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 )


Use Visual Analytics to Get Started with the IIoT


Industrial IoT (IIoT) applications bring about many opportunities to increase operational efficiency by presenting personnel with timely insights into their operations. Visualizing IIoT data using visual analytics is a proven way to facilitate insight-driven decisions. So at the very least your IIoT initiative will start off by integrating IIoT connectivity, visual analytics and other system components. To best ensure early and ongoing success it is recommended that you follow the best practice of starting small, attaining quick wins and then increasing scope and/or scale.

The first step is to connect devices and systems and use visual analytics to create a simple visualization of your IIoT data. If the IIoT devices are mobile or geographically separated, then an appropriate visualization would be to display the location of the devices on a map such as shown above. This is an effective way to verify connections and validate successful integration.

The second step is to collect and intuitively visualize your IIoT data. At this point you can identify issues to make operational efficiency improvements.  As an example, a freight trucking business can see a map with the locations and times of where their trucks are moving at slower than expected speeds. This information is used to change the routes on the fly to maximize on-time deliveries. As this example highlights, connecting to IIoT data streams and visualizing the data facilitates operational efficiency improvements.

The third step is to correlate data from different systems and data sources, including time series data from devices at different locations. Visualizing data correlated by time and location makes it possible to create comprehensive big picture views that reveal details about what happened and is happening, where, when, why and how. Using the trucking example, areas where driving speeds are consistently slower than expected are highlight by the red lines on the map above. This information is used to refine future routes, schedules and delivery commitments.

The fourth step is to apply advanced analytics to the IIoT data to generate insights for inclusion into the visualizations. Returning to the trucking example, advanced analytics will recommend the optimal average truck speed to minimize fuel costs based on the weight of the load they are carrying. Visualizing each truck using color coding to highlight the biggest offenders makes the analytics results actionable at-a-glance so that operations managers and drivers can improve driving efficiency. In the image above it is easy to see the truck icons colored yellow and red that represent the trucks that are traveling outside of the optimal speed range.

Having completed these steps you are positioned to leverage your IIoT infrastructure and expand on your competency by combining visual analytics, data correlation and advanced analytics in innovative ways to address business problems and facilitate operational efficiencies that would not otherwise be possible. Future blog posts will cover such combinations and the corresponding operational efficiencies.


Are You Ready For Autonomous Freight Trucks?


truck convoy web

Recently a group of freight trucks drove across Europe autonomously. Daimler is now selling new semis with autonomous driving features. A Silicon Valley start-up wants to sell equipment to retrofit existing freight trucks for autonomous driving.

Are you ready to share the highways with autonomous trucks?

Autonomous trucks are not driver-less trucks. Trucks still need drivers to handle city driving, parking, refueling, delivery paperwork and many other aspects of freight handling. It’s during those long, boring stretches of highway driving where autonomous systems relieve drivers of some of the dullest and most dangerous work.

The work is dull enough that there’s currently a driver shortage. The American Trucking Associations report that the United States needs about 50,000 more truck drivers. Autonomous systems might help make the job more appealing, and maybe even make today’s drivers more productive. Both outcomes would help with the driver shortage.

Autonomous trucks are designed to have fewer accidents, more predictable performance, and better fuel consumption. One way these trucks conserve fuel is by lining up in platoons to reduce wind resistance.

The trucks use a combination of video cameras and radar to sense the situation around them. That video and radar data can be enriched with other information about weather, terrain, destination and cargo, then analyzed to optimize routes, schedules, fuel consumption and more. The analysis may suggest, for example, that the truck is ahead of schedule and slow the vehicle to arrive on time and conserve fuel in the process.

If your company owns a fleet of such smart trucks, then you might benefit from analysis spanning groups of trucks or your entire fleet. For instance, could you change schedules and routes so that more of your trucks travel in platoons to save fuel? Will the suggested changes to routes and schedules still meet your shipping agreements or delivery commitments?

For years as air travelers, we’ve ridden in planes with autonomous piloting and enjoyed it as the safest form of transportation. But when we’re operating our cars alongside autonomous trucks, does our attitude change? Personally, I’m not sure how I’ll feel passing, or being passed by, a seeming wall of platooned trucks rolling down the road.

What are your thoughts?

( Image: whitestar1955 / 123RF Stock Photo )


Analytics, Transportation Safety And Extreme Weather


transportation safety landslide 01

Extreme weather conditions such as floods, landslides, sinking water tables, droughts, ice storms and snow drifts takes a toll on the landscape. Those weather-driven changes to the landscape directly impact rail beds, roadways and bridges, rendering them unsafe to use until conditions change and repairs are made.

