Featured Application: The main application of this work is the analysis and prediction of the demand in bike sharing systems using their open/private data. A multi agent system is proposed and a case study is conducted using the data of a bicycle sharing system from a middle size city. Abstract: This paper proposes a multi agent system that provides visualization and prediction tools for bike sharing systems (BSS). The presented multi-agent system includes an agent that performs data collection and cleaning processes, it is also capable of creating demand forecasting models for each bicycle station. Moreover, the architecture offers API (Application Programming Interface) services and provides a web application for visualization and forecasting. This work aims to make the system generic enough for it to be able to integrate data from different types of bike sharing systems. Thus, in future studies it will be possible to employ the proposed system in different types of bike sharing systems. This article contains a literature review, a section on the process of developing the system and the built-in prediction models. Moreover, a case study which validates the proposed system by implementing it in a public bicycle sharing system in Salamanca, called SalenBici. It also includes an outline of the results and conclusions, a discussion on the challenges encountered in this domain, as well as possibilities for future work.
In REF , a multi agent system that provided visualization and prediction tools for BSS was proposed.
Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems
Applied Sciences
Engineering