# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 6 Discussion and Future Work  
The resulting hourly booking figures are represented as OD-matrices and can be used directly for the OSABS simulation model. In the realm of simulation, the generated demand patterns serve as valuable input, contributing to the design and implementation of various strategies. Firstly, the implementation of suitable order management strategies, especially rebalancing algorithms, benefits from the predicted spatial and temporal patterns, leading to enhanced operational efficiency and customer satisfaction. Additionally, our methodology provides guidance for the placement of OSABS stations. By accurately predicting traffic flows with high spatial granularity, we can identify areas of high demand and strategically position stations to meet this demand, thus optimizing resource allocation and improving service availability. Furthermore, our approach supports the selection of suitable energy supply technologies. The ability to anticipate OSABS utilization patterns, particularly during periods of increased usage, aids in making informed decisions about the types of energy supply technologies that would be most efficient and sustainable. A further advantage of the proposed methodology lies in its wide applicability to other cities or use cases, as the data used, i.e., bike sharing data (in general or for the city itself) and OD-matrices (most often for larger cities), are widely available. Moreover, the survey on usage probabilities and distance limits can be conducted with little preparation and survey effort. Hence, in terms of conventional BSS, city authorities could use our approach to straightforwardly determine efficient locations for bike docking stations, which not only would ensure that bikes are available where and when they are needed but could also prevent issues such as overcrowding at certain stations. Similarly, system operators could use our demand scenarios to optimize bike distribution, reducing the amount of time and resources spent on repositioning bikes. Hence, through promoting the adoption of (conventional) bike sharing systems, our research may also help to drive a shift towards more sustainable modes of transportation, resulting in reduced carbon emission and air pollution in cities, but also in improved public health and overall quality of stay. Figure 11: Predicted hourly bookings. While providing new insights into the generation of demand scenarios for novel transportation systems, our study also raises the need for additional research. One primary challenge is the theoretical nature of our model coupled with the lack of real-world OSABS data. This has led to a reliance on indirect validation methods, where we compare our model's predictions with existing bike sharing systems data. The comparison suggested higher predicted OSABS utilization in Magdeburg, which we attribute to the unique features of OSABS, such as its extended reach and mobility-as-a-service offerings. While retrieving reasonable results from indirect validation, the integration of real-world OSABS data in future studies and hence the direct validation of our predictions remains a pivotal task. Further, in our analysis, we applied annual usage patterns to represent long-term fluctuations in demand. To enhance the model's accuracy in capturing short-term demand fluctuations, future work could involve the integration of real-time traffic data, weather conditions, and information about special events into the demand forecasting process. This would allow for a more dynamic and responsive model, better suited to the complexities of urban transportation. An additional limitation arises from our model's reliance on traffic zones for predicting demand, without giving the exact position for single requests. In line with findings from [PERSON] et al. (2012) and [PERSON] et al. (2015), we aim to refine the spatial detail of our demand forecasts in future iterations of our research by incorporating land use, sociodemographic information, and different trip purposes.