Is digitalisation truly useful for urban mobility?

Big cities are undergoing major changes in the way citizens behave, and mobility is a good example. Nowadays, transport-sharing companies are flooding cities with cars, motorcycles, bicycles and even scooters. However, the private car seems to remain untouchable. Car ownership is breaking records and has hit maximum levels in countries such as Germany, Italy, Spain, UK or Portugal. In other countries such as Czech Republic, Lithuania, Hungary, Luxemburg or Turkey, the motorization rate has increased at an annual average rate of more than 4% since 2014. Right now, public administrations have a big challenge ahead: how is everyone going to ‘fit’ in the cities of the future?

Technology has definitely gained momentum, and on-street parking management has already started to take advantage of it. In the past, an ‘army’ of hundreds of traffic wardens patrolled the streets to control city payment areas and with a bit of luck, get back home “safe and sound”. This may well become something of the past with the deployment of cars capable of reading the number plates of parked vehicles thanks to their onboard OCR (Optical Character Recognition) cameras. The data from these cameras is sent from the car to an enforcement back-office and is then cross-checked against legitimate parking rights for any violation of the parking rules. The need for parking agents on the street can be drastically reduced and the efficiency improvement achieved by this system is great: one single parking agent can check about 200 vehicles per hour, while a scan-car can reach 2,200 readings per hour. A more efficient operation should lead to fraud reduction, an increase of vehicle rotation and, ultimately, an improvement in mobility.

However, on-street parking enforcement is just the tip of the iceberg and OCR technology has many more applications. Computer Vision enables cities to have real-time data of urban mobility, with has direct and powerful implications. It is currently being used to anticipate traffic incidents before they occur, reduce search traffic for parking spot, control the loading and unloading operations of vehicles, manage the access to city centers, monitor car emissions, build up a holistic digitalisation of urban mobility, more accurate pollution forecasts, etc.

Some studies suggest that approximately 30% of all city traffic is caused by searching for parking, which is one of the main causes for congestion. City traffic can benefit if innovative solutions are applied to reduce the number of vehicles searching for a parking space, and OCR can be highly instrumental in this regard. There are several other factors that prevent the smooth flow of traffic on the streets, such as double parking, accidents, civil works, cross-blocking, the loading and unloading of vehicles, etc.—all of which can be identified and solved more quickly thanks to digitalisation.

Madrid is a good example of how OCR technology can improve digital parking enforcement. The operation is carried out by a consortium of private companies that manage 153,000 parking slots with a fleet of about 25 cars equipped with OCR. The operation is supported by an extra 50 electric scooters, which can finally verify the situation on site. In order to make the process even more efficient, Madrid operators have developed a complex algorithm that, out of the hundreds of possible illegal plates scanned, prioritizes and selects a short list of vehicles so the scooter agent can go and check.

One thing is clear: digitalisation is already improving urban mobility and it seems that it still has a huge development potential. The real impact in urban mobility will occur when information from private cars is processed together with data (from mobile devices, public transport, public information, weather, pollution, etc.) in order to reveal integrated traffic dynamics patterns and to ultimately come up with intuitive, simple and accurate predictions of mobility.

About the authors
César Valero is a Civil Engineer, holds an MBA, and Partner at ALG
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