Reflections on the Paradigm Shift in Travel Demand Models in the Big Data Era
Big data has brought revolutionary changes to the developing basis of travel demand models.Re-flections on the paradigm shift is not only an adaptation to the changes in data conditions in the new era,but also a necessary requirement for improving the accuracy of travel demand models.This paper summa-rizes and contemplates four paradigm shifts in travel demand models based on changes in foundational da-ta conditions.These shifts include:enhancing the explanatory power of models on travel behavior by tran-sitioning from mathematical optimization to causal inference;clarifying the physical meaning of models by moving from proportional factors to probabilistic sampling;achieving inheritance and iterative evolu-tion of current demand by transitioning from holistic reconstruction to incremental models;and improving the precision of travel demand models by moving from finite constraint convergence to prior empirical jus-tification.The paper points out that,for both practical application and scientific research in transportation governance,model accuracy is the sole and highest standard for evaluating model quality.Emphasizing paradigm shifts is not meant to negate previous technical approaches in travel demand models but to inno-vate and develop through inheritance,thus enhancing the capacity of models to simulate and predict the re-al world.
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