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

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

Evaluating the Effectiveness of Bike Sharing Programs in Encouraging Sustainable Transportation in Urban Areas

Bike sharing programs have emerged as a popular solution to promote sustainable transportation in urban areas. This research abstract presents five key points that highlight the effectiveness of bike sharing programs in encouraging sustainable transportation. Firstly, these programs facilitate a modal shift by providing convenient access to bicycles, encouraging individuals to choose cycling as a sustainable transportation option and reducing reliance on private motorized vehicles, thereby decreasing carbon emissions. Secondly, bike sharing programs effectively address the last-mile problem by offering bicycles at strategic locations near transit hubs, providing a convenient and efficient mode of transportation for short-distance trips and complementing existing public transit systems. Thirdly, these programs enhance transportation accessibility by offering affordable rental options, enabling a broader range of people, including those without access to private vehicles or unable to afford their upkeep, to access transportation, thus promoting inclusivity and reducing transportation inequality. Moreover, bike sharing programs promote public health by encouraging regular cycling as a mode of transportation. This promotes physical activity and helps individuals meet recommended activity guidelines, resulting in a reduced risk of non-communicable diseases such as obesity, cardiovascular diseases, and diabetes, contributing to the overall sustainability and well-being of urban populations. Lastly, bike sharing programs generate valuable data that can inform urban transportation planning and infrastructure development. By analyzing usage patterns, trip durations, and popular routes, city planners can identify areas with high demand for cycling infrastructure, leading to the efficient allocation of resources and the optimization of urban transportation systems

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called {\em TruPreTar} to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.Comment: Accepted by AAAI 2020; This is the full version that contains all the proof

Sources and Applications of Emerging Active Travel Data : A Review of the Literature

Peer reviewedPublisher PD

Towards Systematic Specification of Non-Functional Requirements for Sharing Economy Services

Sharing Economy (SE) systems use technologies to enable sharing of physical assets and services among individuals. This allows optimisation of resources, thus contributing to the re-use principle of Circular Economy. In this paper, we assess existing SE services and identify their challenges in areas that are not technically connected to their core functionality but are essential in creating trust: information security and privacy, personal data protection and fair economic incentives. Existing frameworks for elicitation of non-functional requirements are heterogeneous in their focus and domain specific. Hence, we propose to develop a holistic methodology for non-functional requirements specification for SE systems following a top-down-top approach. A holistic methodology considering non-functional requirements is essential and can assist in the analysis and design of SE systems in a systematic and unified way applied from the early stages of the system development

Data towards city bike mobility patterns

New technologies applied to transportation services and the shifting to sustainable modes of transportation turned bike-sharing systems more relevant in the urban mobility scenario. This thesis aims to understand the spatiotemporal station and trip activity patterns in Lisbon bike-sharing system in 2018 and understand trip rate changes in Lisbon bike-sharing system in 2019 and 2020 compared to 2018. By analyzing the spatiotemporal distribution of trips through stations and the weather factors combined with the usage rate throughout the years, it is possible to improve and make the system more suitable to the users’ demand. In this research work, we used large open datasets made available by the Lisbon City Hall, that are deployed by using the CRISP-DM. Our major work contribution was the development of a data analytics process for urban data, specifically bike-sharing data, that helps to understand how people move in the city using bikes. Moreover, we aimed to understand how mobility patterns change over time and the impact of pandemic events. Major findings show that most bike-sharing happens on weekdays, with no precipitation and mild temperature. Additionally, there was an exponential increase in the number of trips, cut short by COVID-19 pandemics. The current approach can be applied to any city with digital data available.As novas tecnologias aplicadas aos serviços de transporte e a transição para meios de transporte sustentáveis tornaram os sistemas de bicicletas partilhadas mais relevantes no cenário da mobilidade urbana. O objetivo deste estudo é compreender os padrões de mobilidade de espaço e tempo das estações e viagens neste sistema de Lisboa em 2018, e também compreender as mudanças na taxa de viagens nos sistemas de Lisboa em 2019 e 2020 em comparação com 2018. Analisando a distribuição de espaço e tempo das viagens através das estações e, os fatores climáticos juntamente com a taxa de utilização ao longo dos anos, é possível melhorar e tornar o sistema mais adequado à procura dos utilizadores. Usamos um grande conjunto de dados com implementação do CRISP-DM. A principal contribuição do trabalho foi o desenvolvimento de um processo de análise e visualização de dados urbanos, especificamente dados de sistemas de bicicletas partilhadas, que permite assim, a melhor compreensão de como as pessoas se movem na cidade usando bicicletas. Além disso, é importante identificar os padrões de mobilidade que mudam com o tempo e o impacto dos eventos pandémicos. Os resultados mostram que a maior parte do uso de bicicletas partilhadas é efetuado durante a semana, sem precipitação e com temperatura amena. Houve um aumento exponencial no número de viagens, por sua vez interrompido pela pandemia do COVID-19. Esta abordagem pode ser aplicada a qualquer cidade com dados digitais disponíveis