Heavy vehicle usage has increased steadily over the last decade, with most government agencies in Australia recognising the at figures will double by 2030 to meet demand and population growth. This places an additional burden on the existing freight routes and transport network and has a significant impact on road maintenance, safety, infrastructure development, road life cycles and driver behaviours.
There is a significant amount of data being generated by various intelligent transport systems, GPS/navigation systems, operator fleet management systems and from regulatory and enforcement bodies. This data has the capacity to transform how government manages the transport network to provide a safer, fit for purpose infrastructure system as well as providing valuable insights into driver behaviours, compliance with legislation and predicting future trends and demands.
There are also some significant benefits for the heavy vehicle/transport industry as they would be able to leverage the data with their insurers to reduce premiums with evidence of high performing drivers, full compliance with legislation and a strong safe record.
Some of the potential applications of big data analytics in the heavy vehicle arena include:
From a safety perspective we are trying to detect unsafe driving patterns and events using on-board sensor data from in-vehicle units. As safety is a primary concern we feel that it should feature heavily in any big data solution, an initial approach would be giving the solution the ability to detect patterns like excessive breaking using force detection. Force detection is a means of detecting changes in acceleration and angular rotation which can be used to detect movement patterns and sudden events.
An example of using force detection for a safety application came be taken from the health-care industry for protecting staff in remote areas of hospitals. A device known as a dead man switch (DMS) is used to detect body movement of staff members using force detection. If a staff member is incapacitated, as there are drastic changes in body movement, an alarm is immediately sent to hospital security to attend to the staff member.
Construct Agility recently completed a mobile app to replicate the DMS to extend the use of this safety feature to all staff using their smartphones. This application is currently being commercialised and has given us some great insights into the use of force detection for safety as well as other applications.
Having the ability to detect these forces in heavy vehicles can be used to detect commonalities from a safety perspective. Detecting occurrences in the same location could point a need to reduce speed limits at a jurisdictional level. While at an operator level detecting occurrences could point to behavioural issues where an operator/driver was not adhering to safe driving practices.
Is the current road network being utilised to capacity? Where do we need to invest in new infrastructure? Being able to answer questions like these will be possible and allow Government to make data driven decisions when creating and adapting policy for the future as well as practical operational usages around road maintenance, access and road pricing.
Road Usage - Pricing Model:
Our vision t is to incorporate the available data with other related transport department data sources such as road type, geo-spatial data, road cost & design specifications and combine these using a big data approach. We can then calculate a cost associated with travel on a specific section of road and which would allow regulatory bodies to calculate a Mass-Distance-Location (MDL) price for each operator based on exact usage. This data would give industry who participate in the program, an opportunity to pay fees based on actual usage. This would also be a feature to encourage more industry application.
Compliance with Regulation
The data collected, would be able to identify driver behaviour including speeding, excessive breaking which are against regulation. To increase industry participation, it would be useful to discuss a discount scheme with insurers should the company’s fleet be in the “high-performing” category.
The future of driverless cars, drone deliveries and other technology driven solutions will change the transport network significantly. The insights, data and analytics captured from heavy and private vehicles should be used to drive policy & decision and will make the integration with new technologies easier, safety and more effective.
Analysing breaking behaviours of HV drivers will allow government to allocate a cost to the repair and reflect this in a pricing structure. It will also identify if maintenance is required sooner that would be scheduled. The opportunities in this area are endless, a heat map of potential hot spots by comparing the core data with other data sets (accident reports; temperatures to indicate issues with asphalt; HV turning points on roads etc) would act as an early warning system for maintenance works.
Current driver information comes in survey format and reliability and accuracy is questionable. Once behaviours are being analysed, you can target the right education campaigns to the right people in the right area, increasing effectiveness and return on investment.
What are your thoughts on big data in the infrastructure space?