How Will Big Data Impact the Future of Transportation? – Part 3

Big data is already being used in transportation sector, for example, to improve road traffic managements and the planning of public transit services. In our interview, Vinay Venkatraman, an interaction designer explains how new technologies can enable the use of big data in mobility industry to provide safer, cleaner and more efficient transportation system.

How can we use big data to understand the needs of transport users better?

Venkatraman: In transportation industry, the vast majority of users are end consumers who just want to go from point A to point B followed by more complex needs for companies or operations. There is a distinction between how you can use data to create more value for users and also to understand their needs at the same time.

In the B2C segment, one of the important things is cost and fuel optimization, something that every end consumer really wants as they want to save money. That is something that data can be extensively used for: to detect driving patterns, for example, to suggest more fuel optimized routes and greener ways of travelling. Those are all sort of key values where big data can be used and today, it is totally doable and possible.

We are working on a proposal for a pilot project for an insurance company where we are collecting data from a car and giving value back to the end consumer to create new ways of organizing their travel. There is a vast amount of data being collected, we are running extensive machine learning algorithms as well as pattern recognition tools, in order to give deep insights to the consumer on how to improve driving performances, in order for them to be safer and make it more cost and fuel efficient for themselves.

Those are just small ways in which it can be done. The amplitude of how much of it can be used is phenomenal. And the real question is which kind of data creates what kind of value for whom – that is the big puzzle to solve. Just creating new ways of harnessing data as well as giving back value is the only way we can understand the needs of users better.

Can you give us some current applications on how big data is used in the transportation industry?

In the marine transport industry, there are many innovations being put forth right now on how big data can be used. One use case is, for example, mitigating the risk of collision in the high seas, to reduce accidents and damages to people and cargo. There are big initiatives being formed on how to collect which amounts of data and make the best use of it in totally new ways – both for guidance, safer navigation at sea and real-time support to the crew on the ship.

But there are also attempts in large scale predictive analytics where we can identify risk areas based on the combination of weather conditions, traffic densities as well as the presence of much smaller boats compared to bigger boats. This sort of advanced analytics is of phenomenal use since it dramatically improves safety on seas.

How do people and companies need to be set up in the future in order to deal with big data generated through mobility?

There is a need for new types of talents, we need more cross-disciplinary and cross-talented people who can deal with technical, analytical and synthetical skills – we need people skills in combination with technological skills to work with these kinds of new unstructured big datasets.

One thing is to have an expertise in people – the other thing is knowing how to collaborate with this expertise. The collaboration skills required for big data are much more important than just the data infrastructure and technology.

There is a need for companies to be a lot more agile in their way of working with data because the velocity and pace at which data comes and goes is so high that if we have a very structured and very slow process in an organization, it is often too late to even know what to do with that data because data is transient which means that it constantly flows and you cannot dam it. If you dam it, there is a high risk of flooding in your organization.

Therefore, you need to know how to regulate it and to make the most out of it – you need to have the agility and technologies required to deal with the big flow of data. That is something where companies have a long way to go in terms of learning, especially in the transport industry because it is centuries old. Since it is something that has been set in stone for a long time, making changes is pretty complex and very difficult.

Can you give us some insights on future applications on how big data can be used to provide an efficient transportation system?

The future of efficient transportation is highly multi-modal and highly multi-dimensional. As we all know multi-modal means using many means of transport. This has been in existence for a few decades now. But, multi-dimensional is when you change the mode of transport, you also change the user experience, the data flow and pricing structures, there are so many variations that happen when you change modes.

This multi-dimensional transport application is going to be the next big thing in the transport industry. The efficiency is one side of the coin and the other side of the coin is user experience. People want to choose the most interesting way of travelling.

It does not always need to be the most convenient or the cheapest. Off course, cost is a high criterion, but not the only one – people also want a comfortable journey, they want a nice experience and most importantly, people want to be engaged.

Hence, there will be new strategies evolving in the multi-dimensional system that include gamification, crowd sourcing and many other ways of harnessing all these data and making sense of it for the benefit of the end consumer or the person travelling. We are going to see a lot of innovations and technologies that are going to emerge in the next decades.

Share your opinion in our comment section: Do you think big data will help us to personalize our transport experience?

