Connected Vehicle Data for Road Safety and Maintenance
Like many technological buzzwords, connected vehicle data has been heralded as game changing for areas as wide ranging as improving transportation efficiency, enhancing vehicle safety, reducing emissions, and enabling new mobility services. But this can seem nebulous when placed in a real-world context. What does connected vehicle data actually mean for those responsible for road safety and maintenance? What demonstrable benefit can connected car data have for those facing the challenges of road maintenance today?
John Cartledge, Global Development Manager at Gaist, explores…
What is the connected car data we are talking about?
As I’ve previously explored, the vast majority of vehicles produced today generate a much broader range of data from various features on the vehicle than ever before, much of which the user will never see or be aware of. One source of the data in question comes from Controller Area Network (CAN bus) data from onboard vehicle sensors such as the vehicle’s accelerometer. As is commonly understood (and suggested by the name), the accelerometer measures acceleration, which in real terms means changes in speed or direction. However, the complexity of onboard sensors in modern vehicles means the measurements can include bumps and vibrations, sharp increases or decreases in velocity (anomalous vehicle acceleration or braking) and forces that might indicate turning too fast, or strong impacts experienced by the vehicle/user.
How is this data useable? Surely there is too much of it to be meaningful?
The first step to is made possible by harvesting millions of Vehicle Sensor data traces from different types of vehicles and perform methodical analysis to provide insights. Understandably ‘millions’ sounds like it’s too big a number to make any difference- it’s surely too much data to inform road safety/maintenance activity?
The key here, and the part of the equation that really provides benefits, is the ability to analyse trends in the data to produce fascinating insights into collective driving behaviour. Understanding actual driver behaviour in a meaningful and useable way depends on transparent and methodical data processing on a large but entirely achievable scale.
To be clear this doesn’t mean just flagging every time there is a harsh braking incident on the network, as you can imagine this would encompass every single anomalous reading on the network which would create way more problems than it solves. It doesn’t help the road safety/maintenance professional in their day-to-day role know where to look. What we are looking at is marking areas on the road network where repeated patterns of erratic driver behaviour can be identified and twinning this with visual data (and other data sources) for verification purposes. We can distil all this data in to a manageable and practical format and add contextual information to assist road safety and maintenance professionals gain enhanced insights into their road network.
So what exactly does that look like for those managing road safety and maintenance?
As previously outlined, the ability to process the huge volumes of data and identify high-risk areas where driver behaviour is repeatedly noted as erratic (for example repeated incidents of harsh braking in a given stretch) means areas for investigation can now be precisely geolocated on a map of the road network.
This can be twinned with other data sources available and flagged for investigation by trained road safety professionals- when contextual high-definition imagery is added, this provides a powerful picture of where road safety and maintenance issues exist and could be developing.
This serves two purposes in the first instance- the ability for the road safety/maintenance professional to verify their existing local knowledge whilst also identifying high risk areas on their network they may not be aware of. This gives professionals in this area a new tool in their arsenal to use their limited resources where they are most needed, prioritise maintenance and improve road safety.
Previously this data has not been available in a useable offer, from your desktop you can now see the highest risk areas across your whole network alongside up-to-date imagery of the area in question. The heavy data lifting is done in order to give you neat, easy-to-use outputs that can inform maintenance and road safety infrastructure projects. What’s more, as this technology develops in terms of outputs, there is huge scope for tailoring the data outputs in partnership with highways authorities/professionals- this use of connected vehicle data is in the vanguard of making huge datasets useable and impactful for highways professionals and road users.
Interested in reading more? You can read some of the early results on our work with partners AISIN for local authorities here.
If you would like to hear more about Gaist’s work in this area and how we can help you gain advanced insights in to your road network contact email@example.com
What we are looking at is marking areas on the road network where repeated patterns of erratic driver behaviour can be identified and twinning this with visual data (and other data sources) for verification purposes "