Challenges encountered

Today, transport and parking operators are faced with many difficulties, including the lack of data centralisation or the difficulty of public services to harmonize, leading to an overall reduction in efficiency, the complexity of communication between services in large networks (large number of actors and flows), and the constant expectations of consumers.

These challenges affect the ability of companies to manage all their resources and to optimise their services by their own means, resulting in a significant loss of earnings. By centralizing their data, these companies could optimize their profits and improve their services. The problem is that the data is scattered: the control agents write the reports by hand, the managers manage their teams through an Excel file...

How to respond to these challenges?

The use of our AI platform, called OctoCity®, allows you to centralize all your data on an intuitive interface. Use our turnkey tool to have a global view of your resources (human resources, materials...), in order to reinforce decision support and optimize the efficiency of your services. Optimize your urban services through our secure data modeling and valuation platform.

With the enhancement of your data, Datategy is able to:

• Predict the failure of parts on vehicles and determine the cause so that they can be replaced preventively.

• Identify people with suspicious behaviour for preventive control

• Analyze accident data to predict the elements likely to cause them

• Determine the performance of your employees and identify what enables them to perform better

• Identify and predict the number of users in order to anticipate possible slowdowns and propose a better itinerary.

• Calculate the best ticket fare

• Offer optimized user services

• Propose optimal itineraries for control officers

• Minimize costs, maximize the capacity of human and material resources to improve financial margins

Use cases

Smart City: Trend analysis of city indicators

Aggregate the data available in the city (transport, security, energy...) and make decisions on predictive data.

Public transport: Increase the rate of user payment

Reduce the number of fraudsters, whether they are active (no ticket) or passive (forgot to validate).

They trust us