If you’ve read a magazine or traveled through an airport in the last couple of years, you’ve probably seen ads for IBM’s Smarter Cities initiative. Today in our Post-Oil Shanghai course, we got to learn about some of the projects behind the very public campaign. Dr. Lisa Amini is the first director of IBM Research Ireland, based in Dublin. They focus on creating urban-scale analytics, optimizations, and systems for sustainable energy and transportation.
Lisa’s group focuses on transforming cities with:
- Sensor data assimilation: how do we ensure data accuracy, and account for the volume of data that comes in from sensors deployed at a metropolitan scale?
- Modelling human demand: how do we design a robuest enough model to reliabily infer demand and peoples’ use of city infrastrucure
- Factor in uncertainty: we’re talking about humans, here.
We have a massive amount of diverse, noisy data, but our ability to use it productively is quite poor.
One reason Lisa’s team is based in Ireland is that Dublin shared their municipal data on energy, water, and other core services. IBM wanted to focus on making use of available data, not laying down new sensors. Lisa shows us a map generated by bus data. The data is much more granular at the city center than in the suburban outreaches. And even downtown, the GPS isn’t terribly accurate, and sometimes locates buses smack dab in the middle of the River Liffey. This complicates efforts to infer and improve the situational awareness.
Bus bunching is a major problem in cities. Buses that begin ten minutes apart get slowed down, and end up clustered in bunches, with long waits in between. One goal is to dynamically adjust schedules and routes to compensate for predictable bunching conditions.
Another project looks to improve traffic signalling, not just for public transportation, but all traffic. Scientists are finding links between vehicle emissions and health, and certain urban corridors and certain times see a dangerous buildup of pollutants. Smarter planning could help us at least prevent this buildup from taking place around schools and hospitals.
Lisa talks about Big Data, but also Fast Data. It’s continuously coming at you, and if you can leverage it in realtime, it can be much more useful than a study conducted well after the fact. Her team is working on technology to make use of data as it comes in, and construct realtime models and optimizations. They can see bus lane speed distribution across an entire city of routes and a fleet of a thousand buses, accounting for anomalies past and present.
Sensor data’s great, but what do you do when a segment of the bus route is flashing red? Often, a disruption requires a person going to the scene to find out what’s wrong. The IBM team is experimenting with using Natural Language Processing data to determine if the cause of traffic is a Madonna performance at the O2 Centre. They can analyze blogs, event feeds, and telco data. Twitter isn’t useful for this yet because of the low percentage of Twitter users with geocoding enabled on their tweets.
Congestion is a significant contributor to CO2 emissions, so proative traffic control is becoming an important tool. Europeans are more and moer concerned about the livability of their cities, and even when they’re not, the EU Commission is happy to regulate. Cities are excited to avoid paying heavy fines, and invest in technologies that help avoid such costs.
At the individual level, congestion charging only works if there’s a reasonable alternative to driving. Even then, Praveen notes, it creates equity issues, where the wealthy can afford to drive into the city, and the poor cannot.
Lisa’s team is also modeling coastal quality and circulation patterns. One of the big problems is the treatment of water in waste plants. These plants treat the water to a static chemical index, and then release the water back into the world. Marine life dies, and we have toxins in the water, because
Large rainfall creates road runoff into the wate system. And tidal conditions can push water back upstream and hold the toxins in place, killing marine life. People are deploying sensors across the water systems, which is a huge improvement on annual testing conducted by a diver. But sensors don’t work incredibly well underwater – they’re limited by range and “fouling” of data.
New technology uses light sensors to understand the movement of water, which, combined with other sensors and de-noising models, can produce a cleaner picture of what’s happening across the bodies of water.
Modelling Human Demand
How people move, interact, and how they prefer to consume resources.
They can improve city services by taking advantage of telco data, smart car data, and other private and public information. The findings show a surprising sapital cohesiveness of regions. Geography still plays a huge role in how we look for services, communicate, and travel. Cellphone path data can illustrate points of origin that can better inform the planning of transportation paths. Political lines are a particularly ineffective way to organize services.
In the energy space, two trends have converged: First, we have more and more renewable energies, but they’re only available when the wind blows and the sun shines (efforts to store this energy notwithstanding). Our fossil fuel power plants require careful management, and must be gradually. Ireland could actually use more windpower than they currently do, but it would have adverse effects on the traditional plants.
