Urban Logic
[06]
Transport Simulation
[Desciption]
A travel demand model using Analytical Hierarchical Processing to estimate origin-destination flow matrices based on surveyed traveller preferences across multiple trip criteria — combining personal judgement with measurable destination attributes.
[Model Objective]
A travel demand model that uses Analytical Hierarchical Processing (AHP) to estimate origin-destination flow matrices based on how travellers actually weigh competing trip criteria. Rather than relying purely on distance or population mass as in conventional gravity models, the model captures the relative importance of multiple destination attributes — proximity, retail density, safety, and others — as perceived by actual trip makers through pairwise comparison surveys. The result is a demand-weighted OD matrix that reflects both measurable conditions and traveller preferences simultaneously. The approach is generic and can be configured for any trip type and any set of destination criteria.
Research Prototype
2017
AHP Origin-Destination Flow Model
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The model produces a normalised attractiveness score per destination from each origin and aggregates these into a complete OD flow matrix. Applied to a three-district test case, it correctly reflects the dominant role of proximity in travel decisions while capturing how improvements in safety or retail density meaningfully shift flow patterns — demonstrating sensitivity to both objective data and surveyed preferences. Once calibrated, sensitivity analysis can reveal which destination attributes have the most leverage over travel demand redistribution.
[Model Components]
Survey-derived pairwise comparison matrix for trip criteria weighting · Interval-band categorisation of destination attributes (distance, activity density, safety, etc.) · Idealised rating vector calculator per attribute band · Destination attractiveness aggregation engine · Full OD matrix generator iterating across all origin-destination pairs
[Model Results]
The model produces a normalised attractiveness score per destination from each origin and aggregates these into a complete OD flow matrix. Applied to a three-district test case, it correctly reflects the dominant role of proximity in travel decisions while capturing how improvements in safety or retail density meaningfully shift flow patterns — demonstrating sensitivity to both objective data and surveyed preferences. Once calibrated, sensitivity analysis can reveal which destination attributes have the most leverage over travel demand redistribution.
[Model Components]
Analytical Hierarchical Processing (AHP) · Pairwise Comparison · Survey-Based Preference Weighting · Idealised Rating Vectors · Interval Scale Categorisation · Origin-Destination Matrix Construction
[Tags]
["AHP", "Travel Demand", "Origin-Destination", "Decision Making", "Urban Mobility"]