Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
INTEGRATING GIS AND AHP FOR BUS ROUTE DEFINITION
Andre Dantas
Department of Civil Engineering - Nagoya Institute of Technology
Nagoya, 466-8555, Showa, Gokiso, Japan
Tel/FAX: 81-52-735-5496
andre@keik1.ace.nitech.ac.jp
Luis Sergio Silveira
Yaeko Yamashita
Transportation Program, University of Brasilia, Brazil
KEY WORDS: GIS, AHP, bus routing problem
ABSTRACT
In this paper, we present the integration of GIS and Analytical Hierarchical Process (AHP) to solve a bus routing
problem. This integration is essential to consider geographical and spatial characteristics of urban areas and
consequently to provide to transportation planners the fundamental information for decision-making activities. In
this sense, raster data format was explored in order to conduct spatial analysis, that significantly contribute for the
obtainment of information for judgement activities of AHP. The application of GIS-AHP integration in Sobradinho
City, Federal District, Brazil, has showed its capability to help planners through out a effective and comprehensive
approach of the most important factors affecting the bus route definition.
of resource limitations to conduct evaluations such as
bus route definition.
1. INTRODUCTION
In the planning of public transportation system, an
adequate structure of bus routes has to be defined in
order to create conditions for the accomplishment of
daily displacements. As part of a general planning
process, Public Transportation by Bus (PTB) has to be
integrated to the system assuring the satisfaction of
user’s necessities in terms of commuting patterns.
Therefore, planners have to evaluate bus routes and
define those that provide an efficient covering of travel
demand and the economical sustainability of PTB.
Consequently, bus routes have been defined without a
comprehensive analysis on factors affecting both users
and bus transit operators, which can contribute to the
degradation of travel conditions. For instance, if a
definition of a bus route is inadequate, local residents
and users could be negatively affected in terms of
increasing
on
travel
time,
complementary
displacements (origin - bus stop - destination),
congestion, pollution, etc.
Especially in developing countries, the definition of
efficient bus routes consists of a fundamental activity
of transportation planning process. Generally, PTB is
responsible for the most part of daily trips, since it is
relatively flexible and low-cost for poor people, which
are highly dependent on this mode (Vasconcellos,
1996). Additionally, due to extremely dynamical
characteristics of urban environment affecting travel
demand, evaluations of suitable routes have to be
periodically conducted (Lindau and Rosado, 1992).
In this paper, we present the integration and application
of Geographical Information Systems (GIS) and
Analytical Hierarchical Process (AHP) for the
definition of bus routes in an urban area. Through out
GIS-AHP integration, it is attempted to follow previous
researches such as Dubois (1979) that developed
efforts towards a combined qualitative-quantitative
approach for bus routing problem. On the other hand,
we also are inspired Kwan’s work (2000) that explored
interactive geovizualization using 3D GIS. Finally, we
use the concepts of GIS-AHP integration, which were
enunciated and applied by Yamashita et al. (2000).
Despite of its acknowledged role in urban
transportation and significance in developing countries,
scientific initiatives towards bus route definition are
still incipient. Examining traditional methodologies,
they are based upon extensive activities of data
collection, without taking into consideration spatial
characteristics that affect PTB and its users.
Furthermore, planning agencies have notably suffered
Combining these experiences, GIS-AHP integration
intends to provide an instrument for simultaneously
incorporation and evaluation of spatial characteristics
without the necessity of huge activities on data
collection and taking into consideration expert’s
knowledge. This conception is only possible due to
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
exploration of GIS’s spatial capabilities and AHP’s
fundaments. The former contributes incorporating
spatial features, which are mainly obtained from
Remote Sensing (RS) data. On the other hand, through
the participation of planners, AHP is decisive to
evaluate and establish relationships between
quantitative and qualitative variables affecting bus
routing problem. Then, this integration is expected to
aid planners since it takes advantage of technological
and theoretical resources that are supposed to provide
better decisions and consequently contribute to the
achievement of an efficient PTB.
traffic conditions, etc. In addition, planners have to pay
attention on the determination of route’s typology
(radial, circular, tangent, diametrical, etc) that better
conforms to the kind of displacements to be attended.
