In the framework of the SASMob project, traffic on the City Centre Bridge has been measured for over a year. This not only means the ability to count the number of vehicles travelling over the Tisza River: the number of passengers on a given trolleybus is also known. Data are accumulating, and different data sources are used in the project.
Traffic on public roads is measures with the traditional, camera-based method, and the data can be made even more detailed using wifi-based measurements. There are even experimental vehicles equipped with sensor systems. In other words, cameras and wifi are used simultaneously to measure traffic – but, before you get scared, the images made by the cameras are transformed into undecryptable sets of numbers (representing the numbers of passengers getting on and off) already on the trolleybus, and only these sets of numbers are forwarded.
However, that is not the only way we are counted: there are trolleybuses whose self-weighing is part of their telemetry: based on the change in the vehicle’s mass, an estimate with an accuracy of more than ninety percent can be made about the number of passengers travelling in the vehicle.
We already have a set of millions of data, we have managed to compile a very valuable database: we know how many people were travelling on a particular vehicles at a given time.
Vilmos Bilicki, Assistant Professor at the Software Development Department of the Institute of Informatics, Faculty of Sciences and Informatics of Szeged University.
In the SASMob project, camera-based data collection separates the numbers of pedestrians, cyclists, passenger cars, community transport vehicles and trucks crossing the bridge. This counting has been done for two years now, which means that the entire project has over fifteen million data sets.
This is a sufficiently large amount of data to “forecast” future events, i.e. to say how many more people will use their car in rainy weather. It makes it easier to estimate the size of the traffic jam on the bridge. Not many such deep data sets exist in the whole world.
And these data sets accurately reflect the consequences of the decisions made in connection with the coronavirus pandemic: free parking, the introduction of online teaching, changes in timetables. – The data sets show that students have stayed at home. Separate periods can be examined, and these changes can be highlighted.
In the course of the first wave, traffic decreased significantly during the curfew restrictions. By now, traffic has become far more intensive.
But still lower than it used to be before the virus problem began. And the data sets also reflect free parking and school closures.
As regards parking, what will be interesting is to see how we have parked, i.e. there will be hypotheses about the time periods when people were looking for parking lots.