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The objective of this study is to investigate the role of spatial characteristics on car ownership and availability respectively in agent-based travel demand models and its affection on the model’s results. Based on Open Data we generate an automated workflow to evaluate spatial characteristics such as land use, points of interest, private vehicle network, and public transport quality. We estimate two multinomial logit models for car ownership: one considering socio-demographic characteristics only, and one considering both, socio-demographic and spatial characteristics. The models’ results are spatially evaluated and compared with statistical data. Moreover, we analyze the sensitivity of public transport quality measures on car ownership. The application of both car ownership models in the agent-based travel demand model mobiTopp to the city of Hamburg, Germany shows that integrating spatial characteristics significantly improves the model’s goodness of fit as well as its overall prediction power. Moreover, the application demonstrates that a detailed consideration of spatial characteristics in car ownership models contributes to a more realistic spatial distribution of cars. Furthermore, the study shows that i.e., public transport quality measures in car ownership models are relevant to reflect secondary mode choice effects (i.e., different mode choice sets due to change in car stock) in travel demand models.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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