Obviously, this analysis has to be conducted in
agreement with other factors affecting the route
definition.
Many scholars have recently dedicated their efforts
towards the solution of bus routing problem through
the incorporation of all these influencing factors.
According to Chua (1994), the most common methods
to solve bus route problems are manual analysis;
graphic methods, UTPS (Urban Transportation
Planning System) software, heuristics and optimization
models. Each of these methods concentrate on specific
aspects that are incorporated in their analysis namely
simplicity, data requirements, systematic organization,
and representation of urban environment, level of detail
on the final solution. For instance, manual analysis is
notably simple and requires few data but suffers on the
achievement of a systematic approach and for more
complex urban structures it is not capable to develop
an efficient representation of affecting factors. On the
other hand, optimization methods reach “optimal”
solutions, considering mathematical representation and
restrictions, but they consume enormous amount of
data. Additionally, optimization methods mostly
incorporate operational costs, regardless of remarkable
influence of other costs.
The description of GIS and AHP integration and its
application is divided into six sections. After this
introduction, a brief description of PTB and bus
routing problem is conducted. Third section describes
the general conception of the integration. Fourth
section presents the methodology for bus. In the fifth
section, a case study conducted in Sobradinho City
(Federal District, Brazil) is reported. Finally, sixth
section discusses GIS-AHP integration and its
efficiency to solve the bus routing problem taking into
consideration data requirements, obtained results and
future perspectives of implementation.
2. PTB AND BUS ROUTING PROBLEM
PTB is a part of urban transportation system, which is
devoted to daily and non-individual or mass
commuting. Basically, there are three main actors
involved in PTB` s activities that are users; transit
operators; and transportation authority. In order to
perform these activities, buses, personnel (operational,
planning, controlling), terminals, bus stops,
frequencies, headway, fares, and routes have to be
defined, managed and updated to guarantee the
effectiveness and equilibrium of PTB (EBTU, 1988)
(Soares, 1997).
Though all these methods are somehow effective, it is
clear none of them can be widely applied without
restrictions. These restrictions come from the
regrettable direction adopted on solving bus routing
problem, which sometimes tends to be excessive or
loosely on data incorporation (manual versus
optimization methods) and in other situations it is
observed a polarization in terms of quantitative and
qualitative evaluation (graphic versus heuristics).
In this context, bus routing problem is a fundamental
task to be conducted. Such a definition simultaneously
affects the urban development and the urban
transportation system in the sense that bus routes will
determine how and where users will be attended. Then,
the more PTB is efficient, the more it will attract and
generate development as well as it will contribute for
reducing individual transportation by car. Therefore,
bus route definition has to comprehend the evaluation
of a large variety of operational indexes (costs,
demand, etc) (NTU, 1997) (ANTP, 1997) and also it is
essential to analyze urban elements such as
employment and residential dispersion; environmental
impacts; and developmental perspectives (Cover,
1994).
Another important restriction is the poorly
representation of spatial characteristics of an urban
area, despite of great efforts on modelling these
characteristics. Unfortunately, for a long period, the
conception of transportation planning models has been
limited by computational capabilities. In modeling
activities, basic operations such as storage,
organization and mathematical manipulation were
often used to process a large amount of data.
Nevertheless, these operations did not contribute to
anything more but data manipulation since it was not
possible to consider spatial reality due to a very limited
database concept for these same processes (Hhakee and
Stromberg, 1993).
Specifically, the definition of a bus route concerns not
only the path by itself, but also spatial supply of
transportation demand, accessibility to terminals and
bus stops, operational costs, environmental impacts,
3. GIS AND AHP INTEGRATION
In the information age, Geoinformation means power
for decision-making activities. The analysis of complex
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
problems that can be spatially referenced requires the
use of Geoinformation as an evident step for obtaining
an improvement on the final decision. Geoinformation
about existing and future situations has a clear value
because it can be used to extend knowledge, enhance
our wisdom and reduce our uncertainty (Stillwell et al.,
1999).
