K-Prototype Segmentation Analysis on Large-scale Ridesourcing Trip Data

TL;DR

This paper uses K-prototypes clustering on Chicago ridesourcing data to identify user segments and analyze how weather, location, and timing influence ride-hailing patterns, providing insights into urban mobility dynamics.

Contribution

It introduces a novel application of K-prototypes clustering to segment ridesourcing users based on mixed data types, revealing six distinct mobility patterns in Chicago.

Findings

Six user prototypes identified with distinct behaviors.

Weather and location significantly influence ride-hailing patterns.

Implications for transit equity and competition discussed.

Abstract

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they impact travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examines emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data are matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcings role in Chicagos mobility system. The goal is to investigate the systematic variations in patronage of ride-hailing. K-prototypes is utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes are identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses implications of the identified clusters related to affordability, equity and competition with transit.