AI Blog

Is this the death of big data?

The era of big data is ending. This might seem like a crazy thing to say at a time when machine learning, deep learning and data science are being integrated in ever more businesses and institutions. But the truth is that the AI community is already moving beyond big data.

How to use random forests to predict mobility patterns

In the previous article, we extracted a set of nine flow patterns from a two-year dataset (2018-2019). However, now there is no direct mapping between the day of the week and patterns. The rules for assigning a pattern to each day are more complicated. Obviously, if we have the measured flow of each day, we can just calculate the distance between each day and each pattern and there we have it. However, the true value lies in doing it in advance, without the measured flow. We can use all the work that we saved in using clustering to extract patterns when we need to predict how to assign a pattern.

Understanding mobility using unsupervised learning

Unsupervised learning is a great tool for data analysis, particularly for understanding how mobility has changed due to COVID-19. We’ve used a dataset from a real city consisting of traffic flow from 445 loop detectors over a four-year period from January 1st 2017 to May 24th 2021.

Metric selection for incident detection systems

Classification problems with highly unbalanced datasets, such as incident detection, pose the trade-off between true positive rate and false negative rate. A key point for choosing the final trade-off is the selection of the metric to evaluate the performance of the system, and this decision must be made by putting oneself in the skin of the user and asking if the chosen metric and the corresponding results are representative of the concept of usefulness.

How to extract patterns from traffic data for better insights into mobility

Mobility patterns are a cornerstone of mobility demand modelling. The idea is to estimate patterns from mobility observations, such as vehicle counts, and use them to build different demand models that explain or emulate people’s mobility needs. In other words, to understand when, why and how people want to go from A to B, we need to start gathering mobility observations and find patterns.

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