A Conceptual Model for the Simulation of the Next Generation Bike-Sharing System with Self-driving Cargo-Bikes

Salah, Imen Haj; Mukku, Vasu Dev; Schmidt, Stephan; Assmann, Tom

doi:10.1007/978-3-030-61075-3_25

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…An overview of the conceptual model is presented in Figure 1. The reader can refer to [40] for a detailed description of the conceptual model. As can be seen in the overview, the main components of the model are customers (created based on demand scenarios), bikes, and stations (waiting and charging stations).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and transportation sector nowadays. Researchers, universities, and companies are working intensively to provide novel mobility options that can be environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising using 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 still needed to develop this new concept. To address this research gap, this paper examines the on-demand shared-use autonomous bikes service (OSABS) from a fleet management perspective. In fact, such a system requires good management strategies in order to ensure its efficiency. Through an agent-based simulation of a case study in Magdeburg, we investigate various parameters that can influence the performance and the service quality of OSABS such as the rebalancing frequency and the relocation type. Tests were performed for two different operational areas: the inner city and the complete city of Magdeburg. We conclude with different management insights for an optimized functioning of the system.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…In order to understand the insights of the OSABS, we developed an agent-based simulation model using Anylogic based on our conceptual model presented in [54]. The developed conceptual model represents the key elements of OSABS, which are: demand scenarios, order management, operational aspects (bikes and stations), and output indicators where we calculate the performance indicators.…”

Section: Model Descriptionmentioning

confidence: 99%

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

Salah

Mukku

Kania

et al. 2021

Future Transportation

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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.

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“…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]). 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].…”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18]). 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.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets

Kania,

Mukku,

Kastner

et al. 2023

Applied Sciences

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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.

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A Conceptual Model for the Simulation of the Next Generation Bike-Sharing System with Self-driving Cargo-Bikes

Cited by 7 publications

References 15 publications

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

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

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

Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets

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