AI4PublicPolicy / Athens, Greece

Management & Optimisation of City Resources

📍Athens, Greece

About the pilot

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.

Poster

Use Cases

Summary

  • Maintenance Policies Optimisation: 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.
  • Predictive Citizen-Centric Transport/Parking Policies Development: 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 optimizing 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).
  • Economic/Revenue Policies Modelling: This third use case expands 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.

Detailed description

Use Case #1
Use Case Name Maintenance policies optimization
Summary This use case will develop and validate policies for the optimal allocation of maintenance resources (e.g., workers, vehicles, materials), as well as for scheduling of different maintenance activities in the city.
Description This use case will 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 for the maintenance process and outcomes.
Value Proposition(s)
  • Reduce time in resolving reported incidents
  • Reduce the average cost per incident for the city
  • Increase citizen satisfaction for the infrastructure maintenance activities
Keywords Infrastructure maintenance, resource allocation, process optimization, citizen satisfaction
Use Case #2
Use Case Name Predictive Citizen-Centric Transport/Parking Policies Development
Summary This use case will be based on the execution of AI analytics over transport data (notably parking information) as a means of providing citizens with predictions about parking availability in the different areas of the city, as well as providing to the city a tool for optimal use of city-parking resources.
Description This use case will be 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 optimizing 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).
Value Proposition(s) ▪       Increased citizen satisfaction from the smart parking activities

▪       Increased revenue from parking

▪       Improved average parking availability

▪       Improved “fill rate” and occupancy for the parking positions.

Keywords Parking space availability, parking optimization, citizen satisfaction, parking payment, parking fine
Use case #3
Use Case Name Economic/Revenue Policies Modelling
Summary 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.

User Stories

Policies for the optimal allocation of maintenance resources

User

Stories ID

As a «type of user», …

Identify the costumer job(s) to which this user story relates.

… I want «some goal» …

Describe the intended goal that the user expects to be fulfilled.

… so that «some reason».

Identify the reason(s) to which this user story relates.

US01 As a Head of Urban Planning Dpt I want to see a prediction/forecast of the completion time for each type of incident so that I can provide a data driven feedback to citizens and the head of operations regarding the completion time
US02 As a Head of Urban Planning Dpt I want to have a prediction of the incidents frequency per season of the year in order to be able to plan the seasonal personnel according to forecasted needs
US03 As a Head of Road Construction, Sewerage and Public Spaces Management Dpt i want to be able to set a route of incident fixes locations based on shortest path principle so that i can optimize the personnel moving and vehicle costs and minimise the resolution time.
US04 As a Head of Road Construction, Sewerage and Public Spaces Management Dpt I want to receive feedback from citizens about maintenance issues handling So I can improve the issue response time and quality
US05 As a City Employee (in the Dpt of Road Construction, Sewerage and Public Spaces Management/Urban Planning/Urban Green Spaces and Public Lighting I want to have a prediction of incidents occurrence frequency So I can plan equipment / materials purchases in optimal prices (economies of scale, etc.)
US06 As a City Employee (in the Dpt of Road Construction, Sewerage and Public Spaces Management/Urban Planning/Urban Green Spaces and Public Lighting I want to have an overview of the predicted maintainance needs in city assets (e.g. roads, lightning, pedestrian etc) So I can schedule activities more efficiently
US07 As a Head of Dpt of Road Construction, Sewerage and Public Spaces Management/Urban Planning/Urban Green Spaces and Public Lighting I want to have an output report on estimated predictions for the timeline, human resources and cost of maintainance of the city So I can provide maintenance services proactively and increase QoS life in the city
US08 As a citizen I want to be informed of the outcome of my complaint / report So I can be sure my complaint has been addressed
US09 As a City Worker I want to have a schedule of the foreseen maintenance and other repairing issues So I can estimate and manage my weekly/monthly workload
US10 As a Head of Dpt of Road Construction, Sewerage and Public Spaces Management/Urban Planning/Urban Green Spaces and Public Lighting I want to have a real-time idea of the teams (e.g. workers, vehicles) that are on the field. So i can optimal scheduling of maintenance. E.g if there is something to do near the team on the field, the scheduling could be readjusted
US11 As a Head of Dpt of Road Construction, Sewerage and Public Spaces Management/Urban Planning/Urban Green Spaces and Public Lighting Classify the incident by its maintenance urgency So I can prioritize maintenance activities

Policies for parking space management and urban mobility

User

Stories ID

As a «type of user», …

Identify the costumer job(s) to which this user story relates.

… I want «some goal» …

Describe the intended goal that the user expects to be fulfilled.

… so that «some reason».

Identify the reason(s) to which this user story relates.

US12 As a Citizen Driver I want to have credible information on available / crowded parking spaces in the city So I can choose the right parking area and minimize the time looking for a parking space
US13 As a Head of the Municipal Police I want to know the areas with high/low parking space requests So I can allocate parking spaces between guests / residents more efficiently
US14 As a Head of the Municipal Police I want to know the duration of each parking space occupation per area So I can allocate parking spaces between guests / residents more efficiently
US15 As a Head of the Municipal Police I want to know the allocation of parking fines per day/time So I can allocate municipal officers patrol more efficiently
US16 As a Citizen Driver I want to have a forecast when I will have a free space. so i can decide to wait or find another parking
US17 As a Citizen Driver I want to know the fares applied by the parking fines (per day/time) so I can decide if I parking due to the nature of my appointment
US18 As a Citizen Driver I want to get a parking recommendation (e.g. cheaper, closer) depending on a specific location So i can decide the parking according to my specification
US19 As a Citizen Driver i want to reserve a parking space So I don’t need to waste time looking for a parking space.
US20 As a Policy Maker I want to have parking spaces available for people with reduced mobility So i can provide a better service to people with reduced mobility by always parking where they want
US21 As a Policy Maker I want to have parking spaces for different types of vehicles (e.g. cars, motorcycles) So i can offer service to different types of user and optimize my parking spaces
US22 As a Citizen Driver I want to define my personal parameters (e.g. reduced mobility, type of vehicle) So i can access the park depending on my situation and benefit from quick and fair access
US23 As a Policy Maker I want to know the areas with high/low parking space requests per time of day So I can optimise fare prices and maximise Municipality revenue
US24 As a Policy Maker Adapt the parking according to the type of demand, for example 1 space for a vehicle with reduced mobility = 1 car space + 1 motorcycle place = 4 motorcycle spaces Optimize parking space according to demand
US25 As a Policy Maker Attracting new customers to parking and motivating current customers by offering more advantageous prices e.g. discount at times when the parking has low space occupation and discounts if they reach x parking per month Increase the satisfaction and adherence of new customers
US26 As a Policy Maker Proactive / smart
maintenance depending on the pattern of incidents, feedback on the beginning of problems, estimated time established by the manufacturer and so on
So  i can minimizing the cost of the maintenance, service and repair activities

Co-creation Workshops

1st Co-creation Workshop

Discover more about the first co-creation workshop organised by DAEM on 13 July 2021 at Serafeio Complex in Athens.

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