User-based redistribution in free-floating bike sharing systems

We investigate the problem of user-based redistribution for free-floating bike sharing systems (BSS). We present a stochastic model of the bike dynamics and we show that the spatial distribution of bikes is correlated. This is specific to free-floating systems and it results in a substantially reduced service level. Offering incentives to users may stimulate them to change their behavior and usage pattern. We analyze drop-off incentives, derive an incentive methodology and study its potential. We show that by implementing a smart incentive system, the number of bikes for establishing a specific service level can be reduced significantly, even if only a minority of users participates. Under realistic behavioral assumptions, 30–50% reduction of bikes is achievable, which converts into substantial costs savings for the operator. Our research was carried out in the context of the development of the new e-bike sharing system “smide” in Zurich, launched in 2017. The incentive approach has been implemented and tested in a field test

Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools

For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of their fleet. The estimation of the expected number of bookings is carried out with a simple model and a more complex model based on meterological information, as the number of loans depends strongly on the current and forecasted weather. Secondly, the knowledge of a service level violation in future on a fine spatial resolution is important for redistribution of bikes. With this information, the service provider can set reward zones where service level violations will occur in the near future. To forecast a service level violation on a fine geographical resolution the current distribution of bikes as well as the time and space information of past rentals has to be taken into account. A Markov Chain Model is formulated to integrate this information. We develop a management tool that describes in an explorative way important information about past, present and predicted future counts on rentals in time and space. It integrates all estimation procedures. The management tool is running in the browser and continuously updates the information and predictions since the bike distribution over the observed area is in continous flow as well as new data are generated continuously

Sustaining dockless bike-sharing based on business principles

Currently in urban areas, the value of money and fuel is increasing because of urban traffic congestion. As an environmentally sustainable and short-distance travel mode, dockless bike-sharing not only assists in resolving the issue of urban traffic congestion, but additionally assists in minimizing pollution, satisfying the demand of the last mile problem, and improving societal health. Despite the positives that this new transportation mode provides, currently there are few effective measures in place to make the development of dockless bike-sharing providers more sustainable. This study endeavors in establishing a foundation for resolving this problem through developing business models of dockless bike-sharing based on business theory and principles, and utilizing the largest dockless bike-sharing company in China as of November 2018 named Mobike as an example within these business models. The long-term sustainability issues of dockless bike-sharing are identified through various methods including an operational analysis of one of Mobike’s largest divisions located in Beijing, China, and potential solutions to those issues as well as policy implications are presented based on the research and analysis conducted

Station segmentation of Lisbon bicycle sharing system based on users demand and supply

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBike-sharing systems are well known in the sustainable mobility field and have several aspects that need optimization and improvement. One of the most relevant aspects is station segmentation based on user demand and supply, and it is the focus of the thesis. The segmentation work has an enormous potential to reduce complexity in predicting the bicycle demand and supply, thus improving the overall quality of service. Several machine learning algorithms were used to investigate the aforementioned segmentation task. This work considers two popular and well-known clustering algorithms to extract and analyze interesting patterns, like the difference between arrivals and departures throughout time and stations: the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the hierarchical clustering. The algorithms are applied to the specific case of GIRA, the bicycle sharing system (BSS) of the city of Lisbon. The obtained results suggest that considering the variables under analysis, the optimal number of clusters to be used in a second phase of the BSS optimization (demand and supply forecast) is the same as the number of stations in the Lisbon BSS. The results are very insightful and allow future work to focus either on the demand forecast or the enrichment of the variables under study

A continuous approximation model for the optimal design of public bike-sharing systems

During the last decade, public bike-sharing systems have gained momentum and popularity. Many cities worldwide have put their trust in bike-sharing to promote bicycle use and move towards more sustainable mobility. This paper presents a parsimonious model from which to derive the optimal strategical design variables for bike-sharing systems (i.e. the number of bicycles, the number of stations and the required intensity of rebalancing operations). This requires an integrated view of the system, allowing the optimization of the trade-off between the costs incurred by the operating agency and the level of service offered to users. The approach is based on the modelling technique of continuous approximations, which requires strong simplifications but allows obtaining very clear trade-offs and insights. The model has been validated using data from Bicing in Barcelona, and the results prove, for example, the existence of economies of scale in bike-sharing systems. Also, station-based and free-floating system configurations are compared, showing that free-floating systems achieve a better average level of service for the same agency costs. In spite of this, the performance of free-floating systems will tend to deteriorate in the absence of a strong regulation. Furthermore, if electrical bikes are used, results show that battery recharging will not imply an active restriction in station-based configurations. In conclusion, the proposed modeling approach represents a tool for strategic design in the planning phase and provides a better understanding of bike-sharing systemsPeer ReviewedPostprint (author's final draft

A Review of Business Models for Shared Mobility and Mobility-as-a-Service (MaaS):A Research Report

The mobility solutions that currently dominate the mobility market have raised global challenges. Specifically, mass car ownership has led to traffic congestion, shortage of parking spaces, and sustainability issues. Recently, mobility solutions driven by technological advancements have emerged to address these issues via more efficient and sustainable use of resources. However, the wide range of mobility offerings has led to a scattered mobility market, and oversight is hard to grasp for travelers. Mobility-as-a-Service (MaaS) platforms aim to address this issue by integrating mobility services into a single platform. However, MaaS providers (operators) struggle to find sustainable business models. Additionally, research on shared mobility business models is limited, and there is little oversight in the scattered business model landscape. This report addresses this issue by summarizing the dominant business models in the mobility market through a systematic review of current initiatives and literature. It provides an overview of active MaaS business models and challenges and opportunities to integrate mobility services into MaaS. The types of mobility services reviewed in this study include bike-sharing, scooter-sharing, car-sharing, e-hailing, and MaaS platform providers. For each mobility service, the dominant operating mode and the main business model actors are identified and represented using the Service-Dominant Business Model Radar (SDBM/R). Furthermore, the value exchanges between the actors are mapped in Value Capture Diagrams. The report concludes with a discussion on the challenges and opportunities related to synthesizing shared mobility modes into MaaS and the expectations for its future