(Macro) data is dead, long live (micro) data​: How more granular data can make our roads safer and more profitable

Valerann
Valerann
Sep 9, 2020 · 8 min read

Traffic management, as we know it today, originated in 1937 with the first traffic counter. Operated off a strip laid across the street, and used a six volt battery. Each hour it printed off a paper strip with the total impressions for that hour. This strip was replaced by pneumatic tubes in the 1950s, magnetic loops in the 1970s, and then radar and camera based vehicle recognition systems in the 2000’s. While a lot has changed in terms of the sensing technology, traffic management has not. This is because the actual data collected by these sensors has not fundamentally changed since 1937: high-level statistics of the number and speed of vehicles that past at a specific location along the road (e.g. at KM marker 122.0 between 9:45:00 and 9:50:00 AM, 120 cars passed in lane one at an average speed of 83 KM/Hour).

The data generated by this method is academically referred to as macroscopic data or ‘sample-based’ data: discrete observations from single locations (e.g., 1 loop per KM) are used to deduce what is happening in the rest of the road. These data samples are used by road operators (private and public companies entrusted with the safety and efficiency of our journeys, and the upkeep of our roads), to actively manage traffic, reduce congestion, and prevent accidents. To collect this data and use it to manage road networks, operators spend upwards of $1.5M per kilometre on traffic management and monitoring technologies and tens of millions of Euros on control centres to make sure our journeys are safe and efficient.

For some use cases, macroscopic data is helpful. For example, if vehicles are rapidly slowing down in one part of the road, it is safe to assume there is congestion in another. For other use cases, where this data is applied, the effectiveness is not so consistent. Systems such as MIDAS used in the UK (Motorway Incident Detection and Automatic Signaling) use observations of traffic speed to determine whether an accident, stopped vehicle, or other obstruction is blocking a lane. This means that if an accident occurred, it would not be directly picked up by the system. The control centre would only receive an alert once that accident caused enough cars to slow down and create congestion, which was then picked up by sensors upstream from the event. Not surprisingly, the majority of accidents picked up by operators are actually called in by drivers.

In an age where we have data about almost everything in our lives, it is strange that we do not have comprehensive data for roads: the largest, most intensely used, and most dangerous infrastructure on earth

$1.5M per kilometre for 2–3 data points may seem like a steep price tag. But, it is worth mentioning that these systems can display incredible value-for-money in improved management of congestion, reduced emissions, improved incident response and lives saved. Still, in an age where we have data about almost everything in our lives, it is strange that we do not have comprehensive data for roads: the largest, most intensely used, and most dangerous infrastructure on earth. Indeed, academia has been calling for a different type of data that could drastically improve the safety and efficiency of our roads by as much as 30%: microscopic data.

Microscopic data, by contrast, provides full visibility of all road events. It gives the control centre a view of the exact location of every single vehicle on the road, in lane-level accuracy, in real time. Basically, it provides a live 2D aerial-view map of the road, including all vehicles, in real time.

This granularity enables four types of insights that have not been feasible to-date.

  • Vehicle-level insights: the ability to trace the movements (anonymously if required) of every vehicle on the road will enable the road operator to detect whether any specific vehicle is at risk in real-time. Alerts can be provided the moment a vehicle stops in a live lane, departs from the road at speed, or crashes. Likewise, should the vehicle be driving dangerously, the control centre can be made aware of this to keep track of the situation.
  • Inter-vehicle insights: the ability to trace how drivers and their vehicles are responding to other vehicles in their vicinity. If a vehicle slows down and another needs to suddenly swerve to avoid a collision, or if an initial bottle neck forms, both will be captured by systems providing microscopic data. This will enable the road operator to be more proactive by better locating bottlenecks before they become congestion, and collecting comprehensive information about near-misses to better prevent accidents
  • Road-level insights: Combining information of specific events (be they vehicle-level or inter-vehicle level events) with data from up- and downstream traffic, the operator can improve its operational decision making process. Connecting a forming bottleneck with the volume and behaviour of upcoming traffic will give certainty to congestion forecasting, contextualising a risk posed by an individual vehicle with the traffic conditions in its vicinity: a stopped vehicle that is 1 meter away from the live lane, during stand-still traffic, is at must lower risk than a stopped truck exposed to live traffic, with fast-moving vehicle approaching, during bad weather conditions.
  • Infrastructure-level insights: microscopic data does not pertain to traffic flow alone, it can also be applied to infrastructure monitoring and maintenance. Today, costly maintenance decisions are also made with very limited objective data. Gritting and ploughing are planned based on weather stations that are often more than 40KMs apart, while road temperatures can change between lanes depending on traffic. Likewise, the planning of resurfacing is based on high-level information about traffic levels and weather conditions. Research suggests that improved data about exactly where maintenance is needed (e.g., where exactly to grit, or what area precisely to resurface) can reduce maintenance costs by as much as 50%, which can amount to over $30M for a mid-sized road operator.

