IoT For Transportation and Logistics

In this step, we’re going to explore how IoT functions in the transportation and logistics vertical.

Transportation advances are possibly the most exciting area in which IoT solutions are being developed. Getting from one place to another can be an arduous, frustrating, and sometimes dangerous undertaking. Anyone who has spent more than an hour sitting in traffic has said to themselves ‘there has to be a better way!’. What makes this vertical so exciting is the many opportunities for improvement and innovation it presents.

In many ways, IoT for transportation is a subcategory of the previous topic, IoT for smart cities, but the space is vast and there are many unique opportunities for innovation that it warrants its own topic. In terms of learning, it can be helpful to consider this topic together with the previous one.

In 2012, Google created a video (above) that demonstrates its early thinking on autonomous vehicles. Part of its vision is to connect each vehicle to a central service (a cloud service) and each other, thereby enabling the system to let each vehicle know where all other vehicles are at any given time.

This would mean intersections without traffic lights which would minimise the need for vehicles to stop or slow down as they entered an intersection. Using predictive analytics and location awareness, vehicles would know where they are in the traffic and navigate appropriately. The video below shows computer models demonstrating this ability at around the 55-second mark.

Viewing Transportation Holistically

Individual vehicle advances are only part of the opportunity space that IoT can address. As we saw in the previous step, transportation needs to be viewed as an ecosystem with each part working together. The more complex the variables in that ecosystem, the more complex the solution.

Whilst their application isn’t in the transportation vertical specifically, General Electric is attempting to solve complex problems by processing large amounts of data (industry professionals call this Big Data). In an article – GE’s Big Bet on Data and Analytics for the MIT Sloan Review, the author describes how GE has developed an analytics engine to process 50 million data variables from 10 million sensors to enable robust predictive analytics which increases its ability to find the cause of problems and not merely an alert when a problem arises.

The problem space is similar to what we find in complex systems such as transportation, and any IoT solution will have to deal with the problem of big data. Bill Ruh, the Chief Digital Officer at GE describes the problem his team faces as if they were dealing with an organic life form:

‘Machines generate time-series data, which is very different than social or transactional data. We had to optimise for the kinds of analytics that would help us understand the behaviour of machines.’

This accurately describes the problem of transportation, even in a localised scenario such as a city. Vehicles, people, pets, and other objects move and have behaviours. Any IoT transportation solution has to account for those behaviours and orchestrate whatever variables are under its control–the vehicle, traffic lights, lighting systems, road or track modification systems–appropriately. This involves analysing enormous amounts of data in real-time and creating a decision engine that can set a large number of variables in a productive, efficient, and safe way.

Whilst economics is a factor in transportation solutions, governments may be more interested in efficiency and safety which creates a different dynamic for solution architects compared to the other verticals we’ve been looking at.

We’ve come to the end of the learning activities for Week 1. Before you move on to Week 2, please complete the Knowledge Check in the next step. Once you’re done, we’ll wrap up the week!