Analysis of public transportation patterns in a densely populated city with station-based shared bikes

• Main Authors: Wang, Di, Wu, Evan, Tan, Ah-Hwee
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Self-regulated Clustering; Shared Bike
• Online Access: https://hdl.handle.net/10356/144625
• Institution: Nanyang Technological University

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.