In this paper, AHP is integrated to GIS in order to help
planners in decision activities involving multicriteria
analysis. AHP is used as a decision rule that
contributes to choose the best or the most preferred
alternatives. Developed by Satty (1980), AHP is based
on human being behavior to decide through the
comparison between “objects” until reaching a
decision. The comparison is related to the assignment
of “weights” according to the relative importance when
comparing to pre-established judgment criteria. Using
a quantitative scale (range between 1 and 9) all the
“objects” are compared leading to a prioritization and
consequent decision. This technique is particularly
interesting due to the establishment of a hierarchy for
decision and quite simple participation of decisionmakers. Both characteristics of AHP contribute for a
suitable integration to GIS, since the hierarchical
structure can be represented in spatial layers and
decision-makers’ judgments can be directly processed,
updated and transferred within GIS environment.
Contributing for reaching accurate and reliable
Geoinformation, GIS involves techniques and
capabilities for analyzing geographic events. These
techniques and capabilities include acquirement,
storage, and visualization functions (Herzog, 2000).
Furthermore, they also comprise advanced functions
that Malczewski (1999) divides into two categories:
statistical modelling and mathematical modelling.
These advanced functions, which have been recently
incorporated into GIS software, allow a better
exploration of spatial relations that were previously
ignored.
However, Geoinformation generated by GIS does not
lead to achieve a decision. Though the development of
analytical functions in GIS, Harris (1996) points out
that there is still a great limitation when planners
expertise (software information) has to be part of the
decision process, since in field (hardware information)
observations are not sufficient to evaluate a conflicting
preferences-problem. In addition, GIS is not enough
intelligent to find out the optimal or near-optimum
decision
GIS-AHP integration can be divided in four phases as
shown in Figure 1. Next, these phases are described in
detail following the concepts enunciated in Yamashita
et al. (2000):
 Phase I (Intelligence): definition of the hierarchical
decision structure concerning on the problem
(objective) definition, criteria identification and
alternative selection for evaluation. AHP establishes
theoretical fundamentals, while GIS provide
explanatory data to diagnose the problem;
 Phase II (Design 1): in this phase pairwise
comparisons are accomplished. Decision-makers
judge according to the fundamental scale defined by
Satty (1980);
 Phase III (Design 2): the priorities of each
alternative, previously identified in Phase I, are
calculated. Formally, be a set of m alternatives, the
final priority (overall score) Pf of fth alternative is
computed in the following way:
In order to overcome this situation, decision techniques
have been associated to GIS for ordering alternatives
and for choosing the most preferred alternative
(Corloni et al., 1999) and (Li et al., 1999) (Densham,
1991). These studies avoid an Operation Research
modelling, which may be implemented without
addressing human values and purposeful human
activity. In opposite, they concentrate on incorporating
expertise knowledge and decision rules to evaluate
spatial related problems exploring Geoinformation
generated by GIS systems (Malczewski 1999).
Pf   wk r fk
(1)
k
where wk is the vector of priorities associated with the
kth
element
of
the
criterion hierarchical
structure,
AHP
Phase I
Phase II
Phase III
Phase IV
Intelligence
Design 1
Design 2
Choice
GIS
Figure 1. phases of GIS-AHP Integration
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
 wk= 1 and rf k is the vector of priorities derived from
Phase I; and
 Phase IV (Choice): using thematic maps that are
generated based upon rf k and Pf , decision-makers can
reach the best decision by analyzing and comparing
the different influences of each criteria.
4.2 GIS database creation
Once these data are gather together, GIS database has
to be created in order to organize the different sources
of information namely numeric, maps, images, etc.
Hard-form (paper) data have to be digitized and then
these digital maps have to be georeferenced according
to local’s coordinate system. It is also important to
define a GIS database that is capable to manipulate
both vector and raster data.
Within this integration, the interaction between GIS
and the decision-group is one of the most significant
aspects. This interaction begins on various
examinations on GIS database for the definition of the
hierarchical decision structure. Next the judgements
are conducted, but if decision-makers need additional
information then spatial queries can be performed.
