Analytics And Transporting Crowds Of Olympics Fans


crowd escalator train station web

With the European Football Championships having just come to a close and the Olympics due to start, the Summer of 2016 will have seen two major events that only happen once every four years on the sporting calendar. These are in addition to the regular annual sporting events such as Wimbledon, the British Grand Prix and the Rugby League Challenge Cup Final. With events such as these, a lot of people travel whether it be locally or internationally. Such spikes in travel can have implications on the travel networks and cause problems with people getting around.

Despite the fact that the football championship was in France and the Olympics Brazil, back at home in the UK it is likely that a huge number of people will be watching these events live whether that be in a pub, a sporting establishment such as a club or at home. A huge number of people would have traveled to Wimbledon and also to Silverstone as well as those who made a trip to France and the more adventurous who might descend on Brazil.

Of course in the modern day world where we are able to watch all of our TV on demand it doesn’t really matter whether we miss one of our favorite programs. In the case of live sport however, it is extremely difficult to keep away from social media, news alerts and radio during a live game. So it is likely that a lot of people will watch sport live to stop the end result being spoiled for them.

Take the Olympics for example. Not only will a lot of people travel to Brazil from all over the world, they then need to travel inside the country to see various events. Local Brazilians also need to travel around the country to see the various events plus conduct their usual business. This will cause an increase in people traveling around the country over the period that the Olympics is taking place.

How can analytics help in these cases?

Using data to predict spikes in demand for transportation could be paramount to the success of a large sporting event such as the Olympics. For example, how many tickets have been sold for an event in one of the satellite locations in Brazil could indicate a lot of people traveling from Rio at the same time. Using IoT and data analytics could mean looking forward to one of these events to predict who might be traveling and what effects this could have. By enriching the data further with the city or postal code of ticket purchaser could tell planners where people are traveling from.

Of course it is difficult to predict as a lot of the locations are new and Brazil hasn’t hosted the Olympics before, but by pulling together data from previous transport networks and large events, planners might be able to predict where blockages or problems might occur. Predicting potential problems offers the opportunity of preventing problems from occurring in the first place.

The main aim would be to look at passenger info for the main transport hubs and see where the potential problems might occur normally, then predict what could happen when these places are busier due to huge numbers of people. Brazil wants to make a good impression during the Olympics for people who are visiting but also for people from the country to be proud that it did a good job. By predicting how the transport networks could be affected it will mean that the travelers will be happy and safe whilst visiting the country but also the networks will remain reliable and thus the country will see overall economic benefits from hosting a large sporting event.

(Image courtesy paha_l / 123RF Stock Photo )


Visual Analytics, With Empathy


Business isn’t empathetic. Let’s face it, it just isn’t. For many of us, business happens inside a building lit by the lingering fluorescence of a good weekend. We are wizards at piloting Excel and chanting quantitative mantras. We are great at drill downs and analyzing historical data. And if we are lucky we will make one or two really good decisions a quarter, based on some column charts and a lot of intuition.

The rest of it is a crap shoot. While discussion groups on LinkedIn are singing the praises of ‘Business Intelligence’ I can’t help but wonder if something is missing from the equation. I’d posit that BI is a solution looking for an answer. The reason is that BI happens in the vacuum of the past and through the lens of equations void of real-world context.

As a user experience (UX) designer, I was taught to ask empathetic questions about the situation surrounding a problem. Algorithms are useful but they don’t always adapt themselves to the current context. Good UX means asking, “What is happening in the situation around me? What is the problem right now? What do my team members at work care about? What will my child want for their birthday? Where will I park downtown tomorrow night?” These questions are everyone’s concerns. They are also business problems, every one of them. If you fail at one the others suffer because everything is connected, not sliced and diced.

To answer questions with empathy, you need real-time awareness of conditions plus insight from the past to predict the future. What is true now may not be in 3 minutes, 3 hours or 3 days. BI is a measure of the past. It is two-dimensional and quantitatively vapid. It isn’t asking raw questions.

Situational intelligence, on the other hand, strives to analyze and present what the conditions are across the spectrum of time. It seeks context and not just quantity. In this way, it provides empathy to users solving a problem.

As an example of empathy and situational intelligence, consider the problem of planning a trip to a congested urban area during rush hour. Some people want to save money on their trip, some want to save time, and some want to balance both. Other people don’t care how long it will take or how much they will need to spend, as long as they are safe.

This simple traffic dashboard speaks to all or just some of these concerns. It requires only 3 APIs and connections to a handful of databases (Google real-time and typical traffic, Trulia crime statistics, video feeds from web cameras). It gracefully accommodates the 3 Vs of our Big Data world: volume, variety, velocity.