Click here to read the first part of the interview

Click here to read the second part of the interview

Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

How Will Big Data Impact the Future of Transportation? – Part 2

The emergence of new mobility innovations and technologies has led to an increase in both volumes and the complexity of big data. In our interview, Vinay Venkatraman, an interaction designer explains how different factors and challenges are affecting big data in the transportation industry. He also shares his thoughts on how storytelling and data visualization can help to solve the complexity of big data.

What are the key factors affecting the growth of big data in the transportation industry?

Venkatraman: One of the challenges that we face when dealing with transportation and transit is that there is a lot of legacy infrastructure embedded in the transportation industry, whether it is train systems, booking systems, traffic management systems or signaling systems.

These have all been built over the decades in varying periods and they have a completely different approach to technology – they were never thought through as a system that would be connecting to much larger data grids.

So, the biggest issue is that there is a big challenge in collecting data out of legacy infrastructure. You need to put new sensors in place, new data collection methods. As a small example crowdsourcing of data that is not thought through as a valid way of collecting data on the transportation industry. Those are all the main factors affecting the growth because the raw materials that you require to drive big data innovation are not easily available.

Even though the infrastructure from a technology point of view exists in many of our industries, the transportation industry is lagging behind a bit. For example, the maritime industry has long legacy issues as well as the transportation industry. While, the airline industry is making a large leap on how data has been used in a very big way. So, it is a paradox because it depends on what you define as transportation today.

If you look at the whole automobile industry, there are extreme spectrums – the ones which are aggressively collecting data, almost building a data of platform with four wheels and there is the other end of the spectrum, where it is the usual engine and pieces of metal and then data is typically an afterthought. So, there are all sorts of automobile companies that cater to the data revolution in different ways, some are able to cope much faster and some are able to cope less.

What are the main challenges associated with big data in the transportation industry?

The primary issue is that we do not have the ability to influence control systems, for example, traffic lights. Hence, we are not able to close the loop fast enough by collecting data, processing it and feeding it back to an actionable task or a system or a control. The feedback loop to a control system is again a legacy infrastructure issue and there are a lot of safety and security issues around it.

For example to highlight the issue, we are working on a project collecting data on air quality in the city of Copenhagen. We are trying to feed it back into the traffic light system, so that we can modulate the traffic flow in such a way that it does not adversely affect air quality in any one place. Just the loop of going through that whole process is extremely tedious and it is the single biggest issue that we are facing right now.

This is mainly because organizations are not ready and there are many different stakeholders involved. Each stakeholder has to have an interface that works with the other from a machine to machine level, but it hardly exists today, typically due to incompatibility data formats and technology systems.

Secondly, the complexity and bureaucracy involved in multi-modal transport is a problem. However, the mega-scale issue has nothing to do with technology, it is more about mindsets and how policy and governance is structured around who owns the data, which part of data, how do they interact, what kind of common platforms do they have and what interoperability standards do they have.

We do not know what kind of people and organizations will be able to resolve those issues, because it eventually boils down to how people operate, some countries are better than others and some private companies are much more agile in these methods and some are very rigid.

The last challenge we often face as a small company is that anyone trying to penetrate the transportation industry requires a lot of capital in advance to make big changes to the system and for small players to make big influences to the system is a big issue. Therefore, we need a new breed of risk capital and policy thinkers in order to facilitate new innovations and try new technologies in this business.

With an exponential rise of data, how can we harness and cope with the complexity of big data?

Firstly, you need high interoperability. Then you have to cope with unstructured data and lastly, you need to tell powerful stories. Storytelling and data visualization is the key element in dealing with the complexity of big data because humans cannot deal with an overload of data and information.

This process of going from data to information is somewhat reasonably doable today, but going from information to meaning is a whole new story. This layer of going from information to meaning is really about human insights – it is about storytelling, narratives and making things visual.

So, the more visual you get in data, the easier it is for people to comprehend and make sense of it. We need to do things that will enable us to take this information and create relevance and meaning in society and organizations. That is the only way we can deal with the complexity of big data.

You said visual representation is one of the key solutions; can you give us an example of one such representation that helps to solve the complexity?

For example, we are working with a corporate travel planning company where we are building a data visualization tool with sophisticated patterns recognition to see the most optimal routes that passengers or employees of a certain company take – taking a look at the most commonly used routes, what fuel efficiency, what cost efficiency and what risk exposure they have – all in one unified view.

This tool allows the travel planners to slice and dice the data in various ways and build scenarios to better help their end users.