These queries can be repeated as much as needed and
complementary data can also be retrieved in order to
eliminate doubts. In the sequence, the priorities are
calculated and then they are transferred to GIS
database. Following, decision-makers discuss the
thematic maps in order to verify eventual problems or
inconsistencies. At this point, the decision-group can
either reach the final decision or restart the process or
make the improvement of the judgements.
4.3 Definition of the potential surface for bus routes
It is aimed to reach a 3D-visualization visualization of
a surface presenting regions (areas and points), where a
bus route would be highly expected to serve these
regions. Therefore, a comprehensive analysis has to be
conducted, mainly based upon GIS-AHP integration. In
this sense, there are five main activities to obtain the
potential surface that are:
4.3.1 Definition of the hierarchical decision
structure: considering GIS database previously
created, planners are supposed to detail the hierarchy
of decision (section 3 – Phase I). Assuming
“Evaluation of potential areas for bus routes” as the
objective, we suggest, based upon the description of
section 2, the following criteria: trip generation; trip
attraction; traffic conditions; environmental impacts;
land use restrictions; and routes’ competition. Subcriteria can be defined according to the level of the
evaluation and the existence of data. Alternatives are
assumed to be all the raster cells that are involved in
the study area.
4. METHODOLOGY FOR BUS ROUTE
DEFINITION
The main concept of this methodology is to provide,
along the decision process, a flexible instrument that
allows redefinition both on spatial characteristics and
expertise’s knowledge. Therefore, the methodology
does not concentrate on a rigid mathematical
formulation that would limit the perspective of
evaluation. In opposition, it is established a general
framework that suggests procedures and spatial
analysis, which will produce sensitive Geoinformation
for decision-makers. Operationally, the methodology
starts on preliminary steps such as diagnosis and GIS
database creation, which are the basis for obtaining
areas (potential surface) that a bus route must attend.
Next, it is performed the process of tracing potential
routes that are latter evaluated based upon a final
potentiality index.
4.3.2 Spatial Analysis for Geoinformation
obtainment using vector data structure: in this
activity, it is essential to generate all necessary
Geoinformation on the criteria, which were defined in
section 4.3.1. Making use of vector data of GIS
database, thematic maps are created in order to be the
basis for establishing judgments on next section
(4.3.3). Obviously, there will be different levels of
aggregation on vector data such as traffic zones, land
use patterns, link capacity, etc. These incompatibilities
will be eliminated during the creation of raster data
structure and its thematic maps for further overlay
operations in GIS (section 4.3.5).
Following, the methodological framework is described
in detail.
4.1 Diagnosis/Inventory
4.3.3 Judgement of Criteria: as defined in section 3
(Phase II), pairwise comparison is conducted for all
criteria considering the thematic maps, which were
created by the previous activity (section 4.3.2.),
generating wk and rf k. For some criteria, judgments
will occur based upon the calculation of indexes such
as traffic congestion, while for others more direct
interference from decision-makers is expected. After
accomplishing judgments, it is suggested their reevaluation based upon GIS database in order to
eliminate inconsistencies. Finally, the judgments are
assigned to new thematic maps.
In this methodological step, data are collected from all
sources. Basically, data on the traffic system, public
transportation system, PTB, travel demand patterns and
land use comprehending previous studies will
contribute for the execution of this methodology.
However, there is no need to conduct intensive data
collection, i.e., only basic indicators can be used if
expert’s (local planner) knowledge is associated
through GIS-AHP integration.
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.

4.3.4 Spatial Analysis for Geoinformation
obtainment using raster data structure: initially,
thematic maps generated in the last activity (section
4.3.3) have to be converted into raster data format.
