Artificial Intelligence – Think Transportation

Services Category: Artificial Intelligence

The term traffic AI refers to applications of Artificial Intelligence (AI) and Machine Learning (ML) in traffic systems. Traffic AI systems collect and analyze traffic data, generate solutions, and apply them to the traffic infrastructure.

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Traffic Engineering

Traffic Engineering aims at the whole picture in order to maximize traffic flow and reduce instances of congestion. Think Transportation aims at devising solutions to maximize traffic handling capabilities and capacities of highways and intersections.

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Transportation Data Analytics

Improve living standards of people by reducing traffic congestions, optimizing traffic routing and increasing urban mobility. Reduce transit and delivery route delays, decrease fleet operational costs, increase asset transportation capacity for your logistics operations.

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Autonomous Vehicles

Accelerate your journey to a safe ADS with defined & regulated guidelines and principles. From Smart Drones to Autonomous Vehicles, we utilize the power of big data, data warehousing and deep learning with extensive testing and validation to provide you with complete modular solutions to your specified tasks.

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Smart Cities

Smart cities bring together infrastructure and technology to improve the quality of life of citizens and enhance their interactions with the urban environment. Our Research and Development Team has developed solutions to smartly resolve novel problems of modern cities.

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Airports

Think Transportation offers consulting services for planning and optimization of airport operations with a focus on Terminal Planning, Airport Processes as well as the management of complex airport operations relating to logistics and transportation.

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Logistics

Logistics is an integral component of Supply Chain Management. It involves the planning, operation and management of freight from the supplier to the consumer.
Think Transportation has matured experience in logistic planning, operation and management, having been involved in projects and worked with major industries and logistic companies.

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Data Collection

Annotated and verified data is necessary for any supervised deep learning model development pipeline, without which model training would lead to slower convergence & unnecessary edge-cases and creating unknown errors throughout the development cycle.

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