We have built a single dashboard solution which allows us to investigate the complexity of air travel – we are talking about hundreds and thousands of tickets per year. We have compressed all this into one unified view which allows the viewer to basically navigate this data landscape in minutes rather than spending days or weeks trying to run complex queries and figure out how to build structured reports.

The time to gather insights is a big leap. We are able to see visual patterns as a very strong trigger for driving the right kind of questions. Off course, you cannot find all the answers in the visual patterns but you can ask the right questions. Asking the right questions and using the right strategy is half the game.

Leave a comment: There are many apps developed based on open data which helps to plan day-to-day journey better. Do you already use such apps? If so, tell us which ones and how they help you in your everyday commute.

Stay tuned, the third part of the interview is coming soon

Click here to read the first of the interview

Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

How Will Big Data Impact the Future of Transportation? – Part 1

Big data is everywhere and it is increasing exponentially. The biggest challenge is how people and companies are going to use this large amount of datasets. In our interview, Vinay Venkatraman, an interaction designer speaks about how big data can drive future mobility innovations. He also shares his thoughts on how bridging the world of design and technology together will create new solutions for people, business and society.

What is big data?

Venkatraman: Big data is the infrastructure as well as the social capacity to handle large amounts of data synchronously and it is composed of both structured and unstructured data. There is a lot of discussion around volumes and velocity of data, but more than that, the key way we look at big data is that there is definitely an exponential rise in both volumes of data and also the complexity and the nature of data.

This combination of handling large volumes of data at very high speeds in combination with heterogeneous approaches is what is really making it interesting as well as challenging.

At any given point of time in history, for the technology infrastructure that was available, data has always been big. Whether it was a card punching machine, a hard drive or any other technology that came around that understood some form of digital data.

It has never been such that it has managed to cope, the data factor has always been bigger than what technology could offer. So, there is nothing new about this concept, except that there is a new social phenomena around on how people are going to use this large amount of heterogeneous datasets.

What is the difference between structured and unstructured data?

The primary difference is that structured data is well thought through in advance as to how data is going to be stored and how it is going to be used. Whereas, with unstructured data, you do not know what type of data you are collecting and there is a big variation in the type of data and the structure of how data is stored and retrieved.

That is the fundamental difference from a principle point of view, but technically there is a big difference in how their underlying database technologies are evolving to suit structured and unstructured data.

There is a lot of push to develop unstructured data collection methods and database technologies – partially because, there is a phenomenal rise in unstructured data compared to structured data. We people on the street as well as large businesses and governments are all generating unstructured data in large volumes.

Currently, this generation of unstructured data is really a big phenomenon. For example, twitter feeds or even emails contain deep insights into a lot of influencers, but it has not been harnessed in effective ways – mainly because of privacy issues and because we do not have appropriate methods and technologies to deal with it in a meaningful way.

But on the other hand, structured data is much easier to handle because you can store them neatly in rows and columns, arrange and sort them or filter them – for example in its simplest form, a typical excel spreadsheet could be said to be a structured dataset.

So on the contrary, the complexity of unstructured data is what will make new opportunities unfold compared to structured data. Traditionally structured data has been good at record keeping but unstructured data can offer phenomenal insights and act as raw material for new innovations.

What is open data and for what purposes can it be used?

Open data is just a matter of principle and policy – it has nothing to do with structured or unstructured data. You could take structured or unstructured data and make it open. With open data, opportunities are immense, because people can share and build upon it.

It is typically in the interest of public agencies and governments to actually open up data as they want people to harness and make use of it. Such open data can become the engine to drive new types of business and also augment the efforts of existing business.

But, we should be aware that public agencies often also collect sensitive data. For example health data or other personal details, there is a certain level of governance of data that is required to manage this securely – you do not want to reveal either for security or privacy reasons. At the same time, there is a vast amount of data which is not so critical to anyone, but in general creates a lot of value for society.

New tools, approaches and systems can be built on it and also new businesses can thrive on it. The biggest need of the hour is to offer the right degree of permeability to such open data, so that it can be put to good use and at the same time protect the privacy and protection of individual’s rights.

In summary, there are a lot of opportunities for using open data as a raw material for creating growth. Though the government models, policies and legislations around it have to be well thought through, in fact, that is one of the biggest challenges many governments are facing right now.

Leave a comment: Have you faced any bad experiences sharing your personal data? Or do you think sharing data will provide a better user experience?

Stay tuned, the second part of the interview is coming soon

Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.