Then, for each criteria and its last level of AHP’s
hierarchy, a raster grid is obtained, which contains the
vector with rf k priorities for each f alternative (raster
cell or pixel) related to criteria k. Next, a connectivity
analysis has to be conducted in order to obtain a
coverage index of priorities (Cfk) for the alternative f
and criteria sub-criteria k, that is expressed by the
following equation:
C kf 
 m

 r n 1
jk
 j 1 



0


Xz 
, if (d fj   )
(2)
, otherwise
C kf
, if ( f  t )
0
, otherwise
k 1
0
0
, otherwise
 
E z  X z L z 1
(5)
(6)
5. CASE STUDY
The case study conducted in Sobradinho City. This city
is located 24 Km far from Brasilia, Brazil. Its
population is approximately 101.090 habitants
according to IPDF (1995). It occupies an area of
569.37 Km2, which involves urban and rural areas.
Despite of notorious dependence on Brasilia’s
governmental activities, there have been a growing
number of commercial and industrial businesses which
is expressed by necessity on defining a Circular
(typology) bus route to attend daily displacements. In
this sense, the methodological framework of section 4
is applied to define a Circular bus route in Sobradinho
City.
where t stands for the identification code of a traffic
system-cell.
4.3.5 Overlay of Coverage Indexes: it consists on the
creation of a final thematic map, which computes Tfk
and wk by applying the following equation:
Vf 
, if ( f  S z )
f 1
4.5 Final evaluation: through the application of
equation 6, decision-makers will have indexes that
express final priority Ez for each route z considering the
extension of each route (Lz)
(3)
 T fk wk
V f
where Xz is the sum of all final priorities (Vf) that are
part of route z, which is described by the network Sz;
 (6) If new potential routes are intended to be
evaluated, increase z (z=z+1) and restart the
tracing process from (1).
where n[] is the number of raster cells within the
project-coverage distance , that has to be defined in
advance by decision-makers, and dfj is the Euclidean
distance from the fth cell to jth cell. The projectcoverage distance is important in the sense that only
those raster cells within a pre-established radius ()
will be considering. This procedure allows the
incorporation of neighborhood relationships affecting
any alternative (f), which is fundamental to express
spatial conditions of the urban area. Once the coverage
indexes of priorities for all cells and sub-criteria are
obtained, it is necessary to confine the analysis to those
cells related to the traffic system (streets, roads,
avenues, etc). The coverage index of traffic system’s
cells (Tfk ) is obtained by applying the following
equation:
T fk 
(3) Try to link these intersections in order to trace
a route z;
(4) If route z is not obtained, repeat (2) until the
complete route is established;
(5) Compute the extension Lz and Vf for all raster
cells of route z by applying the following equation:
5.1 Diagnosis/Inventory:
Four main sources of data were employed on
diagnosing area of the case study. From the
Development Agency of the Federal District
(CODEPLAN) digital database (scale 1:10.000, UTM)
containing the traffic system, constructions,
topography (elevation), hydrography, toponymy and
aerial photographs was obtained. Information on the
classification of Land Use as well as its restrictions for
occupation and traffic system conditions was collected
from the Planning Institute of the Federal District
(IPDF). Transportation data was granted by the
Metropolitan Department of Urban Transportation
(DMTU), which consisted on the existent bus routes,
facilities (terminals and bus stops) and schedules.
Finally, it was used all the background of the work of
Taco et. al. (2000) on travel demand modelling for
Sobradinho City.
, if ( f  t )
(4)
, otherwise
where Vf is the final priority of the fth raster cell.
4.4 Tracing of potential routes: based upon the final
thematic map displaying Vf for all raster cells, planners
have to trace possible routes to be evaluated. Tracing
process is suggested to consider the following
directives:
 (1) Find a raster cell, at those intersections of the
traffic system, with highest Vf ;
 (2) Repeat (1) for the next highest Vf raster cell;
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
were computed and generated the thematic map as
shown in Figure 3b. In Figure 3, it is noticed the
influence of trip generation and attraction criteria on
the final priority (Vf), which were judged as the most
important for bus routing, i.e., 35% of priority for each.
5.2 GIS database creation
Based upon the digital map database from
CODEPLAN (1991), a GIS database was constructed
using MGE software (Intergraph, 1994). After the
georeferencing process, all data collected from the
other sources were digitized into the GIS database.
