Proceedings of the Symposium on ASIA GIS (2001), Tokyo, Japan.

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 users 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.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).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 of resource limitations to conduct evaluations such as bus route definition.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.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 Kwans 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).

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 experts knowledge. This conception is only possible due to exploration of GISs spatial capabilities and AHPs 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.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 PROBLEMPTB 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).

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).

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, traffic conditions, etc. In addition, planners have to pay attention on the determination of routes 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.

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).

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).3. GIS AND AHP INTEGRATION

In the information age, Geoinformation means power for decision-making activities. The analysis of complex 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).

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

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).

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 decision-makers. 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.

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:

where wk is the vector of priorities associated with the kth element of the criterion hierarchical structure,

( 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.

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. 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 expertises 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.

Following, the methodological framework is described in detail. 4.1 Diagnosis/InventoryIn 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 experts (local planner) knowledge is associated through GIS-AHP integration.

4.2 GIS database creationOnce 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 locals coordinate system. It is also important to define a GIS database that is capable to manipulate both vector and raster data.

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. Sub-criteria 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.

4.3.2 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).

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 re-evaluation based upon GIS database in order to eliminate inconsistencies. Finally, the judgments are assigned to new thematic maps.

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 AHPs 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:

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 project-coverage 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 systems cells (Tfk ) is obtained by applying the following equation:

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:

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;

(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:

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).

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)

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 Brasilias 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.

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.

5.2 GIS database creationBased 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.

5.3 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 MGEs 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 systems 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 MGEs tool for overlay analysis, the final priorities of traffic system cells (Vf) 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.

5.4 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. 5.5 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.

RouteXzLzEz

z=10,5016,300,0307

z=20,5818,500,0313

z=30,3611,000,0327

z=40,4618,700,0246

Table 1. Final priority (Ez ) for each route zIt is clear that potential route 3s 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 4s 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.

6. CONCLUSION 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 lets try approach.

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.

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.

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.

REFERENCES

ANTP 1997. Transporte Humano: Cidades com Qualidade de Vida, So Paulo (In Portuguese).

Chua, T. A. 1984. The Planning of Urban Bus Routes and Frequencies: a Survey, Transportation, 12. Elsevier Science Publishers B.V., Amsterdam. CODEPLAN 1991. Pesquisa Domiciliar de Transporte, Braslia (In Portuguese).

Corloni, A.; Laniado, E.; Muratori, S.; 1999. Decision Support System for environmental impact assessment of transport infrastructures; Transportation Research-D, vol.4, pp.1-11.

Cover, R. K. 1994. Emerging Information Systems Technology for Transportation. TR News, n 171, pp. 2-6.Davies, M. A.P, 1994. A multicretria decision model application for managing group decisions; Journal of Operational Research Society, vol. 45, n. 1, pp 47-58.

Densham, P. J., 1991. Spatial Decision Support Systems; In Maguire, D. J, Goodchild, M. F., Rhind, D. W., (eds), Geographical Information Systems Principles, vol. 1, pp. 403-412.

EBTU 1988. Gerncia do Sistema de Transporte Pblico de Passageiros STTP, Mdulos de Treinamento, Planejamento da Operao (In Portuguese).

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, Memria, Braslia.

Intergraph 1994. MGE Modular GIS Environment Grid Analyst.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 activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set, Transportation Research C-8, pp. 185-203.

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 Pblicos Urbanos e a Qualidade Total, Revista dos Transportes, ano 14, 2( trimestre 1992, ANTP, n( 55.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.NTU 1998. Transporte Pblico Urbano: Crise e Oportunidades, verso preliminar, Braslia (In Portuguese).

Satty, T. L.: (1980) The analytic hierarchy process. New York: McGraw-Hill. Soares, S. 1997. O Papel do Estado no Planejamento e na Execuo dos Investimentos. in Revista dos Transportes, 19, ANTP, n( 76 (In Portugues)Stillwell, J.; Geertman, S.; Openshaw, S.; 1999. Geographical Information and Planning. Springer, Berlin.

Taco, P.W.G.; Yamashita, Y.; Souza, N.M. & Dantas, A.S. 2000. Trip Generation Model: A New Conception Using Remote Sensing and Geographic Information Systems. Photogrammetrie Fernerkundung Geoinformation, Germany, vol. 2, pp- 119-131Vasconcelos, E. A. 1996. Transporte Urbano nos Pases em Desenvolvimento, Editoras Unidas So Paulo.Yamashita, Y.; Dantas, A.S., Taco, P.W.G., Yamamoto, K. 1999. Geographic Information System and Remote Sensing in Bicycle Planning. Journal of the Japanese Civil Engineering Association, no. 16, pp. 327-332.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.

AHP

GIS

Phase I

Intelligence

Phase II

Design 1

Phase III

Design 2

Phase IV

Choice

Figure 1. phases of GIS-AHP Integration

EMBED Equation.3

(1)

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0 , otherwise

EMBED Equation.3

(2)

EMBED Equation.3

0 , otherwise

EMBED Equation.3

(3)

EMBED Equation.3

EMBED Equation.3

0 , otherwise

EMBED Equation.3

(4)

EMBED Equation.3

EMBED Equation.3

(5)

EMBED Equation.3

0 , otherwise

EMBED Equation.3

EMBED Equation.3

(6)

Residential

Central

Schools

Hospitals

Capacity

Congestion

Land use Use

Patterns

Schools

Commerce

Service

Industry

C1

C2

C3

C4

S1

S2

S3

Potential for bus route

Trip Generation

Trip Attraction

Routes

Competition

Environmental

impacts

Traffic Conditions

Land use

restrictions

>500m