Abstract
Selection of urban bypass highway alternatives involves the consideration of competing and conflicting criteria and factors, which require multicriteria decision analysis. Analytic hierarchy process (AHP) is one of the most commonly used multicriteria decision making (MCDM) methods that can integrate personal preferences in performing spatial analyses on the physical and nonphysical parameters. In this paper, the traditional AHP is modified to fuzzy AHP for the determination of the optimal bypass route for Eldoret town in Kenya. The fuzzy AHP is proposed in order to take care of the vagueness type uncertainty encountered in alternative bypass location determination. In the implementation, both engineering and environmental factors comprising of physical and socioeconomic objectives were considered at different levels of decision hierarchy. The results showed that the physical objectives (elevation, slope, soils, geology, and drainage networks) and socioeconomic objectives (land-use and road networks) contributed the same weight of 0.5 towards the bypass location prioritization process. At the subcriteria evaluation level, land-use and existing road networks contributed the highest significance of 47.3% amongst the seven decision factors. Integrated with GIS-based least cost path (LCP) analysis, the fuzzy AHP results produced the most desirable and optimal route alignment, as compared to the AHP only prioritization approach.
Highlights
As dependence on urban rail and road networks increases, availability and reliability have become critical transportation issues, with operators being forced to modernize and/or increase the distribution of their networks
Results show that applying fuzzy Analytic hierarchy process (AHP) in multicriteria decision analysis is suitable in determining new route locations
Testing and validation of the model are limited to the experiences of the domain experts, and the values for pairwise comparisons in fuzzy AHP depend on the knowledge of the decision makers
Summary
As dependence on urban rail and road networks increases, availability and reliability have become critical transportation issues, with operators being forced to modernize and/or increase the distribution of their networks This requires a lot of time and money to be invested in configuring and planning transport networks, with dimensioning and cost optimization playing key roles. Problems in the field of transportation planning and traffic control are generally ill-conditioned, that is, geospatially ambiguous and ontologically and epistemically vague in terms of their geographic entity, spatial, and nonspatial representations. This implies that most of these and the associated parameters are characterized by subjectivity, uncertainty, ambiguity, and imprecision. To solve a complex problem, the system needs to be divided into subcomponents at various hierarchical levels (based on their individual complexities) in order to understand the system clearly and describe the relationships with lesser ambiguity
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