Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Abstract

Bike-sharing is adopted as a valid alternative to traditional public transports since they are ecofriendly, prevent traffic congestions, reduce the probability of social contacts which happens on most of the public means. However, some problems may occur such as the irregular distribution of bikes on the stations/racks/areas, and the difficulty of knowing in advance the rack status with a certain degree of confidence, whether there will be available bikes at a specific bike-station at a certain time of the day, or a free slot for leaving the rented bike. Thus, providing predictions can be useful to improve the quality of service, especially in those cases in which the bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bikeslots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions of 15, 30, 45, and 60 minutes. The study demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offer a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. The paper also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different behaviours of the clusters. The solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure.

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

“…The business layer was introduced to classify all operations and front-end tools that consume data from the application layer for producing advanced big data analytics and visualization services, with the goal of building business models, supporting decisionmaking processes and performing simulations and what-if analysis. This can be achieved by implementing, for instance, predictive models based on machine learning, deep learning and artificial intelligence (AI) techniques [34], as well as advanced and interactive visual analysis tools [35]. Moreover, the business layer includes all operations performed by system administrators, which are needed to assess, control and maintain the overall functionality of the platform/framework.…”

“…In Berlin, the smart mobility service Jelbi was introduced in 2019, based on a mobile application that is connected to all services provided locally (e.g., car-sharing companies such as MILES and DB Flinkster and e-vehicle hire companies such as TIER), to foster the passage toward a more sustainable mobility [79]. In Florence, the Snap4City platform has been developed in the context of the Sii-Mobility project for sustainable mobility, providing a flexible IoT smart city platform and several applications to manage heterogeneous and complex urban mobility scenarios by integrating city sensors/actuators and IoT/IoE [34]. Atlanta has powered its transportation infrastructure with many smart technologies.…”

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction

“…Facility location, traditionally, refers to the approach of deploying bike stations to maximise the satisfaction of users demands, with minimum possible infrastructure cost [46], [93]. Recent facility location research for bike-sharing has also investigated the optimal planning of geo-fence stations, which are virtual stations that encourage users to park the bikes in the designated areas inside the geo-fence [55].…”

“…The core of re-balancing is often solved by using the mixed integer programming approach [5], [16], [72], [98]. Reservation scheme [2], demand prediction [3], [4], [7], [93], [93], [99]- [101], as well as monetary incentives [69], [70], [102], [103], can effectively increase the performance of re-balancing.…”

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems

“…In addition, some studies focus on the prediction of rental and return demands for stations, while common models in recent years include multiple regression analysis [ 16 ], random forest [ 17 , 18 ], boosting framework [ 17 , 18 , 19 ], deep learning [ 18 , 20 ], etc. Several works employ probability distributions to model the number of trips at each station, containing negative binomial [ 17 , 21 ], Weibull [ 22 , 23 ] and Poisson [ 16 , 17 ], where the latter is often the best choice for this task.…”