The second trend is smart meters, which provide much more information on how energy is used. This allows for demand shaping, dynamic pricing, and smart appliances that act based on this information. But the energy companies are structured around predicting national energy demands, and follow very conservative policies that optimize for fulfilling peak demand. Energy companies are learning to forecast energy demands for pockets, rather than huge regions, and to take advantage of reneweable energy sources with pilot projects. They foresee running hundreds of thousands of dynamic energy models, rather than their current one-model-that-rules-them-all.
A project with Électricité de France simulates massive amounts of realistic smart meter demand data to test future scenarios. They’re building additive models based on human events like holidays and residential vs. commercial energy usage. The IBM Research team has build complicated flowcharts to identify compelling datastreams.
Utility leaders are forced to make decisions that are fraught with risk and uncertainty. It’s not just optimization, but social welfare and balancing competing costs. Lisa would like to incorporate the notion of risk into the technological systems. When your phone tells you there’s a 10% chance of rain today, it’s not very actionable information. Medical tests and treatment plans can be equally infuriating in that they fall short of complete predictability. How do you communicate information that carries risk with it so leaders can make decisions?
Interconnected water systems, with water treament plants, households, and geographical features demanding different priorities. Water utilities spend enormous amounts of energy moving water from one place to another, losing between 20-70% of the water along the way. We need to begin considering these systems as integrated, and acknowledge the risk and uncertainty inherent within them. When you start working on any one aspect of city services, you quickly involve other departments.
There have been many studies on providing energy and water information to homeowners to encourage conservation. The biggest change you can make? It’s not laundry or watering your lawn. Fix your leaky faucet.
Culture matters, too. Europeans expect more of their government, and citizens get up in arms when a resource like water becomes metered.
Rather than produce a perfect formula and answer to a question like municipal water demand levels, they have built models that allow for imperfections in data and can optimize for cost or service delivery.
An area of hope is to target non-experts with well-communicated information and visualizations of existing data.
What would you do if you had a city’s worth of data?
Lisa’s team is working to convince the city of Dublin to release more municipal data for others to make us of, following in the footsteps of Data.gov, Washington, DC, and San Francisco.
The Social City project seeks to better understand the social context of people in a city to better understand why certain groups of people aren’t getting the resources they need. The conditions in which these people live could be major drivers of why
Sandra: Have you thought about incentivizing people, rather than just providing information?
Lisa: Incentives don’t need to be financial. One study found that knowing how other people like you behave has the ability to change individual behavior.
Praveen: Is it feasible to offset the cost of installing smart meters with the energy savings it provides?
Lisa: Right now, it’s still a net loss, because you still don’t have systems on the energy utlity side to take advantage of smart meters. But utilities know their time to adjust is limited, and governments are helping utilities to see that their time is limited.
People like to see immediate changes when they alter their behavior. Anything you can do to show people a change early in the feedback loop can be powerful (anecdotally).
Q: Can we do a better job of choosing sites for our buildings?
Lisa: There’s great data for this, but there are a lot of difference influencers. The telco data and the social context projects, for example, show just how many factors are at play. People may take advantage of a welfare system, but in the data, we often see them pop up once, and then disappear. They may register under different names. Cities know their service centers aren’t meeting peoples’ needs, often because of inconvenience of location.
Zack: There are a lot of policy implications in your work, and new technologies at play. You’re also in a position to educate policymakers and advocate for specific policies. What kind of barriers do you run into talking to those folks?
Lisa: Where it works is when you find some city leaders who are incredibly passionate about trying to do better and fix their city or some aspect of their city. Predominantly, people take these jobs because they do care about the city and services and infrastructure and making that better. The challenge is that a lot of policy and politics and regulations at larger levels that an individual leader can’t work around. Bus drivers’ union leaders were initially upset about the city sharing the Dublin buses’ GPS data. Are you going to spy on my lunchbreak?? Cities have histories and personalities and election cycles. Some people are afraid that the data will paint a negative picture of their work. Lisa compares it to the tide: sometimes you just can’t command it due to its scale. Leaders can’t yet prove a return on investment on a project because there’s so much uncertainty.