Predicting Carpark Capacity at Ascendas-Singbridge Using Machine Learning
Customer Company Size
Large Corporate
Region
- Asia
Country
- Singapore
Product
- DataRobot’s automated machine learning platform
- DataRobot’s Time Series functionality
Tech Stack
- Machine Learning
- Time Series Analysis
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Machine Learning
Applicable Functions
- Facility Management
Use Cases
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Ascendas-Singbridge Group (ASG) is Asia’s leading sustainable urban and business space solutions provider, with Assets Under Management totaling more than $20 billion worldwide. Headquartered in Singapore, the Group has a presence across 11 countries in Asia, Australia, Europe, and the United States of America. They are focused on improving parking lot efficiency at their many properties around Singapore and throughout Asia. The company has more than $20 billion worth of Assets Under Management (AUM) in 28 cities across 11 countries, including Australia, China, India, Indonesia, and Singapore.
The Challenge
Ascendas-Singbridge Group (ASG), a leading sustainable urban and business space solutions provider in Asia, was facing a challenge with parking capacity at their properties. In densely populated cities like Singapore, parking capacity is a major issue. Despite having high-rise buildings with carparks or garages, parking capacity remained a challenge for both property managers and drivers. ASG wanted to forecast and predict parking lot capacity to optimize their parking services, improve the experience for visitors and drivers, and potentially increase revenue. They had previously used a different platform for model building, but it was costly and did not deliver the accurate predictions they needed.
The Solution
ASG turned to DataRobot’s automated machine learning platform, specifically its Time Series features, to accurately forecast the capacity of their parking lots. They had previously used a different platform for model building, but it was costly and did not deliver the accurate predictions they needed. After a successful proof of concept with DataRobot, they found that the platform could generate more accurate predictions and was easier to use. The ultimate goal is to make carpark availability information accessible to drivers through an app. This would allow drivers to find which buildings around them have hourly parking spots available, providing a new revenue stream for ASG and a better parking experience for drivers.
Operational Impact
Quantitative Benefit
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