Smarter cities are urban areas that incorporate various kinds of electronic data collection to manage city systems effectively and sustainably. They integrate information and technology with the Internet of Things (IoT) to optimize city operations like transportation systems, power plants, water supply networks, waste management, information analysis, and other community services. Constantly evolving, smarter cities enhance the quality of life for its citizens and are quickly emerging everywhere — that’s because major technological, economic, and environmental forces in society are demanding fast urban change.
Our CleanTechnica team recently had the opportunity to meet Eyal Santo, CEO of a new company called UMo, in Paris and then virtually. UMo’s AI Urban Brain has the potential to analyze data, strategies, user cases, campaigns, and success rates from each and every city that deploys its solution. In this interview, we discuss how UMo’s predictive analysis is generated through combining machine learning algorithms, cognitive sciences, and shared knowledge. The recommendations to city planners and officials that result create shared knowledge and unify silos – on the local level, within each city and metropolitan area, and more so on a global level.
Tell us about your slogan: “Smarter cities. Happier people.” How does it capture your mission and vision as a company?
Smarter cities mean happier people. We help cities realize real urban sustainability strategies and save billions of dollars using our proprietary AI technology. We create smarter cities by:
- Reducing congestion, pollution, and accidents;
- Improving public land use and public space;
- Creating healthier and more sustainable citizens; and,
- Strengthening local economies.
I believe smarter cities can allow these qualities to become truths. I should emphasize I mean cities for people – not for cars, not even for self-driving cars – but cities designed for people.
How do you use predictive data to help cities to identify new ways to create healthy and sustainable future environments?
We use data to make predictive analysis. Cities apply this experience, knowledge, and wisdom. For example, the machine learning algorithm may find that “Campaign A” in “Small City X” in the United States, which yielded 80% results, may fit into the recommendations basket for “Mega City Y” in Asia, due to patterns whose similarity was only found by our algorithm. The patterns could be weather, demographics, socioeconomics, proximity to the beach, or distance from it – and thousands of additional parameters.
Please describe some of the innovative AI results that happen when you guide city managers from data aggregation to the “AI urban brain” and an “AI dashboard.”
This link to the UI we built for Tel Aviv shows a process of city data. Focus on the center of the screen – the map: you’ll see there color coded areas. Those represent geographies where UMo’s AI Urban Brain automatically identified urban challenges, such as mismatches between the city strategy and the status on the street. It could be geographies or locations (as in this example) or populations, groups, demographics, businesses, etc.
UMo’s AI Urban Brain constantly processes data and city strategies and automatically floats the challenges. On the bottom left side of the screen you can see two charts: the green one is the savings the city generated by implementing its sustainability strategy. The figures are based on standard models used by cities today – currently weighing in a couple of algorithms developed in Copenhagen and Vancouver. Additional models, such as HEAT, are planned to be taken into the algorithm as well. The other chart is the city’s sustainability score vs. its own strategy and goals.
Once a city stakeholder clicks on a challenged item, you get a drill down into the challenge. Say the headline reads “Low Cycling Usage.” Why is that? Because UMo’s AI Urban Brain identified a corridor where cycling infrastructure is at a 90% level with the city plans and strategies, but the usage is very low. Not only do the city decisionmakers get a full status update, insights, and analytics about each of the challenges: so do the affected people. More so, the insights are processed into recommendations and actionable insights.
It is worthwhile mentioning a very interesting observation made by urban planners and transportation engineers who saw our solution. They recognized the significance of UMo’s AI Urban Brain to urban planning in mobility and transportation, in energy and utilities, in housing and businesses, and in mixed usage. UMo’s solution identifies “desire lines” and patterns and can make predictive analysis with high accuracy.
All this is very important for city planners in the different domains to prepare the supply to match future demand. This is an outcome we even weren’t aware of.
Allow me to add one more item and to talk about savings for the city directly and for the urban society. Our simulator, when implemented on some 60 global cities, weighs in additional parameters such as GDP, number of cars entering the city (city center only), population affected (city core only/without metropolitan area), and more. It then generates the associated savings for a minimal scenario of changing modal share from cars to bicycles only one time a week, for the duration of the year, for an average commute of 10 km each way.
What differentiates UMo from other predictive data analytics companies that are working toward smarter cities?
What is our unique edge?
1. Team: We are mui, mui passionate about our cities, about sustainability, and about people. We bring rich experience from diverse backgrounds – complementing each other, overlapping in other domains.
2. Market: “City is not a problem – city is a solution,” said Jaime Lerner, the legendary mayor of Curitiba, Brazil. The challenges are immense, and, to answer them, the smarter cities market will pass $3.3 trillion in 8 years. It will show high growth for many decades. UMo’s solution touches almost each and every single aspect of smart cities. Therefore, every city that would choose to be smart AND sustainable should choose UMo’s solution.
3. Product: Bred and brewed from a deep knowledge of cities, urbanism, mobility, and technology, our product is AI-based and caters to all stakeholders of the urban ecosystem: the city, the people, the corporate employers, and the local businesses. The result? Not just another ordinary dashboard, but one that, on top of analytics and insights, offers a bank of recommendations drawn from city strategies and the ability to act upon those.
