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Artificial Intelligence in smart cities and  urban mobility

Researchers Devin Diran, Anne Fleur Van Veenstra, Tjerk Timan, Paola Testa and Maria Kirova published a Report on July 2021 (available on the internet at: regarding Artificial Intelligence and urban mobility. This report provides an up-to-date review of this issue. Here are some key issues.

Smart mobility solutions aim at increasing safety and efficiency, reducing traffic congestion, improving air and noise pollution and reducing costs. Smart solutions for mobility are also recognised as essential to further decarbonise the transport sector and reach the ambitious EU emission reduction goals.

The COVID-19 crisis has forced society to realize that it is possible to drastically change mobility habits, especially in cities. The pandemic has indeed accelerated many existing trends, including home working, increased use of individual transport modes and greater general awareness and concerns about health, safety, and environmental sustainability.

In order to fully unleash the benefits enhanced by AI, a holistic approach to public urban mobility planning and management needs to be adopted.

AI applied to urban mobility can rely on data produced by existing infrastructures (e.g. traffic controller detection, urban centrals, video data, etc.), fleet data (car probe data, eBike fleets, public transports) and also third party (public and private) data. Public sector plays a crucial role to ensure the AI solution to be inclusive and secure, counting on reliable, unbiased, fairly shared data, still preserving EU citizens’ privacy.

The next-generation mobility is expected to transform automotive original equipment manufacturer (OEM), mobility services companies, and cities; furthermore, such a transition, to be successful, requires the  mentioned actors to collaborate, engaging also financial services, insurance companies,  telecommunication and utility companies  to achieve the common goal of a new  sustainable mobility. Smart mobility solutions can be implemented by private or public players, with direct and indirect implications for both.

Private initiatives for smart urban mobility

Companies offering self-service electric carsprovide users with an application allowing them to book a car in self-service, choosing the duration of the rental. Typically, these  operators own charging and parking facilities over the territory covered by the service. The self-service rental is often combined with a  carpooling option, highlighting the collaborative nature of the service. Several  companies take advantage of this service as an alternative to owning a fleet of cars for the  employees. Typically, the user is informed  about the amount of CO2 saved, increasing awareness on the environmental topic. Therefore, the authors understand that this AI-based smart mobility application boasts social (collaborative, economic – new vehicle ownership model – and ecological – electric cars only) benefits.    

Mobility as a service (MaaS)

MaaS (tailored transport services offered via a unified platform) is a good alternative to personal transport since mobility on demand is synonymous with freedom to go anywhere anytime. MaaS with AI-based controllers can, for instance, optimize, monitor, and coordinate  autonomous car fleets, while offering great options to  individual users. AI-based MaaS enables ride-sharing users to share autonomous cars across an optimized route in a  much cheaper and safe way, also offering greater social  experience when riding with people of similar interests. 

MaaS can  significantly contribute to the sustainability and human centricity goals. However, governance issues and difficulties for private-led MaaS platforms in devising sufficiently scalable and profitable business models are hampering its diffusion. In the future, municipalities are expected to play an increased role in framing and  enabling the development of virtuous MaaS solutions.

Another relevant application is short distance carpooling, which relies on AI to bridge the gap between car travel and public transport, allowing for precision pricing and precision routing.

Smart parking management, thanks to AI, improves car parks accessibility and fluidity.

The three mentioned applications allow for reduced urban traffic and environmental impact of urban mobility. Nevertheless, private vehicles are considered a sub-optimal sustainable solution for smart urban mobility, even if vehicles are electric, since the risk of traffic increase remains. 

According to the Finnish Center for Artificial Intelligence (FCAI), 38% of people would be willing to give up  their car, if thanks to AI, a similar mobility service can be offered. Given that 20% of household expenditure goes to mobility, this opens great business opportunities. This same money could be used to buy mobility  as a service at flat rate. The service should cover 100% of the users’ needs, which requires access to all  different modes of transport”.

Public initiatives for smart urban mobility

Dubaiboasts a long list of smart mobility initiatives, including a bus on-demand service, in selected areas of the city, whose success has been initially proved by a trial assessing response time, transit time, passengers’ accessibility, affordability, convenience, safety, residents opinions and user experiences. Recently, the Roads and Transport Authority (RTA) of Dubai started a collaboration with the UK company BeemCar to develop  the futuristic transport system called sky pod, defined as “a cross between a monorail and ski lift.