For instance, heavy rains trigger landslides that block roadways and railways, stopping traffic until the debris is removed and the damage repaired.

Analytics can highlight where and when disasters might strike. Predicting where damage may occur and taking precautions prevents damage to people and property.

Factors that signal a landslide or flood hazard include

  • Transportation corridors in proximity to sloped terrain or bodies of water
  • Vegetation, or lack thereof, on slopes and banks
  • Moisture levels in the soil
  • Past, current and projected weather conditions
  • Condition of the road or railway itself
  • Traffic metrics for the corridor including overall volume of traffic, patterns of traffic flow, and criticality of the corridor in connecting valuable locations

Imagine a situational intelligence approach to transportation safety. An analytics application could correlate, analyze and visualize the factors listed above. That analysis and visualization would enable government transportation officials and railroad operations and planning professionals to see the probability of landslide on a slope adjacent to a right of way. Knowing the probability of a landslide and the magnitude of its impact informs decisions about operations, crisis response and mitigation.

A similar situational intelligence approach applies to floods damaging transportation corridors that are adjacent to bodies of water. Some of the measurements and algorithms would be different, but the much of the output and outcomes from such a system would resemble that from landslides.

When public transportation departments and private transportation companies know that a corridor is at risk, they can take action to avert disasters. Traffic and shipments can be redirected to minimize delays and remain safe. Customers can be notified if their shipments will be delayed and new arrival times. Repair crews can be positioned for faster response to the most critical areas.

Analytics for transportation and weather is especially important in the United States given the current condition of transportation infrastructure. The American Society of Civil Engineers give these systems low marks:

Infrastructure that is in barely passable condition is less resilient to the impact of extreme weather. If we can’t change the weather and don’t have the money to improve the infrastructure, at least we can be smart about how we plan and respond.

(Image courtesy of fotokostic / 123RF Stock Photo)


Predicting and Managing Urban Storm Runoff to Protect Roads



It’s El Nino time in the West, which means our cities see storm water runoff from heavy winter rains. Large pools of standing water form on roadways where there’s no drainage, drains are clogged, or the storm water system overflows. So much water on and around roads leads to accidents and traffic jams. Water also degrades road surfaces and erodes road beds, creating more hazards and eventually requiring road repair work.

Viewed as a challenge for situational intelligence, storm water and traffic becomes a compelling example of what’s possible. Situational intelligence features analytics of networks and space / terrain across time. Storm water and traffic is a problem of two networks–drains and roads–during the time when heavy rain is moving through the terrain.

Analytics can pinpoint crucial aspects of storm water runoff, such as:

  • Places where roads and drains intersect
  • Road and drain segments that need repair or replacing
  • Bottlenecks in the flow of water and cars
  • Times and locations where rainfall will be heaviest
  • Locations of road and drain repair crews and police

Of course, meteorology continues to benefit from more sensors and computer power, making weather forecasts more accurate and providing earlier, more specific warnings of storm activity.

Once you have all this data and analysis in a single situational intelligence application, you can start to improve storm water operations and planning.

In operations, you could notify police and crews before the storm hits, so that they can be in place to help. You could also notify news agencies and social media outlets to announce what streets to avoid to prevent accidents and traffic jams.

In planning, you can predict areas where runoff will be heaviest. By overlaying this data with data about road and traffic conditions, you can target your storm system investments to places of highest risk for road damage and traffic problems. Taking a proactive approach helps you stretch your operations budgets further while also positioning you well for the next storm.

The West needs the rain to counter the past four years of drought. We’re not complaining. We just need a way to better manage the impact of heavy rains on our heavily traveled roads.



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.



Waterway Management and Situational Intelligence



When I say “water transportation,” your mind might conjure romantic images of the canals of Venice, Italy, with gondolas and vaporettos ferrying locals and tourists down the Grand Canal. Or maybe you’re the cruise ship type.

But did you know that

Commuters, tourists, trade and cargo move along these and many other waterways around the world, which are vital parts of their surrounding economies.

Like their terrestrial highway cousins, waterways are linear assets. Linear assets pose different challenges from fixed assets, such as power transformers, and mobile assets, such as ships. Highways and waterways share a similar set of concerns about quality, environmental impact, accommodating mixed use, seasonal weather and traffic patterns, and more.

Instead of parts like a machine, linear assets have segments. The size and numbering of segments can vary widely. Characteristics of the asset can change over distance, such as water depth and rate of flow for a waterway. Different characteristics can change at different points in and across different segments.

