Teradata > Case Studies > Smart City Innovation for Multi-Modal Transportation Authority

Smart City Innovation for Multi-Modal Transportation Authority

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 Smart City Innovation for Multi-Modal Transportation Authority - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Data Mining
  • Functional Applications - Transportation Management Systems (TMS)
Applicable Industries
  • Cities & Municipalities
  • Transportation
Applicable Functions
  • Logistics & Transportation
  • Product Research & Development
Use Cases
  • Autonomous Transport Systems
  • Transportation Simulation
Services
  • Data Science Services
  • System Integration
The Challenge
The citywide multi-modal transportation authority was facing a significant challenge due to the city's fast-paced development and growing population. The projected demand was expected to strain the public transportation system if not planned for properly. To serve these anticipated needs, the authority drafted its Land Transport Master Plan for transportation network investments that forecasted needs over a decade. In addition to a rapidly growing population, ever-expanding data volumes posed a considerable challenge to their technology infrastructure. The authority relies on data and applications to ensure smooth travel for all—capturing more than 12 million records on public transport each day. However, land transport IT systems were designed for quick response time, with priority given to keeping transactions moving; but not for keeping data beyond three months. Lost data meant a lost ability to conduct meaningful trend analysis, create long-term policy planning, or engage in data mining.
The Customer

Citywide multi-modal transportation authority

About The Customer
The customer in this case study is a citywide multi-modal transportation authority that manages a transportation system critical for keeping its millions of citizens and socio-economic development moving. The authority oversees this task through traffic management, and regulation of private and public transit. It plans, develops, and manages for short- and long-term needs to provide an efficient, people-centered system that includes roads, rail, buses, taxis, and private vehicles. The authority is responsible for capturing more than 12 million records on public transport each day and relies heavily on data and applications to ensure smooth travel for all.
The Solution
With the expectation that data volumes would continue to grow, a decision was made to create a data management ecosystem focused on implementing new measures to better address commuter needs. Teradata initiated a proof-of-concept study to identify a suitable enterprise data warehouse that would address their growing data volumes and, more importantly, put their data to work. Several test scenarios assessing performance, scalability, and workload management resulted in a 99 percent improvement in query response times. Following these encouraging results, Teradata embarked on a full-scale implementation of a data analytics ecosystem utilizing technology from Teradata. Its implementation helped the authority set a course for creating a decision capability that supports its vision of a truly people-centric transportation system.
Operational Impact
  • Today, the data analytics ecosystem provides the transportation authority with advanced capabilities to perform large-scale business analytics, and transport simulation in the deployment of various Land Transport Master Plan initiatives. This enables planners to determine levels of efficiency and resource optimization—tracking traffic flow and behavior, passenger loading, route running times, and transfer volumes for advanced trend analysis. For the transportation authority, data-driven policy and planning decisions with fast turnaround were regarded as the critical success factors of the strategy. A sound infrastructure is the essential building block for any smart city initiative. The infrastructure must be skillfully engineered to integrate and manage a wide variety, and large volumes, of traditional and non-traditional data.
Quantitative Benefit
  • 99 percent improvement in query response times
  • Ability to capture and analyze more than 12 million records on public transport each day
  • Ability to conduct meaningful trend analysis, create long-term policy planning, and engage in data mining

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