Traffic dashboard

But it also has empathy. The dashboard adjusts throughout time and at a moment’s notice to the concerns of the user based on cost, time or safety in the past, present or future.

BI is about a process. Situational Intelligence is about people and their unfolding story.



Situational Intelligence Broadens the Appeal of Microgrids


Energy generation and distribution isn’t just for utilities anymore.

In a previous post about microgrids, I mentioned New York University and the teaching hospital of Tohuku Fukushi University in Sendai, Japan as two notable microgrids. In both cases, microgrids helped the facilities survive and function after a major catastrophe (Hurricane Sandy and the Fukushima earthquake and tsunami, respectively). Other public sector organizations, including military bases and jails, are also generating and distributing energy through microgrids.

The private sector is also getting into microgrids. According to a 2015 report from Deloitte, “a solid majority (55 percent) of businesses say they generate some portion of their electricity supply on-site, up from 44 percent in 2014.” That’s growth of 25 percent in a single year. In particular, two-thirds of technology, media, and telecommunications companies report generating energy on-site.

Although they are generating and distributing energy, none of these organizations are utilities. Their core competencies lie elsewhere. Managing energy is something that they do to lower operating costs and increase reliability of service. That means that they need an easy way to run their energy operations with minimal staff and effort.

Situational intelligence could help these customer-owned microgrids, in two main ways.

One, situational intelligence applications unite disparate data sources scattered across large organizations This allows energy planners and operators in non-utility organizations to draw on all relevant data to solve their energy-related challenges, while avoiding the tedious and time-consuming work of locating, cleaning and correlating data sets by hand.

Two, situational intelligence applications provide analytic and automation capabilities that enable small staffs to operate complex systems such as microgrids. That helps keep operating costs low, which is part of the purpose of customer microgrids.

It’s not that large public and private organizations don’t need utilities anymore. The utility grid still provides primary and backup power, plus offers a channel to participate in the wider energy market through surplus energy sales. However, the increasing availability and ease of advanced analytics, distributed energy generation and industrial automation through the Internet of Things make microgrids a viable way to realize increased reliability at lower costs.


Connected Cars Are Valuable (Even If They Aren’t Electric)


According to McKinsey, today’s cars—gasoline and electric—run on about 100 million lines of programming and the computing power of 20 personal computers. All that tech is internally focused on the car itself. But increasingly, car owners, businesses and governments are turning their attention to how cars can connect to the outside world to improve safety, performance and the in-car experience.

Such a mobile computing platform, operating on a network of roads and contending with real-time changes in terrain, weather, traffic and operating status, is a perfect environment for situational intelligence applications.

For example, many cars use GPS and sensors to provide predicative analytics about remaining driving range based on current amount of gasoline or battery charge. As a more advanced example, in a previous post I described how Tesla is using situational intelligence to combat range anxiety by ensuring drivers can easily remain in range of a charging station.

By connecting to external sources of data, individual cars could use situational intelligence applications to enhance performance and experience, such as

  • Predicting duration of consumables such as oil, brake and steering fluids, antifreeze and wiper blades
  • Identifying early warning signs of malfunctioning systems
  • Gasoline and charging optimization (“When and where should I fuel next?”)
  • Electricity charging station reservations

By connecting with each other as well as external sources of data, groups of cars could also contribute to and benefit from situational intelligence applications. Possibilities include

  • Accurate, real-time measurement for traffic rate of flow
  • Real-time weather mapping based on sensor data for multiple cars
  • Proximity safety in dense traffic environments
  • Car platooning and other throughput improvements
  • Corporate fleet analytics and management
  • Verifying outdoor advertising exposure

And of course, there’s the ultimate in connected vehicles–the self-driving car.

Business Insider predicts that by 2020, 75 percent of cars shipped globally will be built with the necessary hardware to connect to the internet.

As you can see, you do need to buy a Tesla or other electric vehicle to benefit from situational intelligence while on the road.



Situational Intelligence Makes Electric Vehicles More Useful, Even When They’re Parked


When you buy an electric vehicle (EV), you get a big battery. The Nissan Leaf comes with a 24 kWh battery; the Tesla Model S sports one with 54 kWh capacity.

In a previous post, I talked about how situational intelligence helps EV owners stay within range of a charging station while they are out driving. But what about when your EV is parked at home—can situational intelligence help you get more value from your big, mobile battery?

First, it’s useful to understand how the capacity of a car battery compares to the power usage of a regular household.

