Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes

Abstract

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public transportation system. In this paper, we analyse the public transportation patterns in a densely populated city, Chicago, USA, using comprehensive datasets covering the transportation records on shared bikes, buses, taxis and subways collected over one year's time. Specifically, we apply self-regulated clustering methods to reveal both the majority transportation patterns and the irregular ones. Other than reporting the autonomously discovered transportation patterns, we also show that our method achieves better clustering performance than the benchmarking methods.

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

“…At the feature level, studies [5,37] consider a single factor instead of multiple aspects features and thus may neglect representative elements. Other works collect historical data such as public transportation pattern records [38], crowd flow [39], meteorology data [7,8,40], and so on. Clustering methods applied to bike stations are more and more common in recent works since bike stations share partially similar regional characteristics and will reduce the variance and improve prediction accuracy.…”