Abstract :

Despite the technology disruption from online ride-hailing, street-hailing is still popular among certain people in Jakarta, the capital city of Indonesia. However, the traditional street-hailing faces several challenges for the taxi operators. Unlike ride-hailing apps where customers and drivers are matched and recorded digitally, street-hailing often lack visibility into street demand that is not always successfully picked up, which can lead to inefficiencies in fleet utilization, highlighting a significant operational gap between traditional taxi services and modern ride-hailing platforms.

Artificial Intelligence (AI) and Computer vision (CV) technologies offer a transformational approach to improve visibility and identify the unmet demand for street-hailing within urban transportation in Jakarta. Prior to its implementation, it is important to assess the potential business impact from deploying this digital innovation. This research highlights inference statistics combined with qualitative analysis to evaluate the potential demand that can be captured through CV systems. By leveraging historical taxi trajectory data and interviewing drivers and customers, we aim to uncover patterns and estimate unmet demand to ensure the system development will impact the taxi business and improve the service efficiency and customer satisfaction. This preliminary analysis serves as a foundation for strategic planning of CV system for street-hailing detection implementation.

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Keywords :

taxi; street-hailing; unmet demand; computer vision; artificial intelligence; digital innovation; business impact

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