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Optimizing Public Transport through IoT: A Case Study of SAGULPA
Technology Category
- Infrastructure as a Service (IaaS) - Public Cloud
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cities & Municipalities
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Autonomous Transport Systems
- Smart City Operations
The Challenge
The Sociedad Municipal de Aparcamientos de Las Palmas de Gran Canaria (SAGULPA), a public transport company, was facing a challenge of managing and optimizing their mobility resources. They had access to multiple location data sources but lacked a unified platform to organize and analyze this information. The company wanted to better understand the mobility patterns in the city, including the distribution of journeys, the profile of the travelers, and the usage of their services. The goal was to use this information to improve their services, reduce traffic congestion, and promote sustainable transport solutions.
About The Customer
The customer, SAGULPA, is a public transport company established in 1993 by the City Council of Las Palmas de Gran Canaria. The company was founded to address the shortage of parking spaces and reduce traffic intensity in the city. Over the years, SAGULPA has taken a proactive role in combating excessive energy consumption and emissions. They have launched several projects to promote electric mobility, including the governance of the city’s public bicycle service, Sítycleta.
The Solution
To address this challenge, SAGULPA collaborated with CARTO, Vodafone, Sítycleta, Ayuntamiento de Las Palmas de Gran Canaria, and Guaguas to build a comprehensive data platform. This platform was designed to process large amounts of data from various sources, including municipal buses, public bicycle service, and parking spaces. The data was displayed in a dynamic, visual, interactive, and map-based interface, allowing for detailed analysis by filtering data layers. CARTO's platform was chosen for its ability to analyze commuter patterns through origin-destination matrices and spatial models, and for its capacity to enrich first-party data with external datasets for deeper insights.
Operational Impact
Quantitative Benefit
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