Co-Creation Workshops with City officials

Company

IBM Smarter Cities / local, regional, and national governments

 

Background and Challenge

What is a Smart City and why would anyone want it? When IBM started the ‘Smarter Cities’ campaign in 2010 it was not very clear what that term stands for. Market (local governments) as well as the corporate (IBM) had to be evangelized in how data analytics and IoT technologies can be used to improve livability for millions.

 

Results

‘Smart Cities’ are now widely understood as cities that use innovative technology to improve livability for millions. We learned what customers wanted and what is possible to build with available technologies. Below are some portfolio elements that we offered:

• Transportation: solutions for advanced traffic management, road usage charging (GNSS- and camera-based), Electric Vehicle charging infrastructure, intermodal transport
• Water: river and sea water monitoring, flood management, waste water treatment
• Buildings: asset management, smart metering, smart grid
• City Management: cross-divisional operations dashboard for monitoring real-time, short- and long-term activities (‘City Operations Center’)

The list has further evolved in the past years. The greater ‘Smart Cities’ vision and individual portfolio elements have been presented in numerous co-creation workshops to local governments including Mayors, and compelling pilots and business opportunities were identified. Some of them have turned into profitable business for IBM.

 

My Role / Impact

As Business Development Executive for Smarter Cities I was embedded in the sales team to support them with technical and domain expertise, but I also have been in close touch with product development teams and researchers.

For ‘Smarter Transport’ I led a team of product owners and researchers to create thought leadership material in form of two IBM Redbooks with solution blueprints. I have presented at many conferences and gave press interviews (incl. Financial Times Deutschland) and had a TV appearance (on n-tv).

ROLE

Business Development Executive

CLIENT

Local, Regional, and National                                                Governments

PLATFORMS

Smart Cities Platforms

Data Analytics

Smarter Transportation for Nice Cote d’Azur

Organization

Nice Cote d’Azur Metropolis

 

Background and Challenge

Trapped between Maritime Alps and the Mediterranean Sea is the Metropolis Nice Cote d’Azur, a very popular region facing heavy transport problems. As in many other regions in the world, the strong dependency on cars causes frequent congestions and emissions. Local government is working to foster multimodal transport.

Results

Over a period of 7 weeks full of interviews and research we compiled a comprehensive study on how digital solutions can relieve some of the region’s existing pain points. A key focus was on physical and digital aspects of multimodal transport, ie. the use of digital technologies inside transport stations and throughout the overall journey. The study also touched on a city congestion charge and Autonomous Driving.

 

My Role / Impact

I worked in a team with 6 other advisors from around the globe and brought the intermodal transport, Intelligent Transport Solutions (ITS) and Autonomous Driving aspects to the team. The local government has adopted several of our recommendations leading towards better transport outcomes.

ROLE

Strategy Advisor at IBM

CLIENT

Metropole Nice Cote d'Azur

SOLUTIONS

Intelligent Transport Solutions

Multimodal Hubs

Prediction of air quality

Company

Product development at IBM for environmental authorities and transport agencies as part of the Smarter Cities programme.

 

Background and Challenge

Exposure to air pollutants in ambient air can adversely affect human health. The European Union (EU) has developed health-based standards http://ec.europa.eu/environment/air/quality/standards.htm  for air pollutants. Exceedances of air quality standards for particulate matter (PM10) and nitrogen dioxide (NO2) have led to health concerns and more recently to penalties and driving bans in some cities. As traffic is a strong contributor to air pollution, intelligent traffic management can be an efficient measure to address the issue.

 

Results

Using real-time data from air quality monitoring stations, traffic data from sensors in the road, and weather data together with Machine Learning we built a solution able to predict key air quality parameters with very high accuracy. Predictions 48 hours, 24 hours before and four times on the forecasting day can give input to trigger the timely implementation of measures to avoid exceedances of air quality standards.

Unfortunately, the solution was ahead of its time: EU regulations were not enforced in 2011, and Machine Learning was not a popular technology then. Chemists in environmental departments found it hard to believe that a Machine Learning approach without a physical-chemical model can be so highly accurate.

 

My Role / Impact

I have created this solution with my Master student Jan Kauffmann. I have spoken to many customers in local, regional, and even the national environmental protection agency and presented at conferences. In cooperation with the City of Munich (providing data from induction loops), the State of Bavaria (providing air quality data), and the German Weather Service we have successfully trained our Artificial Neural Network and were able to predict exceedances with very high accuracy.

ROLE

Chief Digital Officer

CLIENT

Environmental Agencies on Local, Regional, and National Level 

SOLUTIONS

Air Quality Monitoring

Intelligent Transport Solutions (ITS)