Management & Optimisation of City Resources
đź“ŤAthens, 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.
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) |
|
| 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) | â–ŞÂ Â Â Â Â Â 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 |
No Comments
Sorry, the comment form is closed at this time.