Data-based mobility analyses for autonomous, on-demand public transport offerings

Autonomous driving is currently one of the biggest topics in the mobility sector, and public transport operators are also devoting more attention to the issue. The first testing sites for integrating autonomous vehicle technology with public transport services are currently being created such as the DB site at the Bavarian town of Bad Birnbach. The EVA project is underway in Karlsruhe. In German, the acronym EVA stands for “electric, interconnected and autonomous”, and the vehicles in question are electric mini-buses.

The project focuses on developing and analysing an autonomous on-demand ecosystem for public transport. Alongside vehicle technology and infrastructure, one of the main tasks is to perform a data- and software-driven mobility analysis for identifying a suitable operating area that connects to Karlsruhe’s existing public transport network in an efficient way. For the first time, such a procedure was developed and implemented for a testing site in Germany.

The method makes use of a multilevel scoring system based on comprehensive parameter sets. These include not just transport- and infrastructure-related variables, but also parameters based on the technical features of autonomous shuttle systems. The result is an operating area that is optimally integrated into the existing public transport network and that also factors in the current limitations of autonomous vehicles to provide an optimum transport service.

This whitepaper looks at the basic procedure for a mobility analysis of this type, at special features within the context of on-demand systems, and at the results of the analysis in Karlsruhe.

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