Challenges

Challenge 1

Micromobility
Micromobility

Evaluation and prediction of patterns and behaviours of micro mobility

Problem

 

Micro mobility poses great challenges in the city environment, as nowadays micro mobility is changing how citizens commute in cities. In this sense there is the need to understand and anticipate which are the spatial and temporal patterns of micro mobility commute in the city, along with parking, storage and operations of micro mobility vehicles

Expected outcome

 

Predictive model of micro mobility commute by spatial unit according with the weather predicted in the day before, proximity to schools, public services, public transportation network. The model will provide a probability of starting and ending a trip in each spatial unit. Besides the predicted commute pattern, the model results will be also useful for micro mobility vehicles operation (e.g. reinforce the number of available vehicles in spatial units, that in a certain hour have a higher probability of being registered the start of a trip)

Challenge 2

Waste management
Waste management

Identification of patterns/profiles and solid waste production prediction

Problem

 

Solid waste production and collection, is nowadays a huge challenge for the municipalities. Indeed, waste collection costs range between 40 to 60% of waste management costs and is responsible for the production of 4,2 to 12 kg of CO2 per tonne of waste. Predicting and understanding the relations between the socio-demographic characteristics and the waste production, will lead to an improvement in the operations efficiency of waste collection and transportation by the municipalities

Expected outcome

 

Identify patterns to support the prediction of the production of solid waste in regular and big events days. The predictive model of waste production will be based, according with the socio-demographic profile of the spatial units, the presence of population and services (e.g. schools, local accommodation). The predictive model will be deployed in PGIL, allowing the creation of a service to predict solid waste production a day before, to optimize waste collection in Lisbon

Challenge 3

Parking
Parking

Identification of patterns, explanatory factors and prediction of abusive parking

Problem

 

As population that lives, works and visits cities are increasing, parking capability is under pressure, namely due to unattractive or insufficient public transportation, inadequate drivers’ education and insufficient regulation. Predicting abusive parking can aid the municipality services to optimize parking inspection and dissuade possible drivers’ irregular behaviour

Expected outcome

 

Identification of patterns and prediction of illegal and abusive parking in the city of Lisbon, by spatial unit and time of day. The model will be based on the complaints about abusive parking registered in “Na Minha Rua” platform and in the car tows registered by the Municipality Police of Lisbon. The proximity to services will also be included in the model, namely the proximity to schools, health services or the proximity to cultural events

Challenge 4

Pollution
Pollution

Elaboration of propagation models for the prediction of atmospheric and liquid pollutants behaviour

Problem

 

As there is an increase in people living in cities, is growing an increase concern regarding atmospheric and liquid pollution. Indeed, there is lack of information about propagation of liquid and atmospheric pollutants so civil protection and sanitation services could understand pollutants propagation and optimize their services in case of an environmental accident. There is the need to model atmospheric and liquid pollutants propagation in the city, to assess pollution impacts in the city environment

Expected outcome

 

Models of atmospheric and liquid pollutants propagation at city micro-scale, with a 15 minutes temporal, resolution. The model will be developed using buildings 3D geometry, traffic data, road characteristics, climate data and green infrastructures data

Challenge 5

Crowd management
Crowd management

Identification of patterns and predictive modelling of the impact that the realization of marathons has on public transportation demand

Problem

 

The recent and future increase in cities population will input a big pressure in cities infrastructures, namely crowd and traffic management. More specifically, this pressure increases with the realization of special events namely marathons, carrying significant challenges in cities mobility and transportation systems. In this sense is of extreme importance to assess the impacts that crowds have in the transports system and how municipalities and transport companies can optimize their resources to face the increase of population during the realization of marathons

Expected outcome

 

Prediction by hour of public transportation demand before, during and after the realization of marathons. This prediction model will be deployed in PGIL, where it will provide a day earlier, the expected public transportation demand. This service will be used by the municipality and public transportation companies to optimize their services provided to citizens