UPDF AI

Drive On - A Ride Hailing Application

Nirdesh Kumar

2025 · DOI: 10.22214/ijraset.2025.70221
International Journal for Research in Applied Science and Engineering Technology · 0 Citations

Abstract

Ride-hailing services have become an essential part of urban mobility. "Drive On" aims to offer an efficient, safe, and

cost-effective transportation solution, similar to Rapido, catering to daily commuters. This research paper discusses the app's

development, key features, market potential, challenges, and future prospects. The rise of ride-hailing platforms such as Uber,

Lyft, and Ola have revolutionized urban mobility, offering flexible work opportunities to millions of drivers worldwide. This

review paper explores the economic, social, and technological aspects of driving on ride-hailing apps, focusing on driver

earnings, working conditions, algorithmic management, and regulatory challenges. It examines the impact of gig work on

labour markets, driver job satisfaction, and financial sustainability.

Furthermore, the study analyses how ridehailing affects urban congestion, traffic patterns, and environmental sustainability.

The paper also highlights the evolving role of artificial intelligence (AI) and data-driven decision-making in ride-matching,

surge pricing, and driver performance evaluation. While ride-hailing offers flexibility and income opportunities, concerns

regarding job security, income volatility, and regulatory compliance remain prominent. The review concludes by discussing

future directions for research and policy interventions that can improve driver welfare and ensure the long-term sustainability of

the ride-hailing ecosystem.