Superlinear’s “AI for Sustainability: in Practice” series
The transportation sector accounts for 17% of global greenhouse gas emissions, equivalent to 7.3 billion metric tons of CO2 annually. It is an excellent example of an area where improvements are urgently needed. AI can play a significant role in mitigating the negative impacts of transportation.
Machine Learning Engineer, Robbe De Sutter, presented several compelling use cases for employing AI in demand forecasting for the transportation sector. Watch his presentation below, or continue reading instead ⬇️
Demand forecasting: An overview
Demand forecasting is a technique that uses historical data to predict future demand, helping companies improve customer experience, optimize resources, and reduce carbon footprints. AI-powered demand forecasting can be particularly useful for the transportation sector, as it can provide accurate estimates of the number of passengers for different types of transportation. These estimates can assist in better resource management and reduce emissions.
AI as a co-pilot
Instead of replacing human decision-makers, AI can be viewed as a co-pilot here, providing valuable insights and recommendations to experts in the field. For instance, AI-generated forecasts can indicate if a train requires more wagons during peak hours or if a bus with no passengers should remain idle. The expert can then make the final decision based on the information provided by the AI system.
AI for transportation in action
Case study 1: Predicting expected occupancy
One of the primary applications of AI in demand forecasting for transportation is predicting expected occupancy. This helps in determining how many people will be using a specific mode of transportation, which in turn assists scheduling managers in creating more efficient schedules. By providing more transportation options where needed and reducing wasteful practices, we can improve passenger experiences and reduce emissions.
Case study 2: Dynamic pricing
Another way to apply demand forecasting is through dynamic pricing. Instead of increasing prices during high-demand periods, as ride-hailing companies such as Uber and Lyft do, transportation providers can offer discounts during off-peak hours. Encouraging people to use public transportation during these hours can reduce traffic congestion and promote a more sustainable transportation system.
Case study 3: Resource allocation
We can also employ AI-based demand forecasting in resource allocation. For example, we created a solution for Brussels Airport Company to predict the number of people with reduced mobility needing assistance. By having accurate forecasts, the airport could allocate the necessary resources, such as wheelchairs and staff, to provide a better experience for these passengers. This efficient resource management not only benefits the passengers with reduced mobility but also improves the overall travel experience for all.
Embracing AI for a greener future
AI has the power to help improve resource efficiency, reduce waste, enhance scheduling, and contribute to a more sustainable future. By harnessing the capabilities of AI, we can create a transportation system that is both efficient and environmentally friendly!
Are you ready to welcome AI as your copilot in making more informed decisions that also benefit our planet? Don't hesitate to reach out to Superlinear!
