AI4Cities | Mobility

Mobility

AI4CIties challenges suppliers to come up with AI solutions that reduce CO2 emissions in the mobility field. With over 70% of EU citizens now living in urban areas, traffic congestion and inefficient transport systems still persist across the continent and account for 24% of GHG emissions in European cities. AI technologies have already proven they can help optimise traditional transport systems and processes to have a more positive (potential) impact on the environment.

Cities participating in the Mobility challenge: Amsterdam, Helsinki, Paris, Stavanger, Tallinn.

MOBILITY DATA SOURCES

Subchallenges

The purpose of these sub-challenges is to describe and give examples of the cities’ current needs. It is not expected that a solution will cover every issue mentioned under a sub-challenge. On the other hand, solving only one of the issues listed may not be enough for the solution to advance to the later phases of the PCP. Tenderers are encouraged to address several problem areas mentioned under one or multiple subchallenges.

Mobility-as-a-Service

The aim of MaaS is to provide an alternative to the use of private cars, which woudl help to reduce traffic congestion and volume constraints in urban transport capacity. MaaS is the integration of various forms of transport services into a single mobility platform accessible on-demand with real-time predictive information concerning multimodal itineraries and multimodal positioning. Hence, MaaS by definition is a) multimodal, because it has to include more than one mode of mobility service and b) it is user-centric as a tailor-made service suggests the most suitable solutions based on the user’s travel needs.

Although many MaaS initiatives currently developed are at a local level, the coexistence of MaaS platforms with different geographic footprints is likely to become a reality as we move towards larger convergence platforms across technology domains.

Adressable problem areas (non-exhaustive list):

High usage of private vehicles within city boundaries
Relatively low attractiveness of public transportation
Relatively low engagement in active mobility activities
Sub-optimal placement of city bike terminals, e-scooters and parking spaces (e.g. they are not well-integrated within the public transport network)
Lack of accessibility in transportation systems and public space (i.e. citizens with disabilities, senior citizens etc.)
Lack of real-time multimodal information for citizens, integrating public transport, user-specific routing,on-demand parking, active mobility and unified payment systems
Lack of understanding and real-time prediction of passenger behaviour
Lack of motivation among citizens to change their habits, due to low awareness of the impact of their mobility choices on their carbon footprint.

Traffic Flow Optimisation

The continuous growth of traffic, while a positive economic indicator, has created severe problems for city authorities. Many cities across the world have layouts that were planned decades ago – or in some cases, centuries
ago. The rate of growth of vehicle use has exceeded the most optimistic planning assumptions, which has led to congestions, which in turn has increased fuel consumption and CO2 emissions. Furthermore, cities with busy ports need to look for novel solutions in maritime transport, while a razor-sharp focus is also required for optimising new alternative transportation options like drone traffic. As changing the city infrastructure in conjunction with these challenge domains is both resource- and time-intensive, we must look elsewhere for innovative smart solutions.

Adressable problem areas (non-exhaustive list)

Optimising the use of existing road infrastructure
Integrating different infrastructure domains such as road, air and water
Predictive traffic planning (e.g. taking into account the effect of major public events)
Using quantitative traffic data analysis as input for long-term traffic planning and real-time traffic control
Using hyperlocal air quality measurements to support public space design and user-specific travel planning
Addressing the shortage of reliable data sources and the under-utilisation of existing ones
Optimising the location of EV charging stations. Currently they are often located in places where there is little demand, absent in locations where they are required the most and are often used inappropriately (e.g. by non-EVs or exceeding necessary charging time)
Better understanding of the potential demand for new modes of travel and the necessity to develop a predictive usage model for these emerging mobility domains (e.g. e-scooters, e-bikes, personal vehicle sharing, etc)

Optimisation of logistics

In addition to curbing the use or private vehicles, streamlining commercial transport and urban logistics is another important aspect of moving towards climate neutrality. Commercial delivery fleets cause large amounts of traffic congestion, particularly in urban city centres. E-commerce, restaurant deliveries and parcel deliveries are on the rise. Local transport is not optimised, which causes extra costs and makes local markets less effective. This leads to a shift towards long distance sales, which causes CO2 emissions to rise.

Adressable problem areas (non-exhaustive list):

Half-empty vehicles making logistics deliveries
Bad routing optimisation, when making deliveries
Very little or no task coordination between transport suppliers (all optimization happens within a fleet)
Difficulties for consumers and producers to get a unified perspective on the CO2 emissions created by local and long-distance trade
Deliveries made during peak traffic hours leading to increases in congestion
Deliveries made by cars using fossil fuel
Inaccessibility of local goods based on rational and optimized transport
Poor mapping and usage of potential data sources
Higher costs to all parties in the logistics supply chain, due to all of the above
Inefficient use and coordination of maritime and river transportation
Citizens using private cars for shopping trips, when coordinated and efficient logistics solutions would cause less CO2 emissions and save time

Wildcard

AI4Cities has also reserved a possibility for the suppliers to independently propose a solution for a mobility challenge that we have not defined. In their bid, the supplier must outline the specific sub-challenge that they have
identified and the proposed solution. The solution must still satisfy the general criteria: CO2 and other GHG emissions reduction and the use of AI technologies.

Mobility

Subchallenges

Newsletter?

Follow?