Abstract
Transport systems are subjects to a wide range of interactions between different actors occurring on different time scales, so their holistic assessment still remains a major challenge. Particularly the consideration of disruptive transport means such as modular vehicles or drones can make the integrated assessment not applicable, because little or no knowledge about their impacts are often available. In framework of this study we propose a Continuum Approximation (CA) methodology to explore and optimize different transportation system configurations for urban modular vehicles supposing integrated deployment on both passenger and parcel markets. Based on the classical approach from [1] we aim to derive transport system-relevant relationships such as average tour length per customer, number of stops per tour, headways and the number of vehicles in the delivery area with respect to continuous demand density. Furthermore, we validate this approach by comparing test data to discrete agent-based simulation and applied the model in context of the modeled scenario. In fact, our study reveals that the proposed CA model can be an effective tool to approximate the basic transport relationships especially in regions with small customer density and uniform demand. We also show in context of a scenario with modular autonomous vehicles, how the number for potential depots can be approximated in a hypothetical delivery region when only very rough system parameters are known.
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