Case Study: Ascendas-Singbridge achieves 20% revenue uplift and accurate carpark capacity forecasting with DataRobot

Predicting Carpark Capacity at Ascendas-Singbridge Using Machine Learning

Ascendas-Singbridge, Asia’s leading sustainable urban and business space solutions provider, faced a persistent challenge forecasting carpark capacity across its properties in Singapore and the region. To improve parking efficiency, visitor experience, and uncover new revenue opportunities, Ascendas-Singbridge turned to DataRobot’s automated machine learning platform—specifically its Time Series features—to predict hourly and daily parking demand and the mix of season-pass versus hourly parkers.

DataRobot ran a proof of concept and delivered more accurate time-series models than Ascendas-Singbridge’s previous vendor while automating time-series feature engineering and simplifying deployment for IT developers without formal data science training. The solution enabled dynamic allocation of parking (e.g., opening hourly spots when forecasts showed season-pass vacancies), scaled easily to new sites, and drove measurable impact: a 20% increase in revenue within the first eight months after rollout.

Ascendas-Singbridge

Leong Hiong Yee

Manager - IT Enterprise Information Management