smart. clean. sustainable.
smart. clean. sustainable.
Clean City Lab
Maintaining the cleanliness of our cities requires substantial resources in terms of machines and personnel. AI makes it possible to objectify and optimise these efforts.
Keeping our cities clean requires substantial resources in terms of machines and personnel. Depending on the country and the city, urban cleaning can be the responsibility of the road authority (public service), delegated to private companies, a combination of these two approaches, or carried out by a public-private partnership.
The respective organizations are responsible for the quality of their service (in other words, the level of city cleanliness) and they need to face their clients. The private companies and the road authorities have responsibilities towards the elected officials, and the elected officials towards the inhabitants. According to Ayumi Kawaji, head of operational performance at Suez: "The objective measurement of the level of cleanliness facilitates exchanges between Suez and its clients or between local authorities and their elected representatives."
The control of these services is usually done by verifying that the resources are used as planned (obligation of means). However, in the end, users are more concerned by the result than by the means used. This is why Gautier Feuillepain, director of waste management for the city of Le Havre in France, renamed the "cleaning management service" to "cleanliness management": "Indeed, if we talk about cleanliness management, we make the link with the inhabitant and we discover an approach that goes beyond the traditional scope of our services."
In the relationship between the client and the organization in charge of cleanliness, this quality of service can be measured through a cleanliness index, allowing for an objective and quantified evaluation. This is an obligation of result.
Moreover, this measure of cleanliness makes it possible to identify areas that are over-cleaned or not clean enough in relation to the expected quality of service, and to commit resources where they are needed. In other words, to manage cleaning by quality and results rather than by means.
Cities that have adopted this approach have obtained convincing results, which we will describe in the present Clean City Lab article. We will focus on the optimisation of mechanised cleaning and the improvement of the cleanliness level.
As described in a previous article (1), the Clean City Index measures the level of cleanliness of a place (street, square, etc.) by a score ranging from 0 (dirty) to 5 (clean). This index can be extended to the whole city or to its street sectors (2). Regardless of the city, the definition of the index stays the same. However, the expected level of cleanliness will depend on the city's objectives. For reporting the quality of service (for example, a cleanliness level between 3.2 and 3.7), we use the following colour codes:
Uniform cleanliness throughout the city improves user perception, and over-clean areas do not compensate for dirty areas. The following examples show progressively how to control an objective level of cleanliness while increasing the overall cleanliness level (transforming red and blue areas into green) and optimising the use of resources.
Together with the VVP — Voirie, Ville Propre de Genève — we carried out a pilot project called "Geneva Clean City Lab" in one of the city's sectors (northwest of the train station). The objective of this project was to experiment, with the responsible teams, the approaches made possible by the measurement of cleanliness and to quantify the results on the ground.
This district is divided into seven zones that are cleaned according to a weekly routine schedule. The target cleanliness level is 3.5 to 4.0. Measuring the Clean City Index over a one-month period revealed that five zones were within the target quality level, while one was above (East: 4.2) and one was below (West: 3.4).
The crew chief then changed the frequency of cleaning in these two areas. In the red zone, the frequency was increased from once a day to twice a day (with the second round of cleaning only in problematic streets), while in the blue zone the frequency was reduced from once a day to twice a week.
The results were that the previously red zone changed to green and its index increased from 3.7 to 4.1. The cleanliness level became uniform. With the same amount of resources, by simply changing the frequency of use, perception as well as actual cleanliness improved.
The entire city of Utrecht participates in various initiatives aimed at building a healthy future and a city for well-being. Two teams — Innovation/Smart City and Roads — looked for a solution to improve the cleanliness of the inner city area and to reduce the need for manual sweeping.
The inner city is cleaned by two sweepers and ten road workers. The Utrecht City Council has divided it into 19 zones, which are cleaned on a routine weekly schedule. One of these areas corresponds to the shopping streets; keeping it clean is particularly important for the city. Yet this is the area with the most complaints.
As in the previous example, the initial one-month analysis identified green, blue and red zones. This time, the idea was to increase the size of the blue areas (increase the area to be cleaned with the same resources) and reduce the size of the red areas. Some areas were thus grouped together and their number was reduced from 19 to 13, which simplifies management. The frequency and duration of the cleanings were also adjusted.
The results were a cleaner shopping area, a higher overall standard and greater consistency. These changes also freed up four of the ten road workers, who can now be assigned to other tasks, such as prevention activities.
According to Sofie Berns, innovation manager for the city of Utrecht: "This is a very interesting project for the city of Utrecht, which we have since replicated in another area. It allows us to involve employees in improving their work and to free up tedious jobs for more rewarding tasks. We also meet the city's challenges of ensuring a quality living environment without increasing costs."
In the two previous examples, the changes were simply in the frequency of cleaning and the adjustment of areas. Once the level of cleanliness was improved in the Clean City Lab sector in Geneva, the responsible teams looked for options to use their machines more efficiently.
The team proposed to eliminate the need for one of the three sweepers by optimising the routes based on the measured level of cleanliness and the amount of work to be done. José Vilarino, team leader, notes: "In our first proposal, we were a bit ambitious and the drivers were not able to complete all the routes in time. We took their comments into account and the second version was well received. We are very happy with the results, as the new routes have reduced machine working hours by 20% while maintaining the same level of cleanliness." These new routes were then adapted to the three periods: "normal", "vacations" and "leaves".
For the same level of cleanliness, the adaptation of the sweepers' routes made it possible to eliminate one sweeper out of three and to reduce machine hours by 20%. This is also a saving for the environment.
The City of Basel has been actively engaged in climate protection, energy efficiency and renewable energies for many years. The city's municipal authorities set themselves a very ambitious target: switching to a 95% zero-carbon fleet by 2025. "The purchase costs of electric sweepers are still much higher than those of traditional machines," confirms Dominik Egli, head of urban cleaning. "To reconcile environmental objectives and public budgets, we had to develop innovative approaches."
Dominik Egli pursued the idea of reducing the number of vehicles in his fleet. Replacing a smaller number of vehicles would already make the operation profitable in terms of investment costs. To do this, he worked with the managers of the seven districts that make up his organisation. Each district had historically sized its vehicle requirements with reserves (spare vehicles), to account for variability in demand as well as in vehicle availability (breakdowns, maintenance, etc.). Based on measurements of the level of cleanliness, the working group then determined the minimum number of vehicles needed per district. Reserve vehicles were made available to all districts on an on-call basis. This reserve vehicle pooling reduced the number of vehicles in the entire fleet by 20%.
A change in organisation, with the involvement of teams throughout the city, has made it possible to achieve the goal of providing the same level of cleanliness with vehicles that no longer emit CO2.
These few examples show that an approach based on results management allows for a more rational and efficient organisation of resources, while improving the overall perception of cleanliness. These approaches are based on the use of a reference index that quantifies the efforts to be made and makes the results objective. They involve road workers and enhance their contribution to a better cleaned city. They also meet the expectations of those who place the orders (residents, elected officials, road authorities), while benefiting the environment. Unfortunately, these approaches are still too rare; they remain the prerogative of cities wishing to set ambitious objectives and invest in the innovation of their services. Continuous improvement and quality management is an objective that every city would benefit from adopting. We hope that these few examples will inspire other local authorities and convince them to embark on the adventure!
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