An average US household uses approximately 900 kWh per month, according to the Energy Information Agency. Opower says that the bottom 90 percent of the economic strata use about 600 kWh per month. So, at 20-30 kWh per day for most households, and battery capacity of 24-54 kWh for most EVs, it’s conceivable for your fully-charged car to power your house throughout an entire day. (Of course, if you did that, you’re probably not driving anywhere that day.)

One way an EV battery is useful at home is demand response. In times of high grid demand, utilities could instruct houses to draw power from the EV battery instead of the grid, to reduce the need for additional, expensive peak generation. Demand response events range in time from a few minutes to a few hours, so demand response doesn’t need to put a crimp on EV range.

Another way an EV battery is useful at home is energy storage. At times of high grid supply, either at night when usage is low or on sunny days full of residential solar power, the EV battery can store excess energy and help keep the grid in balance.

Situational intelligence makes EV batteries more useful while parked by correlating, forecasting, analysing and visualizing multiple data sources such as

  • EV location and battery capacity
  • Grid energy supply and demand
  • Renewable energy supply and demand
  • EV owner’s demand response and car charging preferences
  • EV owner’s home energy usage patterns

Understanding all these variables in real time across a city’s grid enables a more affordable and reliable power supply without inconveniencing EV drivers. With the right power prices and incentives, this can be added incentive for making your next car an EV.



Situational Intelligence Makes Electric Vehicles More Predictable


A recent article in Cleantechnica lists some staggering annual growth rates for electric vehicles in 2014: 45 percent growth in Japan, 69 percent in the United States and 120 percent growth in China. Fifteen new models of electric vehicles are slated for introduction this year, according to EVObession.

Even with these numbers, the overall population of electric vehicles remains relatively small: 100,000 thousand in China, 110,000 in Japan and 290,000 in the U.S. As this map of California shows, electric vehicle adoption varies greatly from county to county, even within the U.S. state with the highest rate of adoption.

Given this rapid but uneven adoption of power-hungry electric cars, how can utilities adapt? It’s difficult to predict where and when cars will need to charge, and how much charging they will need. Until penetration rises and becomes more even, it’s difficult to justify investing in widespread infrastructure upgrades to support charging. Can making smarter use of existing infrastructure meet charging needs for now and the near future? Because electric vehicle adoption occurs in pockets of concentration, utilities face the risk of multiple charging vehicles overloading a portion of the grid and causing localized brownouts or blackouts.

Situational intelligence offers analytics and visualization across space, time and node, providing an ideal approach for integrating electric vehicles into the existing power infrastructure:

  • Spatial analysis shows which households have electric vehicles or are likely to acquire one, where vehicles have traveled and are likely to travel, and where drivers typically recharge their cars. It also shows the location of chargers. When placed on a map of utility assets grade by reliability, this can pinpoint places where vehicle charging is likely to cause problems.
  • Temporal analysis shows when electric vehicles are likely to charge in relation to forecasted supply and prices of energy. This helps drivers and utilities match the demand of charging with available supply.
  • Nodal analysis shows frequently used chargers and optimal locations for new chargers. It could also show drivers routes that are estimated to consume less power and thus extend vehicle range.

For example, electric vehicle maker Tesla Motors recently announced a software upgrade that includes this type of situational intelligence. According to a Business Insider article, with the update, “a [Tesla] Model S will know where it is, where the closest Supercharger is at all times, and how much battery charge it has remaining, as well as how far it has to go to a given destination, if that information is available.”

As the population of electric vehicle grows, drivers and electricity providers will benefits from increased analytics to ensure smooth driving.



Situational Intelligence and Smart Cities: Energy, Part 2


In a previous post, I wrote about how progressively sophisticated uses of situational intelligence for energy can help a smart city evolve. Energy is the enabling technology for a smart city, because it powers the Internet of Things. All those sensors, mobile devices, smart street lights, programmable signs, electric vehicles and other powered devices, plus the computers and communication networks that gather, store, analyze and visualize the resulting streams of data, require a steady supply of electricity to operate.

Because it is the bedrock for the smart city, energy needs to be affordable, resilient and sustainable. These three qualities help ensure a steady supply of power for operating the Internet of Things. How does situational intelligence make energy more affordable, resilient and sustainable?

