# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
###### Abstract
As outlined in [14], we therefore believe that OSABS, by offering efficient, accessible, and eco-friendly transportation alternatives, not only addresses the shortcomings of conventional bike sharing systems but also significantly contributes to creating more livable urban environments. Through a reduction in motorized traffic, OSABS leads to a multitude of benefits, including increased traffic safety, the redistribution of public spaces, and a reduction in negative environmental impacts. Further, the system's versatility and user-centric design, as indicated by the high acceptance and usage intentions reported in [15], suggest its potential to replace not only conventional bike sharing but also other short-distance transportation modes, including walking, biking, and public transport. Figure 1 shows the conceptual OSABS workflow. Initially, the user requests an autonomous driving cargo bike (ADCB) via a smartphone app. As a next step, the Operation Control Center (OCC) selects a suitable bike which will then autonomously drive to the user's location. After the bike arrives, the user manually fides it to a desired location. At the destination, the user releases the bike and it autonomously drives to the next customer or to a waiting or charging station (e.g., Refs. [16; 17; 18]). Figure 1: OSABS workflow. In order to explore its potential for sustainable urban mobility and provide a better understanding of the novel system, we evaluate different strategies (order management, station distribution, and rebalancing) and energy supply technologies using an agent-based simulation model [17; 18]. Next to various business cases, demand scenarios that predict the hourly use of OSABS for a one-year period are a key input for the simulation model. However, due to the novelty of OSABS, there is currently no information about its utilization. Within this paper, we therefore provide a data-driven approach that aims at modeling different demand scenarios for OSABS in a temporally and spatially disaggregated manner. Our contributions are three-fold. First, we identify the usage potentials of OSABS as a replacement for conventional modes of transport. Second, by analyzing and clustering trip data from a conventional bike sharing system in Hamburg, Germany, we show that annual usage patterns can be modeled with trip data for only eight representational weeks. Finally, we propose a methodology that enables traffic prediction for novel transportation systems such as OSABS with high temporal and spatial granularity by using traffic demand models and historical bike sharing data. We illustrate this approach by applying it as a case study to the city of Magdeburg, Germany. As our results show, the proposed approach provides reasonable predictions of hourly OSABS demand. Additionally, since we enable the representation of annual patterns with only eight weeks, we can reduce the computational effort of simulating annual OSABS usage by nearly 85%. The remainder of this paper is organized as follows. Within the next two sections, we review a collection of related publications on different aspects of bike sharing and demand prediction (Section 2) and outline the OSABS simulation model (Section 3). In Section 4, we then describe our demand generation methodology and its application as a case study. In Section 5, we present our modeling results. Finally, in Section 6, we discuss our findings, summarize our work, and highlight future research initiatives.