5.4 Tracing of potential routes:
Following the steps described in section 4.4 for tracing,
three potential routes were identified. Additionally, we
also incorporated an under operation-route (z=4) in
order to establish a basis for comparison. Figure 4
shows the routes selected for the final evaluation.
Potential route 1 (z=1) presents an extensive covering
of the highest priorities, which are not restricted to
central area of the city. Potential route 2 (z=2) is almost
similar to potential route 2, expect that it is longer
intending to cover more points of high priority. Finally,
potential route 3 (z=3) is more concentrated on central
displacements, expressing the necessity to supply
transportation services in the central area.
5.3 Definition of the potential surface for bus routes
A group of transportation planners was invited to
participate on the conduction of the following activities
of the methodology. Following, these activities are
described:
5.3.1 Definition of the hierarchical decision
structure: the set of criteria previously selected in
section 4.3.1 (trip generation; trip attraction; traffic
conditions; environmental impacts; land use
restrictions; and routes’ competition) were considered
insufficient in terms sub-criteria for evaluating the bus
routing problem. Therefore, two more levels were
incorporated into the hierarchical structure as shown in
Figure 2.
5.3.2 Spatial Analysis for Geoinformation
obtainment using vector data structure: Initially,
thematic maps were created from GIS database for
Sobradinho City. It can be noticed that it is a typical
residential urban area with a very extensive traffic
system. There are not many problems in terms of
congestion, which are concentrated on some
commercial areas. Regarding PTB, circular bus route
covers 18,7 Km, which passes through about 55 bus
stops along the traffic system. Next, based on the
results from Taco et. al. (2000), trip generation and
attraction indexes for each of the land use patterns
were used to create thematic maps. Analyzing these
maps, it can be perceived that trip generation presents,
for all most all the study area, very small variations
except for multiple residential areas (apartment
buildings);
5.3.3 Judgement of Criteria: pairwise comparison
was conducted for all criteria and sub-criteria based on
a discussion among decision group, leading to the
obtainment of wk and rf k vectors;
5.3.4 Spatial Analysis for Geoinformation
obtainment using raster data structure: For each
criteria of the hierarchical decision structure (Figure 2),
the conversion of thematic maps on vector data format
was conducted using MGE’s Grid Analyst Module.
Then, priorities for all f alternatives (raster cells) were
obtained and subsequently they were processed
following equation 2, that computed coverage indexes
(Cfk) considering a project-coverage distance () being
500 meters. Next, based on Cfk values, coverage
indexes of traffic system’s cells (Tfk ) was calculated by
applying equation 3, which were used to created the
thematic maps as displayed in Figure 3a.
5.3.5 Overlay of Coverage Indexes: applying
equation 4 and making use of MGE’s tool for overlay
analysis, the final priorities of traffic system’ cells (Vf)
5.5 Final evaluation:
For each potential route previously identified, equation
6 was applied in order to obtain the final priority (Ez).
Table 1 shows Ez results as well as the extension of
each route and its respective Xz value.
Route
z=1
z=2
z=3
z=4
Xz
0,50
0,58
0,36
0,46
Lz
16,30
18,50
11,00
18,70
Ez
0,0307
0,0313
0,0327
0,0246
Table 1. Final priority (Ez ) for each route z
It is clear that potential route 3’s extension is decisive
for its highest final priority, since it has the shortest
extension. This shows that the selected route has also
to be efficient in terms of extension. For instance,
potential routes 1 and 2 present high priorities but they
are not efficient in the sense that they would have to
have much higher priorities due to the excessive
extension. In fact, the evaluation shows that routes 1
and 2 cover a vast area of the city, which leads to a
very high priority (Xz) but eventually they do not pass
through raster cells (f) with high priorities by
themselves. In opposition, route 3 is mostly
concentrated in areas with high potential in terms of
trip generation and attraction. Therefore, route 3 just
gather together raster cells will high priorities.
It is also important to perceive the performance of
route 4. As route 1 and 2, route 4’s extension can be
considered extremely long, too. Despite the fact that
bus users do not have to walk so much from their
origin/destination to bus stop since route 4 covers the
most part of Sobradinho City, it can be pondered that it
is not an efficient for attending the whole population.