These capabilities are not just crucial to improve the safety and efficiency of our roads. As vehicles become more connected and autonomous, they will require better data to maintain safe autonomy. The data current systems provide is useless for CAVs, as it is aggregated, non-current, and sample-based. Rich, granular, comprehensive micro-data could support these future technologies and make road operators a central player in the future of road-based mobility.

With these huge benefits to lives, time, revenue, and costs, it is natural to wonder why industry has not sought to acquire microscopic data in the past. The short answer is that the economics did not make sense until now. Traffic management requires resilient systems that work at all hours. This resilience has only been afforded by robust expensive infrastructure such as powerlines, communication cables, stable road-side poles, and gantries. These pieces of supportive equipment are expensive to install along long stretches of roads and even more expensive to ensure continuous data-collecting road cover (a single road-side camera including its supportive equipment costs over $100K). These astronomic costs meant that the operators could only afford to collect data from specific locations and try to infer from that data what is happening on the rest of their network. This issue was compounded by a proliferation of technologies that have become more and more niche’ with time. A road operator would need separate systems to count traffic, detect accidents, monitor weather, and detect dangerous behaviour (such as speeding), creating a fragmented solution that was expensive to procure, more expensive to install, and more expensive still to integrate.

Today, with advancements in IoT such as wireless communication, solar panels, batteries, edge computing, and cloud hosting, microscopic data is finally accessible. Road operators today can leverage these new IoT technologies to collect, analyse and leverage this data to improve their roads’ safety and capacity, increase their revenues, and reduce their costs. Indeed, roads could enjoy a 31% reduction in fatalities, a 20% reduction in congestion, an 13% increase in toll revenues, and a 50% reduction in maintenance and operational costs. Likewise, the nature of the solution means that all use cases can be monitored and managed from a single platform. If an operator wants to begin monitoring a new type of event (e.g., wrong way driving) she no longer has to deploy new equipment on the road; rather she can just query the data in a new way–much simpler!

Of course, each road is slightly different, so road operators will need to be decisive and focused to enjoy these benefits in the long term. Road operators that want to begin the transition to microscopic road management should follow a few simple steps:

  1. Define the use cases they care about most first — It is tempting to look at all use cases at once, but leveraging microdata will require the operator to act on these new insights. If the operator tries too many use cases, he may find it challenging to adapt its operations to reflect and capture these improvements. So, operators should choose the most valuable use cases and focus on those first. Express lanes can look into better pricing to maximise revenues, while motorways can seek to improve incident and bottleneck detection to maintain road capacity. Once the operator has tailored the system to these specific use cases, she can begin to enjoy the broader benefits of the unique data at her fingertips.
  2. Start with a single location, and expand with the improving economics — operators do not need to make the transition in one go. Microscopic data can benefit the road from the first road segment. Operators should identify the road segments that would benefit most from better visibility and control and begin using the system there. Once installed, they can begin expanding the system as it proves more and more value.
  3. Expand both locations and use cases — once initial value is proven in one location, the operator should begin expanding the use of the system to more locations, trialing more use cases. This will improve both the economics of the data and the economics of the road, increasing the value of the road asset itself.

Microscopic data is becoming available. Road operators that learn how to leverage this data will improve the safety of their roads, the flow of traffic, the revenues they make, and the costs they bear. The operators that learn to leverage it first will have a competitive advantage in new tenders and bids. In the long term, this data will enable roads to become a key player in an ecosystem of partial and full autonomy. Thanks to advancements in IoT technology, these benefits are now available at a significantly lower price tag than current systems, while providing a more resilient system. It is time to declare: (Macro) data is dead, long live (micro) data!

Learn more about Valerann’s technology that provides operators with microscopic visibility of all road events at valerann.com

Valerann

Making roads smarter

Valerann

Valerann is re-thinking how roads are operated by using IoT to provide visibility to our roads and road management. Our publication is how we see the future of road operation.

Valerann

Written by

Valerann

Developing operating systems for roads, transforming them into data generating infrastructure that make our journeys safer, faster, and autonomy ready

Valerann

Valerann is re-thinking how roads are operated by using IoT to provide visibility to our roads and road management. Our publication is how we see the future of road operation.