A Multi-day Needs-based Modeling Approach for Activity and Travel Demand Analysis
This paper proposes a multi-day needs-based model for activity and travel demand analysis. The model captures the multi-day dynamics in activity generation, which enables the modeling of activities with increased flexibility in time and space (e.g., e-commerce and remote working). As an enhancement...
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Zusammenfassung: | This paper proposes a multi-day needs-based model for activity and travel
demand analysis. The model captures the multi-day dynamics in activity
generation, which enables the modeling of activities with increased flexibility
in time and space (e.g., e-commerce and remote working). As an enhancement to
activity-based models, the proposed model captures the underlying
decision-making process of activity generation by accounting for psychological
needs as the drivers of activities. The level of need satisfaction is modeled
as a psychological inventory, whose utility is optimized via decisions on
activity participation, location, and duration. The utility includes both the
benefit in the inventory gained and the cost in time, monetary expense as well
as maintenance of safety stock. The model includes two sub-models, a
Deterministic Model that optimizes the utility of the inventory, and an
Empirical Model that accounts for heterogeneity and stochasticity. Numerical
experiments are conducted to demonstrate model scalability. A maximum
likelihood estimator is proposed, the properties of the log-likelihood function
are examined and the recovery of true parameters is tested. This research
contributes to the literature on transportation demand models in the following
three aspects. First, it is arguably better grounded in psychological theory
than traditional models and allows the generation of activity patterns to be
policy-sensitive (while avoiding the need for ad hoc utility definitions).
Second, it contributes to the development of needs-based models with a
non-myopic approach to model multi-day activity patterns. Third, it proposes a
tractable model formulation via problem reformulation and computational
enhancements, which allows for maximum likelihood parameter estimation. |
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DOI: | 10.48550/arxiv.2312.15373 |