Understanding Spatiotemporal Station and Trip Activity Patterns in the Lisbon Bike-Sharing System

Abstract

The development of the Internet of Things and mobile technology is connecting people and cities and generating large volumes of geolocated and space-time data. This paper identifies patterns in the Lisbon GIRA bike-sharing system (BSS), by analyzing the spatiotemporal distribution of travel distance, speed and duration, and correlating with environmental factors, such as weather conditions. Through cluster analysis the paper finds novel insights in origindestination BSS stations, regarding spatial patterns and usage frequency. Such findings can inform decision makers and BSS operators towards service optimization, aiming at improving the Lisbon GIRA network planning in the framework of multimodal urban mobility.

Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

“…Future work aims to better understand these results' implications, especially regarding external factors such as weather, air quality, events, crowd flow, and bike data. Studies [41,42] of the Lisbon bike-sharing system have shown the influence of external factors, such as weather and bike type, in bike use and ride frequency. Moreover, pedestrian walkability, cycleways, bike stations, and bike accident data are of interest to correlate with this study's findings and understand the overall Lisbon urban mobility scenario.…”