Abstract
Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be always evaluated by the AHP. The decision makers prefer subjective judgments to referring to the AHP evaluation results. This circumstance not only decreased the AHP advantages, but also raised the risk of the policies. A method to consider both objective measures and subjective judgments of project attributes can help reduce this problem. Accordingly, combining the AHP with the artificial neural network (ANN) methodologies would theoretically be a proper solution to bring a ranking predication model by creating the obscure relations between objective measures by the AHP and subjective judgments. However, the inconsistency between the AHP evaluation and subjective judgments resulted in the inferior soundness of the AHP/ANN ranking forecast model. To overcome this problem, this study proposes the data preprocessing method (DPM) to calculate the correlation coefficient value using the subjective and objective ranking incidence matrixes; according to the correlation coefficient value, the consistency between the AHP rankings and subjective judgments of railroad projects can be evaluated and improved, so that the forecast accuracy of the AHP/ANN ranking forecast model can also be enhanced. Based on this concept, a practical railroad project ranking experience derived from the Institute of Transportation of Taiwan is illustrated in this paper to reveal the feasibility of applying the DPM to the AHP/ANN ranking prediction model.
Highlights
The limited yearly fiscal budget usually constrains the implementation of infrastructure construction projects, especially for the developing countries
To prioritize projects considering both objective and subjective measurements simultaneously, this study combines Analytic Hierarchy Process (AHP) with the proposed data preprocessing method (DPM) and artificial neural network (ANN) learning algorithm to develop the AHP/DPM/ANN railroad project ranking mechanism, which is an effective approach to overcome the shortcomings of using AHP and ANN alone
The proposed data preprocessing method plays the critical role for this integrative application since the data consistency between objective and subjective rankings of railroad projects can be validated well to be the training dataset of the ANN
Summary
The limited yearly fiscal budget usually constrains the implementation of infrastructure construction projects, especially for the developing countries. To determine the project budget allocation, prioritizing alternative projects was the critical approach to provide advanced decision-making information To this end, the multi-criteria decision making tools (MCDM), Analytic Hierarchy Process (AHP) is the public methodology to prioritize transportation infrastructure projects (Saaty 1980; Cheng et al 2002) in Taiwan. For the AHP/ANN model, lower consistency between the input layer (AHP evaluation data) and the output layer (subjective judgment orders) of the historical training data determines the worse prediction accuracy due to the inconsistencies between subjective and objective cognitive logic. By controlling the data consistency, the AHP/DPM/ANN prioritizing process provides a practical reference model for determining the budget allocation priorities of railroad projects under the considerations to both objective and subjective measurements. The AHP/DPM/ANN process is applied to rank the railroad construction projects budgeted in 2002 to demonstrate the feasibility and applicability of this model
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