Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets

Abstract

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size.

Implications of the Relocation Type and Frequency for Shared Autonomous Bike Service: Comparison between the Inner and Complete City Scenarios for Magdeburg as a Case Study

“…Nevertheless, there is a slight difference in the effort made to achieve the needed service level, as seen in the relocation distance and energy values in Figures 4 and 5. These results confirm the finding of the previous study [37] where we had very similar service levels for the (PR + RaR) case of the three relocation strategies. A new finding for this paper is that the strategies where rebalancing after rental (RaR) is applied required the least number of bikes.…”

“…Fifth, we believe that these results could be better evaluated if we consider the costs associated with the bike investment and energy consumption. While it is hard to give precise values at this stage of research, we will evaluate the results according to first estimations based on our talks with industry experts and our best knowledge of the market price [37]. Table 5 presents the cost assumptions of our study.…”

“…The demand scenarios are generated based on realistic spatial and temporal demand patterns determined from real-world traveling data. This paper is considered as a continuity of the work done in [37] where we made a first comparison between the three relocation strategies for the inner city but without varying the rebalancing parameters. In this work, we extend the study by investigating the implication of the rebalancing frequency and comparing the various relocation types.…”

On Optimizing Driving Patterns of Autonomous Cargo Bikes as a Function of Distance and Speed—A Psychological Study

“…Its field of application is, for example, access to public transport or bridging the last mile, for transport purposes in urban settings such as shopping, load transport, child transport, or for pedalling support for common cycle lanes for users with limited mobility, see also [12]- [14]. Thus, the autonomous cargo bike could significantly promote door-to-door mobility and solve common problems of conventional bike-sharing systems, such as fleet imbalances during peak hours [15], [16]. Our interdisciplinary research focuses not only on the technical realization of such an autonomous cargo bike, but also on how it should behave in daily contact with pedestrians, other cyclists, or car drivers.…”