Abstract
Travel demand plays an essential role in strategic transport planning. Generally, experts use either discrete methods, e.g. discrete choice models or simulation, e.g. activity-based models to estimate demand in transportation. This paper offers a different solution; instead of using the traditional approach, the demand is considered as a Multi Criteria Decision Making (MCDM) problem and surveying the citizens’ preferences provides the results for decision support. Public transport demand depends on two main issues, quality and price of the transportation. In a hierarchical model, both issues have been integrated and the well-proven Analytic Hierarchical Process (AHP) method has been applied in the current research. Further, fuzzyfication of the scores have also been conducted because of the citizen evaluator pattern. The fuzzy-AHP (FAHP) model has been tested in a real-world situation with the case study of Amman (Jordan).
Highlights
IntroductionThe four-step models integrate four phases of travel demand determination: (1) trip generation, (2) trip distribution, (3) modal split and (4) trip assignment (McNally 2007)
Travel demand analysis and forecasting is a major issue in the transportation discipline and this topic continues to receive extra attention both in academic research and applied transportation planning (Rasouli, Timmermans 2012).There are three main directions in travel demand modelling: (1) the four-step models, (2) discrete choice models and (3) activity-based models.The four-step models integrate four phases of travel demand determination: (1) trip generation, (2) trip distribution, (3) modal split and (4) trip assignment (McNally 2007)
While John et al (2014) used TOPSIS and FAHP to select an appropriate model for evaluation of performance efficiency in seaports, the outcome of this study showed that increasing reliability is the best investment strategy
Summary
The four-step models integrate four phases of travel demand determination: (1) trip generation, (2) trip distribution, (3) modal split and (4) trip assignment (McNally 2007). The unit of these models is a trip conducted by individuals and these trips are aggregated for the ultimate determination of the demand. Each single travel choice is considered as a utility maximization behaviour (Bhat 2018) in which the utility function has a deterministic and a random component (Hasnine, Habib 2018).
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