English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the emergence of e-hailing taxi services, a growing number of scholarshave attempted to analyze the taxi trips data to gain insights from drivers'and passengers' flow patterns and understand different dynamics of urban publictransportation. Existing studies are limited to passengers' location analysise.g., pick-up and drop-off points, in the context of maximizing the profits orbetter managing the resources for service providers. Moreover, taxi drivers'locations at the time of pick-up requests and their pickup performance in thespatial-temporal domain have not been explored. In this paper, we analyzedrivers' and passengers' locations at the time of booking request in thecontext of drivers' pick-up performances. To facilitate our analysis, weimplement a modified and extended version of a co-clustering technique, calledsco-iVAT, to obtain useful clusters and co-clusters from big relational data,derived from booking records of Grab ride-hailing service in Singapore. We alsoexplored the possibility of predicting timely pickup for a given bookingrequest, without using entire trajectories data. Finally, we devised twoscoring mechanisms to compute pickup performance score for all drivercandidates for a booking request. These scores could be integrated into abooking assignment model to prioritize top-performing drivers for passengerpickups.

Understanding the Dynamics of Drivers' Locations for Passengers Pickup Performance: A Case Study