Case study

Origin-Destination data for Rat Running

Understanding how drivers are using main roads as intended or local roads instead.

If it was found that people were using residential streets to avoid the main road, then the council wanted to reduce the instances of these rat runs.

Rat running is problematic because local roads cannot handle larger volumes, which can disrupt local communities and residents.

Where vehicles are theorised to travel.

The council used connected vehicle data answer the following questions: are vehicles using one or more shortcuts? Which shortcut is being used the most? Are there any other shortcuts in the area? What is the time difference between using the main road vs the local roads? and what percentage of drivers are using the shortcuts compared to the main road? The council also compared average travel time for the various paths taken, average speed for said paths, times of day (e.g. peaks and off peaks, school peaks, etc.), and percentage of vehicles using the rat running route

Image showing where cars travel most during evening peak hours through Park Avenue.
Where cars travel during evening peak hours on the main road.
Where cars travel during non-peak hours at Park Avenue.
Where vehicles travel during non-peak hours near Early St.
Where vehicles travel during morning peak hours around Park Avenue.
Where vehicles travel during morning peak hours on the motorway.
Where vehicles travel the most.

The council established that 48.9% of driers were using shortcuts in the morning peak across different local roads and that more drivers used the local roads on weekdays compared to weekends. By enabling the council to gain insight into how drivers behave on these roads at certain times, there can be a clearer idea of how and where to prevent these rat runs.

A table and chart showing the volume of connected vehicles travelling through a particular road on a particular day.
Use cases

How our customers are using Connected Vehicle data

Applications of vehicle-generated data for use cases across state-wide freight modelling, origin-destination studies, VMS signage effectiveness, road safety, and local area traffic management.

Crow distance travelled by specified commercial vehicles around South-East Queensland.

Origin-Destination

A study outlining the origin and destination of 20 million Commercial vehicles trips across southeast Queensland.

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Street view of Castlereagh Highway in Ben Bullen prior the train level crossing (going southbound).

Identifying dangerous train level crossings

Analysing driver behaviour at Ben Bullen to determine infrastructure and driver safety levels at the local train level crossing.

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Visual representation of movement along the M7 at Glendenning and at the offramp.

Identifying Spillback

Transurban analysed driver behaviour to identify spillback, congestion, and how it affects road users.

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