The City of Athens is the capital city and largest Municipality in Greece and serves a population of about 750,000 people, swelling to almost 3 million when daily commuters are included. The City has a complex and aged infrastructure (roads, water pipelines, streetlights, public spaces, public buildings, garbage collection facilities, etc.). Furthermore, as one of Europe’s largest metropolitan areas, Athens must deal with significant traffic flows in the city, which must be handled in cost effective and environment efficient ways, but also in ways acceptable by the citizens.
The pilot aims at developing, demonstrating and evaluating data-driven, citizen-centric and evidence-based policies about the maintenance of the city’s infrastructure and the citizens’ transport and urban mobility, including the economic implications of these policies.
This use case aims to develop and validate policies for the optimal allocation of maintenance resources (e.g., workers, vehicles, materials), as well as for scheduling of different activities in the city. The production of policies will take into account different parameters such as the type of infrastructure, the time needed for repair, the frequency of maintenance problems of specific types and in specific locations, possible bottleneck, as well as citizens’ feedback on their satisfaction from the maintenance process and outcomes.
Infrastructure maintenance, resource allocation, process optimisation, citizen satisfaction
This use case is based on the execution of AI analytics over transport data (notably parking information) as a means of providing citizens with prediction about parking availability in the different areas of the city. Taking into account historical data about parking spaces availability, fines imposed, fares paid and more, the AI4PublicPolicy tools will be used to recommend to citizens optimal tactics for finding parking spaces (e.g., locations with high-availability), as well as for optimising their parking payments (i.e. fares during specific times of day and for specific locations). Citizens’ feedback will be solicited in order to fine-tune the data-driven policies and the related recommendations. Likewise, the VPME will be used to identify policies for the creation/allocation of parking spaces for the citizens (i.e. identifying areas where there is sufficient availability of spaces and other locations where more parking zones are needed to be created).
Parking space availability, parking optimization, citizen satisfaction, parking payment, parking fine
This third use case will expand the previous two use cases with information about the city’s revenues from the parking and the maintenance planning. In particular, the policy models of the previous use cases will be augmented with fiscal/monetary parameters in order to identify policies that maximize the city’s revenues from the parking service, while minimizing the cost of the maintenance, service and repair activities.
Revenue streams, fiscal parameters, cost-benefit