4. Technology: UMo’s AI Urban Brain uses our proprietary AI algorithms to melt-pot data and city strategies.
5. The people and the city: These are in the center of our thinking and design. Solutions may not be simple to implement, but they are easy financially. A city such as New York can start saving with our solution at least $150 million a year, for a fraction of that amount – about $3 million a year (for the urban mobility mode). Scaling could be fairly rapid.
6. Unifying separate silos: Until now, almost every system for cities was aimed at a specific department: education, transportation, welfare, housing, revenues, utilities, businesses, etc. Databases were and are mostly still separated. Decision makers “sit” in separate silos. UMo’s AI Urban Brain reads data from multiple city databases as well as from additional sources. Therefore, the output it presents is “cross-silo,” unifying the different city silos and making sure knowledge is shared rather than leaving them separate. City stakeholders can learn from UMo’s solution about challenges before they may even surface, know exactly whom to address, and even build cross-departmental teams to solve challenges.
7. Future-ready: As self-driving cars become the standard, we will help cities implement their policies and regulations on AVs, making sure they drive around in our streets in the most sustainable manner (and not as zombie-cars with zero occupancy). And, while helping cities on urban mobility is the first phase, UMo’s AI Urban Brain will support in the years to come additional dimensions of sustainability management for cities, such as renewable energies, waste management, urban forestry, urban farming, and many others. While today we have to manually calibrate our AI algorithm to weigh in city strategies, future versions will be fully automated and self-learning.
8. Clear business model: We offer SaaS, direct sales, and thorough strategic partners (tier-I solution providers such as IBM, Cisco, Siemens, and Deloitte).
9. Driven by the impact: For us, the rewarding outcome is the change we bring to people’s lives: increased urban life quality, more prosperity to local business, better connected communities, and robust health. Just imagine what cities can do for their people with the huge amounts of money they and the urban society will save.
Tell us a bit about your UMo leadership team: you — Eyal Santo — and Eyal Zohar. What talents, background experiences, and educations do you both contribute to UMo?
A very brief description does a perfect job. It starts with a single word, “passionate,” extends into two words, “VERY passionate,” and continues into a whole sentence: “We are very passionate about our cities and their people.”
Eyal and I are avid mountain bikers – that is where we both met and became friends. We took our passion from the mountain bike trails to the city as we both cycled to work numerous times during our 20+ years in high tech. Eyal Zohar specialized in project management, product management, and marketing. I was more on the strategy and bizdev side. I left high tech in 2007 and for three years was MD for a bicycle import company. For three more years I had my own business of importing bicycles and providing them to the corporate market via operational leasing, building, and managing corporate cycling policies.
I started drafting my ideas for smarter cities in the summer of 2016. In early 2017 I felt confident enough to approach my friend, Eyal Zohar. We decided to apply for the MassChallenge accelerator program and were among 52 finalists chosen for the Israel cohort, out of >500 applicants. While UMo has evolved into a technology-based startup from the consulting firm it previously used to be, the original spirit and the vision remain.
Both Eyal Zohar and I studied at the Technion, Israel Institute of Technology – Eyal for his BSc in Industrial and Management Engineering, me for my BA Physics. Eyal Zohar is also a graduate of the Tel Aviv University’s MBA program in marketing and technology. I plan to go for my MSc in Physics after I retire. That would be in cosmology and quantum physics …
How do you help city managers to translate your predictive data numbers into actual sustainable practices through urban mobility strategies?
Unfortunately, I don’t think that moving from fossil fuels to electric cars will be our cities’ salvation. Neither would be self-driving cars. Fossil to electric is a nice move, but it is mostly moving pollution from one place to another, as long as the world is like 80% dependent on fossil fuels for electricity generation.
And – the manufacturing process for batteries is very polluting, let alone toxic.
As for self-driving cars – they are ONE layer of mobility, ONE piece of the puzzle. Nothing more. We should remember this. And what if MaaS fails? Shared mobility is not about carrying one person at a time, then moving to carry the next one. It’s about two to four people — strangers or acquaintances — sharing a self-driving car ride. And this is VERY difficult to realize. What if people will NOT give up on ownership? Say – in 2030 your daughter turns 12, and, instead of driving her around town, you buy her a ping self-driving car to take her to school, friends, movies, shopping, etc. You think this is imaginary? This scenario comes straight out of MIT media lab research.
Bottom line: cities need to allocate a LOT of careful thinking, brainstorming, and planning for an electric, self-driving future.
One of the dimensions of sustainable urban mobility has to do with self driving cars. We enable the city to implement its sustainability strategy, priorities, policies, and regulation on self-driving cars.
- Do they pay for using the road?
- Is it a dynamic price – i.e. the higher the occupancy is – the lower the price or the price-per-person is?
- Is the owner rewarded for the car driving around with occupancy of at least 75% at least 80% of the time?
- Is the owner “ticketed” for occupancy lower than 25% more than 30% of the time? Etc.
Self-driving cars will be obliged to share data with the city — and with this data, we will help the city make sure they drive around our streets in the most sustainable manner.
UMo & Smarter Cities Everywhere
If you’d like to learn more about UMo’s approach to smart analytics for smarter cities, they’d be happy to talk to you. Here’s a link to get you started.
We’d like to thank Eyal for the time and thoughtfulness he devoted to this CleanTechnica exclusive interview and his insights into the smarter cities of the future — and now.
Photo gallery courtesy of UMo
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