Mobility pattern prediction
Unlike purely data-driven methods, a simulation-based approach enables prediction of traffic states under recurrent conditions but also to predict the impact of incidents or changes to traffic control plans.
Tag: AI Blog
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.
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.
How to extract patterns from traffic data for better insights into mobility
Mobility patterns are a cornerstone of mobility demand modelling; clustering helps us extract usable daily patterns from huge sets of traffic data.
