Researchers at the Complexity Science Hub have developed a machine learning model that can predict traffic activity in different zones of cities. The model uses data from a major car-sharing company in Italy as a proxy for overall city traffic. The data includes the location of all cars in their fleet in four Italian cities (Rome, Turin, Milan, and Florence) in 2017. The data was obtained by constantly querying the service provider's web APIs, recording the parking location of each car, as well as the start and end timestamps. This information allows researchers to identify the origin and destination of each trip.
Predicting city traffic to improve urban traffic flow
Understanding how different urban zones interact can help avoid traffic jams and enable targeted responses of policy makers, such as local expansion of public transportation. "As populations grow in urban areas, this knowledge can help policymakers design and implement effective transportation policies and inclusive urban planning," says Simone Daniotti of the Complexity Science Hub. If the model shows that there is a nontrivial connection between two zones, services could be provided that compensate for this interaction. If the model shows that there is little activity in a particular location, policymakers could use that knowledge to invest in structures to change that.
Accurate spatio-temporal forecasting and anomaly detection
The model not only allows accurate spatio-temporal forecasting in different urban areas, but also accurate anomaly detection. Anomalies such as strikes and bad weather conditions, both of which are related to traffic. The model could also make predictions about traffic patterns for other cities such as Vienna, however, this would require appropriate data.
Outperforming other models
While there are already many models designed to predict traffic behavior in cities, "the vast majority of prediction models on aggregated data are not fully interpretable. Even though some structure of the model connects two zones, they cannot be interpreted as an interaction," explains Daniotti. This limits understanding of the underlying mechanisms that govern citizens' daily routines. Since only a minimal number of constraints are considered and all parameters represent actual interactions, the new model is fully interpretable.
Interpretation is important
"Of course it is important to make predictions," Daniotti explains, "but you can make very accurate predictions, and if you don't interpret the results correctly, you sometimes run the risk of drawing very wrong conclusions." Without knowing the reason why the model is showing a particular result, it is difficult to control for events where the model was not showing what you expected. "Inspecting the model and understanding it, helps us, and also policy makers, to not draw wrong conclusions," Daniotti says.
Journal Information: Simone Daniotti et al, A maximum entropy approach for the modelling of car-sharing parking dynamics, Scientific Reports (2023). DOI: 10.1038/s41598-023-30134-9