Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems

Les systèmes de vélos en libre-service sont utilisés à l’échelle mondiale pour soulager la congestion et apporter une solution aux problèmes environnementaux dans les villes. De nos jours, presque toutes les grandes villes ont un tel système. Ces systèmes sont très pratiques pour les utilisateurs qui n’ont pas besoin de faire l’entretien du vélo et peuvent le rendre presque partout dans la ville. Cependant, le nombre croissant d’abonnés et la demande aléatoire rendent la planification opérationnelle du système très difficile. Prédire la demande en vélos a été l’objet de nombreuses recherches dans la communauté scientifique. Cependant, la plupart des travaux ont cherché à prédire la demande globale du réseau, qui n’est généralement pas suffisante pour améliorer la planification opérationnelle. En effet elle nécessite des prévisions spécifiques pour chaque station et à des moments précis de la journée. Ces travaux ont montré qu’une variation significative du trafic peut être liée à des comportements réguliers, et à des facteurs externes tels que les heures de pointe ou les conditions météorologiques. En particulier, de nombreux opérateurs utilisent des intervalles pour combler les lacunes dans la prédiction du trafic. Cependant, très peu de travaux ont cherché à correctement définir ces intervalles. Dans cette recherche, nous nous concentrons sur la modélisation de la distribution statistique du nombre de déplacements qui se produisent à chaque heure et chaque station. Ce modèle ne se contente pas de prédire l’espérance du nombre de voyages prévus, mais aussi la probabilité de chaque nombre de départs et d’arrivées par station en utilisant la demande historique. Le modèle mis en place est composé de trois parties. Tout d’abord, nous estimons, en utilisant des techniques d’apprentissage machine, le nombre de trajets attendus à chaque station. Puis, nous calculons la confiance sur la première prédiction (variance attendue). Enfin, nous déterminons la bonne distribution à utiliser.----------ABSTRACT: Bikesharing systems are globally used and provide relief to congestion and environmental issues in cities. Nowadays, almost all big cities have a bicycle-sharing system. These systems are very convenient for users that don’t need to do maintenance of the bicycle and can return it almost everywhere in the city. However, the increasing number of subscribers and the stochastic demand makes the operational planning of the system very difficult. Predicting bike demand has been a major effort in the scientific community. However, most of the efforts have been focused on the prediction of the global demand for the entire system. This is typically not sufficient to improve the operational planning, which requires demand predictions for each station and at specific moments during the day. A significant variation of the traffic can be linked to regular behaviors, and external factors as peak hours or weather. In particular, many system operators use fill level intervals which guide the redeployment crews in their efforts to equilibrate the system. However, little work has been done on how to effectively define those fill levels. In this research, we focus on modeling the distribution of the number of trips that occur at each hour and each station. This model not only seeks to predict the number of expected trips, but also determines as precisely as possible the expected distribution of trips. It uses the historical observed demand to predict future demand. The prediction model is composed of three parts. First, we estimate from historical data the expected number of trips, using machine learning techniques that use features related to weather and time. Second, we compute the confidence of the first prediction (expected variance). Finally, we focus on determining the right distribution to use. The first part uses a two-step algorithm that first reduces the problem to a simpler one, minimizing the information lost, then learns a predictive algorithm on the reduced problem. The prediction process inverts this mechanism. Several simplification and prediction methods are tested and compared in terms of precision and computing times. The final test compares distribution estimations in terms of log likelihood. The results show that the choice of the best algorithm depends on the station. Then a combined model is proposed to better model the demand. Our models are tested on several networks (Montreal, New York and Washington). Finally, this model is used to define an online rebalancing strategy close to the one used by Bixi at Montreal. This strategy has been deployed in Montreal

On the Simultaneous Computation of Target Inventories and Intervals for Bimodal Bike-Sharing Systems