Plus, there are relationships with parallel linear assets, such as power lines that supply energy to the fixed assets along a waterway. There’s constant interplay between the fixed, mobile and linear assets based on time of year, time of day, weather and other factors. All this change and variability means that analyzing linear assets quick gets complicated.

Situational intelligence applications, with the combination of spatial and network analytics and visualizations, work well for monitoring, managing and optimizing linear assets such as waterways. They offer the breadth and flexibility of data integration, analytics and visualization to handle the complexity of waterways and the myriad other related assets—fixed, mobile and linear. Users can analyze and visualize characteristics in real time across dynamic segments.

I apologize if I’ve ruined your romantic conceptions about water transportation.

(Image “Venice grand canal rialto bridge 2012” by chensiyuan. Licensed under GFDL via Wikimedia Commons)


Can Connected Cars Learn to Increase Their Operational Efficiency?


You become more adept at driving familiar routes over time. You learn the best times to leave to avoid traffic, which highway lanes move faster, and where large puddles form after heavy rainstorms. Could your connected car also become more adept over time?

Using the spatial, temporal and network analysis of situational intelligence, a connected car could learn your normal commuting routes and times, correlate those with weather records and your fuel efficiency performance, and offer suggestions for how to drive the route more fuel efficiently. For connected hybrid cars, such data could be transformed into optimizing when to draw on battery charge versus gasoline for more fuel efficiency. Similar analytics might make alternate suggestions if you prefer to make the trip as quickly as safety allows, regardless of fuel efficiency.

Connected cars could perform their own version of air conditioning demand response. On hot days, cars could analyze the forecasted outside temperature along your route, or draw on outside temperature readings from other connected cars ahead of you. Those other cars could also share their operating performance related to specific stretches of road. Your car would then combine this with your interior comfort preferences and your current fuel supply and usage.

Based on all this information, your car could control your air conditioner in much the same way your utility controls air conditioning during demand response event, pre-cooling the cabin on flat stretches of road then throttling back cooling while driving up hills. This would mean better fuel efficiency for you without sacrificing comfort while you drive.

Demand response is just one way that connected cars could learn from one another to increase operational efficiency. Crowd-sourced predictive maintenance might be another way.

Suppose that past data shared between connected cars shows that drivers in your city with your make and model of car tend to need their water pump replaced after 57,000 miles of in-city driving, regardless of highway driving miles. Based on this data, your car could warn you of a potential pump failure before it happens.

Your car could even, with your permission, share this water pump information with others. Your mechanic might receive the update and order the part ahead of your next appointment, saving you time at the shop and preventing a future breakdown while out on the road. The car manufacturer could use this water pump information to improve product design and reliability and inform dealers of potential issues. Makers of after-market car parts might pay to receive this sort of reliability information to help them design and market products.

Connection, analytics and machine learning are still on the horizon for the average car, but that horizon is fast approaching. I think we’ll find great new conveniences and operating efficiencies as that horizon draws near.


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.



The Industrial Internet of Things


Except for smart electricity meters (if your house even has one), the Internet of Things is likely not invading the average home in the near future. Individual consumers are having a hard time justifying connecting their refrigerators and dishwashers to the Internet.

On the other hand, the Internet of Things is likely already up and running in your workplace and your city. For years now, cities and industries have been cutting costs and improving service by connecting their processes to the Internet. Consider these uses:

  • As this Venture Beat blog points out, a GE jet engine generates 500 GB of data per flight. Fly a four-engine plane, that’s 2 TB of data. Think about that on your next business trip.
  • In areas with toll road and toll bridges, planners and researchers use data from toll payment transponders to study traffic volumes and patterns.
  • Harley-Davidson has connected the machinery and systems in its York, PA manufacturing plant to automate workflows and optimize production systems without human intervention.
  • As mentioned in this Accenture report Apache Corporation, an oil and gas company, is using IoT approaches to predict and avoid pump failures that reduce production.

Some have taken to calling this the phenomenon the Industrial Internet of Things. There’s even an industry consortium.

It’s in this industrial arena, and not your house, that situational intelligence shines. Situational intelligence excels at analyzing and visualizing multiple, disparate silos of data. Companies and cities are where you’ll find those silos of data.

This isn’t to say that individual consumers won’t benefit from the Internet of Things. Fewer traffic jams, more reliable service, higher quality products, better environmental protection—all are possible using situational intelligence to analyze and visualize data from the Industrial Internet of Things.