  • Affordable energy
    To keep energy affordable, Situational intelligence applications can optimize capital planning for cities and utilities, as I also discussed in the previous post. Capital efficiency helps control the price of power generation and distribution. By forecasting energy supply and demand based on multiple correlated data sources, situational intelligence applications help utilities meet demand through demand response, instead of spending money to purchase extra power on the open market or to build new power generation plants. Avoiding these costs keeps energy affordable. Marketing and customer service professionals use situational intelligence applications to analyze customer profiles and usage and develop optimal pricing and packaging approaches. Paying just for the energy services you need and use helps keep power bills small.
  • Resilient energy
    Situational intelligence applications provide spatial-temporal-nodal analysis to determine risk levels in individual assets on the grid, a precursor to lowering risk and thus raising resilience. To make the grid as a whole more resilient, situational intelligence applications help city and utility officials forecast the impact of crisis events so that communities can better prepare for and respond to severe weather and natural disasters.
  • Sustainable energy
    Grid planners, energy dispatchers, power marketers and others use situational intelligence applications to integrate sustainable, renewable sources like solar, wind, geothermal, biomass and tidal into the generation mix. Situational intelligence applications also power the analytics for integrating grids-scale storage and demand response into grid operations.

With affordable, resilient and sustainable energy, the Smart City can evolve beyond today’s small minority of innovative city departments and wealthy neighborhoods as sensor and communication networks become pervasive.

Where have you seen the Internet of Things spreading in your community?


Situational Intelligence and Smart Cities: Crisis Response


So far in this series on situational intelligence and smart cities, we’ve looked at services in constant use such as transportation, water, and energy. How might the concept of progressive use cases help evolve smart infrastructure for more intermittent services, such as crisis response?

As a first step, situational intelligence supports integrating all first responder communications and locations onto a single, citywide GIS system, possibly on a video wall in a city operations center. By knowing where first responders are located and using route optimization analysis, city officials can dispatch the closest and best-suited people in time of crisis. Quick response saves costs, property and lives.

Operational savings can be applied towards increasing coordination between city agencies in response to crisis. With all first response systems shown on a single map, the city operators can more easily dispatch appropriate teams to the scene. For instance, a large apartment building fire might require fire fighters, police for crowd and traffic control, shelter workers and counselors for displaced residents, and even animal control to help rescue and reunite pets and families separated in the fire.

With a history of crisis responses to analyze with other information about weather, building inspection reports, crime trends, and even social media, city officials can expand situational intelligence to crisis prediction models, early warning systems, and crises prevention programs.


Situational Intelligence and Smart Cities: Energy, Part 1


The previous entry in this smart cities series examined how situational intelligence could be used to improve and optimize municipal water systems. Let’s look at how progressive use cases can help evolve smart infrastructure for city energy services and consumption.

As a first step, situational intelligence supports aggregating the energy consumption of city facilities into a single view of energy usage across city operations. Aggregation leads to efficiency in accounts payable and provides leverage for negotiating better energy rates with suppliers.

With the savings from accounting efficiencies and lower energy rates, city officials can invest in the energy efficiency of city facilities. Increased energy efficiency lowers costs, and the savings flow directly to the bottom line for improved fiscal health. Situational intelligence supports analyzing and scoring buildings according to their energy efficiency and projecting savings from capital investments in efficiency.

With buildings and facilities operating more efficiently, cities are in a position to begin implementing microgrids and energy storage devices to keep city services running in times of crisis, take advantage of renewable energy sources such as solar and wind, and participate in demand response programs that would increase reliability, lower energy bills and reduce carbon footprint. Situational intelligence maps and projects energy availability from storage devices and from renewable sources.

Energy and water, our most recent topics in this series, are constant needs for city residents. In a future blog post, we’ll see how progressive use cases might help to evolve services of a more intermittent nature, such as crisis response.


Situational Intelligence and Smart Cities: Water


Previously, we looked at how progressive use cases can help cities evolve their smart infrastructure, and how that might work for city transportation. Let’s look at how this evolution might work for municipal water systems.

As a first step, situational intelligence supports mapping water flows through meters across the city. By comparing projected and actual water flows, situational intelligence can locate water mains most in need for repair or replacement. Critical and proactive repairs can help address the 10 percent or more of water lost in most distribution systems due to leaks. Reclaiming lost water raises production, which saves money by either increasing sales or holding down operating costs.

With the savings from repairing leaks, a city can add a customer service module to their situational intelligence platform to provide better service to customers at lower operating costs. At the same time, customer satisfaction with city water service can rise.

Once a city can map water flows across the distribution system and increase customer service and satisfaction, it is in position to implement a water conservation program to increase sustainability and ensure water supply for future economic development. Situational intelligence supports water conservation by flagging anomalies in water consumption patterns governed by conservation program rules.

In a future blog post, we’ll see how this progression can work for energy.