The future blog of the Machine Vision People

Urban traffic is one of the major challenges of the future. Today, already more than 50% of people live in cities, and this figure will rise to around 70 percent by 2050. This means, of course, that traffic will also increase dramatically – and urban mobility must be prepared for this change. If this infrastructural adjustment through modernization, technological possibilities and matching traffic management strategies does not take place, the consequences will not only affect the attractiveness and livability of cities, but will also be severe in financial and safety terms.

What exactly these consequences can look like is apparent from some current numbers: For example, urban mobility and road congestion currently cost the EU an estimated €110 billion per year. And they are also one of the main causes of air pollution worldwide. It is therefore very likely that legal entities such as the EU will sooner or later impose fines if these conditions are not addressed and certain climate targets not met. Not to neglect the costs that would result from health problems of residents due to the air and noise emissions, as well as costly damages resulting from environmental disasters for which the cities could be held partly responsible.

Indirect costs also arise from inefficient mobility. Studies indicate that a 10% reduction in journey times can increase productivity by 2.9% and that in heavily congested regions such as cities, flowing traffic can mean productivity gains of up to 30%.

Conversely this also implies that a high level of congestion extremely limits productivity.
And another aspect that will hit urban areas hard as traffic increases is safety. Road traffic is already responsible for 1.3 million deaths per year. What will happen when in 25 years' time there will be around 20% more road users in urban traffic?

This lack of safety, the inefficiency of mobility and environmental pollution would extremely reduce the livability in cities and lead to an urban exodus – and thus to a decline in retail trade and the associated tax revenues.
Taking all this into account, it becomes clear why roads (together with the entire infrastructure) are often referred to as the lifelines of a city. If the further development of urban mobility comes to a halt, this will have far-reaching economic, ecological and social consequences.

So, how can this be avoided?

Urban vehicle access regulation as a key measure

In order to prevent these scenarios and drive forward the inner-city traffic transition, there are various approaches. One of them is speed reduction, which is particularly effective in terms of air pollution (more on this and the technical requirements here). In this blog article, however, the focus will be on a different approach, which is to reduce traffic – without restricting the mobility of residents.

There are three levers to address this: reducing car traffic (especially, but not only, in the private sector), shifting traffic to environmentally friendly means of transport and improving traffic and transportation systems and their management.

One measure covering all of these points is the urban vehicle access regulation or UVAR for short. This refers to policies and measures implemented by cities or urban areas as part of urban planning and transportation strategies to regulate vehicle access in specific zones.  These regulations aim to reduce traffic congestion, improve air quality, increase the safety of road users - especially pedestrians and cyclists - and encourage the use of more sustainable modes of transportation such as public transit, walking and cycling. Evidence that this approach works is provided by a number of studies from Germany, Japan and the United Kingdom. They presented data showing that UVARs lead to a reduction in cardiovascular disease outcomes and indicated an improvement in respiratory disease. The research also documented that the total number of injuries or car-related injuries was reduced by the access control.

A decisive advantage of UVARs that should also be emphasized is their capacity to finance infrastructure through fees and fines. In this way, they not only prevent financial losses, but also open up economic opportunities. UVARs can make an important contribution ensuring the continuous modernization of urban mobility.

The cordon approach – from clean air zones to congestion charging

One approach to implementing these vehicle access regulations is the cordon-based approach, in which areas known as cordons are defined. Here, entry and exit points are established at which vehicles are identified using technologies such as camera-based license plate recognition.

To illustrate this, three established forms are described in the following:

Low Emission Zones (LEZs) are areas where access for vehicles that do not meet specified emission standards is restricted or subject to a charge. Sometimes they are also referred to as Clean Air Zones (CAZs). The primary aim is to reduce air pollution and promote the use of clean means of transportation.

In Congestion Charging Zones (CCZs), motorized vehicles are required to pay a fixed or variable fee to enter or circulate, especially during rush hours. This measure is intended to reduce traffic congestion and also encourage the use of public transport. It additionally serves as a source of revenue to finance infrastructure, for example.

Environmental Charging Zones (ECZs) describe a complex concept that uses dynamic pricing methods for all modes of transport. The objective is to calculate and charge mobility prices for all modes of mobility based on the environmental impact of the selected transportation mode or the capacity of use to ensure fair pricing of all types of mobility. This means that, in contrast to the LEZ and CCZ, the ECZ also incorporates the prices of public transport.

Both approaches (CCZs and ECZs) are also known as road pricing or mobility pricing.

The needs and benefits of the different UVAR approaches

When considering the different UVAR solutions, it is noticeable that the complexity increases from LEZ to ECZ. Although they all use similar roadside technologies for automatic license plate reading, there are major differences in the software requirements.

