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How NS Can Use AI to Optimise Station Areas

NS case

Transport

  • How can it optimise its train station areas?

  • How can it automate its yearly count of cars parked at its 400 train stations?

  • How can NS work smarter with the new data & analytics technologies available?

  • NS automatically receives and analyses aerial images for every NS station

  • Information on car parking utilisation is collected cheaply and more often

  • NS can finetune its car parking facilities

AI, Data & Analytics

How NS Can Use AI to Optimise Station Areas

Nederlandse Spoorwegen (NS) is the main passenger rail service in the Netherlands. As part of its services, it operates, manages, and develops all train station areas in collaboration with various partners. In these partnerships, exchanging information about the use of the premises is key. Every year, NS carries out a manual count of all the cars parked at its 400 train stations—a process that's both time-consuming and expensive. Could NS use new technologies to make this task more efficient?
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Business Challenges

NS is keen to explore the use of new technologies, so they’ve signed up for Emixa’s annual Advanced Data & Analytics Hackathon. During the initial discussions, our team of consultants worked with NS to evaluate several ideas. The concept of automating the counting of parking space usage emerged as the most promising in terms of both added value and feasibility. With the data and technologies available, could we create a working prototype in under 48 hours? The answer: yes, we can!

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Keys to Success

During the hackathon, the team utilised publicly available aerial data alongside specific data provided by NS. The consultants employed the latest AI recognition model, YOLOv8, to count the number of parked cars using aerial imagery. Through a combination of creativity and expertise, the team successfully developed a working prototype.

Following the hackathon, the consultants further enhanced the prototype by incorporating additional data sources, using highly precise coordinates and more detailed aerial photos. Our team developed an automated Python script capable of retrieving and analysing images for every NS station. Additionally, the information can now be accessed more easily.

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Results

Information on car parking utilisation rates can now be collected automatically and more frequently. This will help NS finetune its car parking facilities and further improve its train stations’ facilities, together with its partners.
Header NS Case (960 x 353 px) (1)

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