# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
###### Abstract  
Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks. bike sharing; demand generation; mobility on demand; autonomous bike; future mobility; cargo bike +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/0](https://creativecommons.org/licenses/by/4.0/0)]([https://creativecommons.org/licenses/by/4.0/0](https://creativecommons.org/licenses/by/4.0/0))). However, as pointed out by previous studies (e.g., Refs. [8; 9; 10; 11]), bike sharing systems commonly face spatial and temporal fluctuations in bike rentals, which lead to uneven distribution of bicycles among stations and thus to unavailability and customer dissatisfaction. To prevent supply imbalance, system operators have to deploy costly and unsustainable redistribution operations, which are usually performed by trucks or trailers [8]. Further, although bike sharing systems are highly integrated into public transport, they do not provide door-to-door mobility, since users still need to walk to a certain bike sharing station and often cannot return the bike at their final destination [12]. [PERSON] et al. [13] provided an approach to address the associated ecological and economic disadvantages by introducing a framework for the utilization of shared autonomous bicycles in the context of mobility as a service concept. Within the AuRa (Autonomes Rad, Eng. autonomous bike) research project, we are currently further enhancing this approach as we aim to develop an on-demand, shared-use, self-driving cargo bikes service (OSABS). Through leveraging autonomous driving functions, the system enables automated demand-responsive rebalancing and thus reduces rebalancing effort and service unavailability. In addition, the system enables door-to-door mobility through reserved on-demand provision, with the possibility of transporting cargo and economically viable applications in less dense urban areas [13].