We are leaders in the use of Artificial Intelligence (AI) to understand how people respond to the world around them.
We have developed world-first AI-powered tools that accurately understand and predict how people use and move through our cities, at a strategic and a detailed level.
These tools allow evidence-based decisions to be made on how to design cities and transport networks that balance everyone’s needs: residents, commuters, commercial, retail, freight, pedestrians, cyclists, the elderly, families, those with mobility impairments and many more. They can help make our cities smarter, more liveable and more sustainable.
This AI analaysis has been used extensively in Sydney, NSW, but is applicable to any city in the world. Indeed, we would love to work with other cities: please do reach out if you’d be interested in exploring what AI could do for your city’s urban planning, walking and cycling.
If you would like to talk to us about any of these tools, arrange a demo or simply find out more, please let us know below.
Trellis AI: Advanced Transport Network Analysis
Trellis AI is a specialised artificial intelligence (AI) tool developed specifically to analyse, forecasting, and optimise transport networks.
It learns, understands and then accurately predicts how transport modes - including rail, freight, car, bus, bikes and micromobility - will respond to changes, such as network and service changes, population growth, land use change or improved interchanges. It takes a holistic, all-mode approach to network analysis, enabling planners to see how changes on one mode flow across to other modes.
The tool is the first of its kind and allows planners to identify network constraints, understand changes to networks and forecast demand changes. The tool ensures an accurate, data-driven approach to transport planning decisions.
Trellis AI uses sophisticated deep learning and neural network technology. It is significantly more accurate, faster and lower cost than conventional modelling approaches.
The Cognitive Mobility and Place System (C-MAPS) analyses and predicts walking and cycling at route (street) level. It can analyse and predict activity across several thousand links in a city.
It uses cutting-edge AI, including deep learning for street-by-street image recognition, to analyse the streetscape, urban form, population and employment growth and projects to understand and predict how people will use urban space in future.
The tool is the first of its kind and has provided exceptionally detailed insights on a range of client projects. It has been trained on Sydney, but can be trained and applied for any other city.
The tool provides robust evidence for how cycling, walking and placemaking patterns and volumes will change in future. This data can be used to inform urban design, housing, population, employment and transport changes as well as business cases and investment decisions. For more information, click here.
The Place and Walking System (PAWS) represents a groundbreaking approach to understanding and enhancing walkability in urban centers. It works at a whole of centre, as opposed to route level.
It combines 'big data' analysis with sophisticated machine learning and geospatial analytics. PAWS assesses over 100 variables that influence walking in urban centres to identify those that have most importance.
For a detailed description of the PAWS model and its findings, click here.
The Pedestrian Safety Assessment Tool (PedSAT) uses AI techniques to analyse locations to determine the level of risk of fatal and serious injury to pedestrians.
The AI tool compares real-world pedestrian accident data with many factors that determine pedestrian risk.
PedSAT provides a 5-star risk rating of points and links on road. The tool quantifies risk, suggests appropriate treatments, and provides an assessment of safety after treatment.
If you would like a demo of our tools, would like to sign up for our newsletter, or just would like to know more, please let us know below: