2013
The AADT at Naabi gate from the output was compared to traffic counts at
Ngorongoro the same year. The registrations had 75% of the counted traffic
volumes, probably due to a diversion east of the gate. Having almost the
same traffic levels in the registrations and the counts, we expect the model to
represent the real traffic sufficiently correct.
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The scenarios in 2030 were built increasing the OD trips following the linear
annual traffic growth of 7%. Figure 5 shows the outputs.
The outputs of the scenarios in 2030 with the Mugumu airport are represented
in Figure 6. They were built increasing and changing the OD trips. 75% of the
trips with either origin or destination in the international airport of Kilimanjaro
were changed to the potential new international airport in Mugumu.
Vkm in a day
S.A.
N.A.
E.N.
0
200000
INSIDE PARK vkm
400000
OUTSIDE PARK vkm
Figure 5: Traffic volumes (AADT) and vkm in the different network scenarios in 2030
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Vkm in a day
S.A.
N.A.
E.N.
0
200000
INSIDE PARK vkm
400000
OUTSIDE PARK vkm
Figure 6: Traffic volumes (AADT) and vkm in the different network scenarios in 2030 with
Mugumu airport
3.5. From traffic volumes to monetary values
The results show that the upcoming transport growth in the Serengeti cannot
be dealt only by the main access road. The northern alternative spreads the
traffic inside the Park, while the southern alternative removes a low share of
the traffic from the Park. These alternatives do not generate important
reductions if traffic volumes inside the Park, or at least they must be combined
with other measures.
The assessment should not only be based on the traffic volumes. The traffic
volumes on the roads per road user type in the different scenarios, output of
the transport model, might improve the assessment of several variables in the
CBA. Some of them are described as follows.
Maintenance costs includes the work needed to keep the road operated to a
minimum standard, such as line marking, asphalting or bump fixing. The traffic
volumes, especially the heavy vehicles, will accelerate the road wear, and
hence these costs will increase.
Air pollution can be defined as some gases such as sulphur oxides, ammonia,
nitrogen oxides, and small particles floating in the air have harmful
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consequences for the environment and for humans, causing serious health
problems (Barfod & Leleur, 2013). The above mentioned gases are products
of combustion or partial combustion, meaning that this impact can be
considered proportional to the kilometres driven per vehicle type.
Besides the air pollution impact, which can be considered local, there is the
global effect of gas emissions. The global warming is the rise of average
temperature of the atmosphere and the oceans at a global level due to the
greenhouse effect. Such increase of the temperatures can change
ecosystems in unpredictable ways. Some of the greenhouse gases are
methane and carbon dioxide, which are originated during the production and
combustion of fuel respectively (Barfod & Leleur, 2013). The monetary value
is assigned by multiplying the unit price of each mode of transport by its
variation in kilometres driven.
Noise pollution can be defined as an excess of noise that can cause not only
discomfort, but also physiological and psychological damages such as
increased stress levels, sleep disorders or hearing loss (Tzivian et al., 2015).
It affects the humans in urban residential areas and the wild animals in the
nature. The costs of noise are calculated, for every transport mode, by
multiplying its unit price by the number of kilometres driven.
Wildlife road killings is an important variable in this case study, since the road
crosses a game reserve. The unit price is given in monetary units per vehicle
and kilometre driven, meaning that the number and severity of the accidents
are not included, but only the costs as a whole.
4.
DISCUSSION
The use of simple transport models, even in uncomplicated transport networks
cannot be replaced by counts. An assessment should not be based on an
isolated road because the traffic volumes will vary in the whole network after
an infrastructure project. Estimating the traffic volumes only with counts may
be biased if not all the links are considered, as the assumptions of OD might
be misunderstood. Using transport models, we can observe the interaction
between the roads for different alternatives. Moreover, we can determine the
travel time or distance driven for the different road user types and on the
different roads. Others have made simple transport models using counts to
suggest solutions to reduce the impacts from traffic in vulnerable areas
(Lambas and Ricci, 2014).
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We have made a very simple transport model that contributes to
understanding the transport demand in Serengeti and the nearby areas.
Compared to using one-day counts, as was done in the ESIA, the main
improvement with using our model is that it considers the impacts of the
different scenarios for the different traveller groups. If we define a transport
model as a calculation method made up of components of knowledge or
information, and do not restrict the definition to specific calculation methods,
the method mentioned above, using counts, could be regarded as the
simplest of transport models. Our method is also simple, and would then
represent the next step on a complexity ladder.
What did we gain from adding a little extra complexity? In addition to
confirming traffic volume numbers, we got a more transparent method,
indications of the variation in the traffic volumes, both random and seasonal
variations. Dividing the traffic in groups adds possibilities for the analysis,
because the groups have different expected growth curves, transport service
needs and willingness to pay for these services. Using this information might
be the key to know how to prioritize and provide the optimal transport service
solution for traffic to, from and through the Park.
Transport models might not explain by themselves the impacts of an
infrastructure project. Our simple transport model is an additional tool that,
used in the decision making process, might help to assess the impacts of
important variables, such as air pollution, wildlife-vehicle collisions or
operating costs. Many of these variables have a cost per vehicle type and
vkm; hence, the transport model clearly improves the monetary evaluation.
CBA is defended as an objective method. However, it is difficult to assign
value to some of the effects of an infrastructure (Adams, 1994). Therefore, in
more advanced phases of the decision making process, the non-monetary
values are included, such as effects on environment, cultural heritage or
natural resources. Additionally, assessments of spatial and social
development play a role for decision makers when the results of a project
affect special areas or groups (Statens vegvesen, 2006).
5.
CONCLUSION
This paper aims to highlight that the simulation of traffic volumes by simple
transport models improves the assessment of new infrastructures. Simple
models in low traffic volumes roads might be as effective as complex models
in urban areas.
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1) Transport models are very useful, even for uncomplicated transport
networks and volumes.
2) The impacts of new infrastructures can be partly addressed using
simple transport models. Nonetheless, several tools should be
combined to obtain a good overall assessment.
Further research might be oriented to improve the simple transport models
when there is data limitation. This may refer to the acquisition of OD patterns
or to the estimation of weighting values in the generalised costs.
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