Innovative urban transportation

Innovative urban transportation

The smart cities of the world are always seeking to provide urban transportation systems that allow you to get from A to B with minimum fuss. Thanks to a new wave of technological innovation, smart cities are set to achieve higher levels of transport safety and efficiency than previously thought possible.

Our multi-disciplinary research teams are investigating opportunities to improve virtually all aspects of urban transportation:

Accurate, affordable, city-wide traffic measurement

Among our most significant current projects is the creation of a low-cost, laser-based traffic monitoring device that promises to enable detailed studies of entire road networks’ functioning.

According to lead researcher Dr Jamie Mackenzie, a roadside-pole-mounted device will enable the gathering of valuable, potentially life-saving data with unprecedented flexibility and affordability.

“A fully developed device that’s affordable enough could be deployed en masse across a city’s road network and enable large-scale monitoring,” says Dr Mackenzie.

“That includes the dense deployment of multiple devices along a single corridor, which would allow us to see how vehicles change their speed in response to various objects or conditions. That’s rarely achieved, and could inform all sorts of decisions regarding traffic management and driver education.”

Passing-distance indicators

Another high-tech device we recently designed and developed attaches to bicycles and measures the distance to passing vehicles, while also tracking the bike’s location and speed.

Dr Mackenzie says the device will deliver crucial information on how vehicle passing distances change in relation to factors such as speed limits or the presence of a bicycle lane.

“With further development, a significant number of devices could be given to volunteer cyclists across a city to map dangerous locations, and evaluate the real effectiveness of minimum passing distance laws.”

Machine learning video analysis

Also in the area of cycling safety, we have developed video-analysis software to detect and count cyclists on the road, rank their visibility and gauge their speed.

The software, initially developed as a pilot for the South Australian Motor Accident Commission, could allow authorities to determine cyclists’ exposure at particular points in traffic networks without having to install specialised counting systems.

“The manner in which this works is novel in approach, as it uses machine learning algorithms,” says lead researcher and postdoctoral research fellow Dr Zygmunt Szpak. “That’s quite different to previous vision-based detection and tracking systems.”

Demand for the technology is strong, adds Dr Szpak, but the software’s specialised nature means continuous use is not yet feasible. With this in mind we are now working to adapt the approach for use with licensed software.

Making the most of advanced driver assistance

With modern vehicles adopting increasingly advanced technology to assist drivers and improve safety, we are developing similarly advanced testing equipment to ensure manufacturers’ performance claims can be replicated in real world, Australian conditions.

The equipment consists of custom-built robots that manipulate the steering and foot pedals of test vehicles to accurate tolerances. Foam vehicle targets are then attached to electric karts, which interact with the test vehicle in various scenarios, and if a collision occurs the foam target collapses safely and can be rebuilt for the next test.

The equipment could also play an important role in assessing autonomous vehicle systems and reconstructing autonomous vehicle crash scenarios.

Tagged in Societal wellbeing, ai, machine learning, transportation, smart cities