The  objective of the project is to meet the Dubai Self-Driving Transport Strategy, which aims at reaching 25% of  journeys in Dubai autonomous by 2030. Each sky pod is a four-seater suspended from a driveway unit that  rests inside a hollow, lightweight beam. Linear motors power the pod, making it travel at 50 km/h in a  network placed above the traffic in a criss-cross manner, covering 15 km with 21 stations and with the  capacity to transport 8400 passengers/h. The sky pod is expected to reduce transportation costs by 44% and  environmental pollution by 12%. 

Among the excellences in smart mobility in the old continent, it is worth mentioning Reykjavik for the efficiency of its transport systems. Specifically, the app Strætó for a smart bus transport  network has registered more than 85,000 downloads since its creation in 2014. Copenhagen aims  at becoming a zero-carbon city by 2025 through an integrated system encompassing intelligent  bus priority system, fully electrified car sharing, building infrastructure enhancing commuting by  foot and by bike, including a network of 28 cycle superhighways. In addition, Geneva boasts a very efficient smart parking system, deployed through a network of sensors, lowering the number  of vehicles searching for a place to park by 30%. 

Deployment challenges for municipalities

Every change, even the most positive one, as it is the case of smart cities and urban mobility, implies costs. Those costs are not necessarily evenly and fairly distributed over those who enjoy the benefits of the change, leading to conflicting interests that need to be properly managed in order to achieve the envisaged  betterment for all. These costs can be associated with the switch in the technological paradigm, and to the  social acceptance of it. 

The researchers stress that the introduction of AI for smart solutions in urban environments affects several established value chains, on  top of the final users. In addition, as typically occurs for emerging technologies and related fields of  applications, an additional category of challenges concerns the creation of an appropriate regulatory  framework

Concerning the technological challenges, both AI-based applications for smart cities and urban mobility suffer from lack of computing power, trust, limited knowledge, biased data collection processes, together  with privacy and security issues, especially at local level. 

Concerning social challenges, it is often assumed that for rational or utility maximizing consumers (or  stakeholders in general), new technology will eventually replace the old one. On the contrary, human history and relevant literature shows innovation adoption is everything but smooth  and automatic, it indeed takes way more than an improved technology to ensure innovation uptake. 

The study points out that the main existing obstacles for the full deployment of smart cities and smart urban mobility solutions are related to the lack of understanding of the solutions proposed and, at times, the difficult access to those. 

The new breed of AI-based solutions, “despite their machine-orientation, needs to be a user-centered technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built,  and risks should be eliminated, for this transition to take off33. Since the deployment of smart cities and smart  urban mobility systems rely on similar technology to be applied in the same environment, local authorities  will face the same kind of challenges for both. 

Findings and recommendations 

Urban AI is part of a greater stream of digital transformation of the intertwined physical, social and digital realities. For effective and trustworthy AI in addition to exemplary AI European regulation and Digital Strategy, the research  recommends including urban AI in EU research programs addressing data exchange, communication networks and policy on mobility and energy, enhancing capacity building initiatives involving both private and public (especially local) stakeholders.

On the operational side, the sharing of infrastructure (sensors, hardware, software) and data is key for urban AI business cases. Better inclusion of AI in research and policy frameworks pertaining to, among others, European Data Spaces and Ecosystems,(inter)sectoral interoperability and harmonization, is expected to facilitate urban AI implementation, also through the deployment of test and experimentation facilities as part of the Digital Europe program, supporting the EU in tackling the digital transformation.

AI demands regulation crossing the borders of technologies and domains. At the time of writing, not all cities are equipped with the necessary expertise to guarantee public values in the digitalization, leading to either non being involved as a local party, or not sufficiently being able to manage and control AI solutions implemented by commercial parties (e.g. large tech firms). The authors conclude that EU-wide support for infrastructure and governance on digitalization, e.g. Urban Data Platforms, is essential. 

When it comes to new regulation, the ineliminable trade-off between efficiency and equity is key. In this respect, the researchers recommend to prioritize efficiency, in order to accelerate the uptake of smart solutions in urban context. 

This approach is considered socially acceptable since also people without direct access to AI solutions will still benefit from those thanks to positive externalities, including more efficient infrastructures for energy, and waste management, reduced pollution, noise and congestions. In parallel, equity must be (partially)  ensured by unbiased data collection.

Furthermore, innovative procurementshould become the norm, entailing requirements for technical and ethically responsible AI. This holds for local governments taking the lead in AI implementation where procurement entails  technology supply, and for cities where enterprises are heavily involved in the provision of (AI-based) public services.

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