While LEZs get by with pure incident documentation, charging zones demand centralized systems for user management and the payment process. In addition, the back office software for these approaches must also enable user interaction, such as drivers registering their vehicle or paying for a vehicle pass, etc. It is therefore easiest for cities to start with the LEZ when introducing UVAR.

Enforcement for maximum effect

But regardless of the type of UVAR, one thing is always of crucial importance: Without effective enforcement, their intended effect largely fails to materialize, which also means that the negative consequences mentioned above are not prevented. Restricted access zones or low emission zones will still be used by unauthorized vehicles, and road and mobility pricing cannot be reliably implemented.

But how can efficient monitoring be achieved? The simplest solution may initially appear to be enforcement through personnel. However, for manual controls to provide a high penetration rate and thus effectiveness, an extremely large amount of manpower would be required. The solution of choice, as indicated in the previous passage, should therefore be the application of automated enforcement technologies.

The possibilities that modern enforcement systems offer cities in this respect are substantial. For example, current AI-based systems have very high detection rates and enable automatic enforcement via license plate reading (ANPR). This enables effective enforcement of UVARs by automatically recording violations, which has a strong deterrent effect that leads to the desired change in driving behavior.
The previously mentioned zones with payment models can also be implemented efficiently using this kind of enforcement technology. In these cases, an ANPR solution can be used to detect the entry or exit of a vehicle and to initiate the billing process. If an advanced charging model is desired, a GNSS (Global Navigation Satellite System)-based system – processed via smartphone apps or On Board Units – can be integrated to track the route or distance traveled in a defined area.

One solution, many applications

In view of the diverse urban requirements for traffic control and the limited budgets and resources of many cities, the AI-based systems described above have another decisive advantage. They can be deployed in many different applications, making additional investment and effort almost obsolete. To name a few of these applications: some systems are additionally suitable for capturing offenses like red light violations, the use of mobile phones while driving or missing seatbelts.  And there are more potential areas of use in traffic safety, management and control. For example, certain of these solutions can detect vulnerable road users and count vehicles or vehicle classes.

Moreover, the flexibility of modern enforcement systems is not limited to the type of application; the deployment options also offer a great scope. Just one device can be enough to monitor several lanes simultaneously and some systems can be operated mobile, semi-fixed or stationary on existing infrastructure making cost-intensive road construction unnecessary.

One issue that makes many people hesitant about automated enforcement technologies, despite their obvious advantages, is data protection. But here too, modern systems are at the cutting edge. Compliance with data protection standards, tamper-proof case data and a high degree of legal certainty are always considered for example by using the latest encryption technologies.

Conclusion

IN SHORT

  • Modernizing urban traffic prevents negative economic, environmental and social consequences
  • Measures for sustainable urban traffic require enforcement to be effective
  • AI-based enforcement systems are the solution of choice due to their very high detection rate and automatic license plate reading (ANPR)
  • AI-based enforcement technology has a wide range of applications, from supporting sustainability measures and capturing offenses to improving traffic management

Summary

Cities require mobility solutions such as UVARs to cope with urban migration and the resulting increase in road users. Each of the three variants – Low Emission Zones, Congestion Charging Zones and Environmental Charging Zones – has its own benefits, but what they all have in common is the need of effective automatic enforcement. Only then can these measures become real drivers of the urban traffic transition and solutions to traffic congestion in cities. AI-based enforcement systems in particular are suitable in this regard due to their precision, versatility and efficient application options. As a result, they not only promote the sustainability of traffic and the livability of a city, but also prevent the economic damage that can result from neglecting the modernization of mobility.

For cities to benefit as quickly and efficiently as possible from enforcement technologies for UVAR and beyond, they should rely on experienced full-service providers such as Vitronic. This makes the introduction of these systems more manageable and streamlined. Hardware, software, implementation, operation and maintenance from a single source significantly supports the success of these solutions.

Quellen:

https://www.eca.europa.eu/lists/ecadocuments/ap19_07/ap_urban_mobility_en.pdf

https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(23)00120-2/fulltext

https://unstats.un.org/sdgs/report/2022/goal-11/

Marc-Pascal Lehrich

Marc-Pascal Lehrich

Business Development Manager for New Mobility & Smart City
E-Mail
marc-pascal.lehrich@vitronic.com
Telefon
+49 151 6896 2219
I am Marc-Pascal Lehrich, Business Development Manager at VITRONIC, specializing in the development of new mobility and smart city solutions. Our goal is to master the challenges of the present and future with innovative technologies and thus contribute to the creation of safe, liveable urban spaces.

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