Therefore, it would be interesting to have this route
changed or re-evaluated in its operational conception.
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
Potential for bus route
Objective
Level I
Trip
Generation
Trip
Attraction
Route’s
Competition
Environmental
impacts
Level II
School
s
Land use
Use
Patterns
Commerce
Capacity
Service Industry
C1 C2 C3 C4
S1 S2 S3
Traffic
Conditions
Land use
restrictions
Congestion
>500m <500m
Level III
Residential
Central
Schools
Hospitals
Alternatives
Figure 2. Hierarchical decision structure for bus routing in Sobradinho City
N
Trip Generation
Route’s competition
Trip Attraction
Environmental impacts
Traffic conditions
Land use restriction
(a)
(b)
Figure 3. Coverage index of traffic system’s cells: (a) Priority per criteria (Tfk); (b ) Final Priority (Ez )
N
z=1
z=3
z=2
z=4
(under
operation)
Figure 4. Potential bus routes traced for evaluation
9
Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.
Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.
6. CONCLUSION
EBTU 1988. Gerência do Sistema de Transporte Público de
Passageiros – STTP, Módulos de Treinamento, Planejamento
da Operação (In Portuguese).
Decision-making, especially for transportation
planners, is not only hard task to be conduct due to the
complexity of involved factors, but mainly because
there are great restrictions on data survey and
modelling. Usually, there are so many requirements on
data and so complex modelling (a further calibration)
that planners avoid these time and money consuming
activities, which are substituted by “let’s try” approach.
Herzog, M. T. 2000. GIS technology and Implementation; In
Urban Planning and Development, eds. Easa, S. and Chan,
Y., pp 9-31, ASCE, USA
IPDF 1995. Plano Diretor Local de Sobradinho, vol II,
Memória, Brasília.
Intergraph 1994. MGE – Modular GIS Environment – Grid
Analyst.
This paper tried to contribute for changing this
situation for bus routing problem. We proposed a
methodology that made use of GIS-AHP integration in
order to conduct a comprehensive but simultaneously
not data collection-dependent solution. In this sense,
we take advantage of various resources of GIS and its
capability to fully represent urban reality through the
exploration of a combined vector-raster database.
Khakee, A. and Stromberg, K.; 1993. Applying futures
studies and strategic choice approach in urban planning;
Journal of Operational Research Society, vol. 44, n. 3, pp.
213-224.
Kwan, M. 2000. Interactive geovisualization of activitytravel patterns using three-dimensional geographical
information systems: a methodological exploration with a
large data set, Transportation Research C-8, pp. 185-203.
The methodology was successfully applied to a case
study in a developing country. Results, not only
numerical but mostly from decision-makers
participation, show that the methodology is effective
on the definition of a efficient bus route. We foresee
some improvements on the automation of tracing bus
routes based upon the potential surface.
Li, X.; Wang, W.; Li, F.; Deng, X..; GIS based map overlay
method for comprehensive assesment of road environmental
impact; Transportation Research-D, vol.4, pp.147-158; 1999.
Lindau, L.A.; Rosado, A. B. (1992) Os Transportes Públicos
Urbanos e a Qualidade Total, Revista dos Transportes, ano
14, 2 trimestre 1992, ANTP, n 55.
ACKNOWLEDGMENTS
We would like to express our gratitude to Mr. Pastor
Taco that initiated and massively contributed with his
comments along the conduction of this research. We
also wish to thank CNPq (Brazilian Scientific and
Technologic Development) for the grants that
supported this research.
Longley, P. A., Goodchild, M. F., Maguire, D. J., Rhind, D.
W. 1999. Geographical Information Systems – Principles
and Technical issues, vol. 1 and 2, John Willey&Sons, New
York.
Malczewski, J.; 1999. GIS and Multicriteria Decision
Analysis, John Willey&Sons, New York.
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Dantas, A., Silveira, L., Yamashita, Y. (2001) All rights are reserved – Todos os direitos estao reservados.