Approaches to Successful Entry of the Ride-sharing Service for Startups

Abstract

Ride-sharing can be a key factor in reducing traffic volume and CO2 emission for public transportation especially within urban area. However, it is not easy for small ride-sharing startups, which typically has limited budget and manpower, to boost the service in the early stages. In this paper, we present our on-demand service whose main operation is based on a mobile application for taxi-sharing which utilizes the users' trust network. We also explain in detail the three phased approaches we have applied in an attempt to increase the matching rate among the users. A high matching rate is crucial for survival of a ride-sharing service, because once a service fails to match customers in real-time, the retention rate falls sharply. Next, we analyze and interpret the implication of committing to each approach. Additionally, we also introduce one last method, which employs an AI agent to guarantee match making among users at all times by overcoming the time-spatial constraints.