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

Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where 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 bike-slots 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, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both 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

Internet of things applications using Raspberry-Pi: a survey

The internet of things (IoT) is the communication of everything with anything else, with the primary goal of data transfer over a network. Raspberry Pi, a low-cost computer device with minimal energy consumption is employed in IoT applications designed to accomplish many of the same tasks as a normal desktop computer. Raspberry Pi is a quad-core computer with parallel processing capabilities that may be used to speed up computations and processes. The Raspberry Pi is an extremely useful and promising technology that offers portability, parallelism, low cost, and low power consumption, making it ideal for IoT applications. In this article, the authors provide an overview of IoT and Raspberry Pi and research on IoT applications using Raspberry Pi in various fields, including transportation, agriculture, and medicine. This article will outline the details of several research publications on Raspberry Pi-based IoT applications

Parkkeerausten määrän ennustaminen tunneittain kausittaisesta datasta

Forecasting parking occupancy in city areas has become increasingly important to give the city and drivers a way to predict the available parking spaces. The city can use this information for planning and the drivers can predict where to park their car and avoiding the need of searching for a parking space. In this paper we introduce various prediction models for forecasting parking occupancy on an hourly level and compare their forecasting performance with a dataset of parking instances. The tested models include linear regression, gradient boosting, SARIMAX, TBATS, Facebook Prophet, and two neural network classes: long short-term memory and gated recurrent unit. The experimental model results were compared against each other, and the evaluated results suggest that gradient boosting is the best performing model for our dataset. The results are evaluated both in the error metrics and training times of the models.Parkkipaikkojen käyttöasteen ennustaminen kaupunkialueilla on tullut yhä tärkeämmäksi, jotta kaupungilla ja kuljettajilla on tapa ennakoida vapaana olevia pysäköintipaikkoja. Kaupunki voi käyttää ennusteita liikenteen suunnitteluun ja kuljettajat voivat ennakoida, mihin pysäköidä autonsa ja välttää pysäköintipaikan etsimisen tuomia haittapuolia, kuten bensan- ja ajankulutusta. Tässä työssä esittelemme erilaisia ennustemalleja pysäköintien käyttöasteen ennustamiseksi tunnin välein ja vertaamme niiden ennustekykyä pysäköintitapahtuma tietoaineistoa käyttäen. Testattuihin malleihin sisältyvät lineaarinen regressio, gradient boosting, SARIMAX, TBATS, Facebook Prophet sekä kaksi neuroverkkoluokkaa: long short-term memory ja gated recurrent unit. Mallien alustavat tulokset viittaavat siihen, että gradient boosting antaa parhaat tulokset työn aineistoa käytettäessä. Mallien vertailun perusteena käytettiin sekä suorituskykyä, että koulutusaikoja