ABSTRACT: The emerging demand for electric bicycles in recent years has prompted several Bike-Sharing Systems around the world to adapt their service to a new wave of commuters. Many of these systems have incorporated electric bikes into their network while still maintaining the use of regular mechanical bicycles. However, the presence of two types of bikes in a Bike-Sharing network may impact how rebalancing operations should be conducted in the system. Regular and electric bikes may exhibit distinct demand patterns throughout the day, which can hinder efficient planning of such operations. In this paper, we propose a new model that provides rebalancing recommendations based on the demand prediction for each type of bike. Additionally, we simulate the performance of our model under different scenarios, considering commuters’ varying inclination to substitute their preferred bike with one of a different type. Our empirical experiments indicate the potential of our model to improve user satisfaction, reducing the total lost demand by approximately 10%, while reducing the lost demand for electric bikes by around 30%, on average, when compared to the existing rebalancing strategy used by the real-world Bike-Sharing System under study. Remarkably, this was accomplished while maintaining an almost identical average hourly count of rebalancing operations

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach

Rebalancing techniques for station-based bike sharing systems

With bike-sharing systems that utilize fixed rent and drop off stations becoming popular in cities and metropolitan areas worldwide, the issue of station fill balance becomes apparent. It is important for the user experience and the organization's bottom line that bikes are available at the stations where they are needed and that stations do not become too crowded and thus prevent easy returns. There is not, however, a clear solution of how to perform this rebalancing. Considerations include how to determine the stations that most need to be rebalanced, how frequently to do this rebalancing in the system, and how many resources to expend doing it. Methodologies answering some of these questions have been proposed, but many do not provide all of the answers necessary to fully implement a real-world solution. Additionally, there is no benchmarking tool to fairly compare these rebalancing approaches on a given system. This thesis proposes exactly this kind of tool in the form of a station-based bike-sharing system simulator. The simulator is modular and provides several parameters to allow the comparison of different systems, historical data, workloads, and rebalancing strategies. To demonstrate its capabilities, experiments were run comparing the effects of individual parameter changes and various combinations of parameter configurations on various metrics, including gross revenue and lost revenue from missed demand. Analysis of these experimental results gives not only a look into the simulation's uses as a comparative tool, but also provides information on alternatives to common predictive rebalancing strategies

Developing sales forecasting by utilizing business intelligence : A Single Case Study

In a highly volatile business environment, companies must utilize technological tools to make decisions effectively and efficiently. Therefore, companies use business intelligence to aid in decision-making. Sales forecasting is a key component of decision-making in companies, as it lays the groundwork for a vast number of decisions. However, only some companies have harnessed the full potential of business intelligence and sales forecasting. This study examines how business intelligence can be utilized in sales forecasting. More precisely, this study examines the significance of capabilities in improving sales forecasting accuracy. The role of capabilities is vital as capabilities are intangible assets, in contrast to technological tools that are easily imitable and mobile. This study's literature review focuses on business intelligence and sales forecasting literature. Previous studies of business intelligence and sales forecasting capabilities are examined to identify the key capabilities. Previous studies that synthesize business intelligence and sales forecasting are lacking. Moreover, there is a gap in the previous literature as the key business intelligence capabilities for sales forecasting have not been identified. This thesis adopts a qualitative approach to answering the research questions. The case study method is used to examine how business intelligence can be used to develop sales forecasting. More precisely, the study is a single case study focusing on a company operating in the rental industry. The data is gathered for the study through six semi-structured interviews. The interviewees are chosen based on their business intelligence and sales forecasting expertise. The findings of this study provide insight into how business intelligence can be utilized to develop sales forecasting. The theoretical framework developed for this study presents the key business intelligence and sales forecasting capabilities that improve sales forecasting accuracy. More precisely, the key capabilities are analyzed to get more detailed information on how capabilities can be developed to match the needs of the sales forecasting process. This study concludes by addressing its limitations and suggesting future research to extend the research in business intelligence and sales forecasting

Passively generated big data for micro-mobility: state-of-the-art and future research directions

The sharp rise in popularity of micro-mobility poses significant challenges in terms of ensuring its safety, addressing its social impacts, mitigating its environmental effects, and designing its systems. Meanwhile, micro-mobility is characterised by its richness in passively generated big data that has considerable potential to address the challenges. Despite an increase in recent literature utilising passively generated micro-mobility data, knowledge and findings are fragmented, limiting the value of the data collected. To fill this gap, this article provides a timely review of how micro-mobility research and practice have exploited passively generated big data and its applications to address major challenges of micro-mobility. Despite its clear advantages in coverage, resolution, and the removal of human errors, passively generated big data needs to be handled with consideration of bias, inaccuracies, and privacy concerns. The paper also highlights areas requiring further research and provides new insights for safe, efficient, sustainable, and equitable micro-mobility