WSP is using data and machine learning to optimise traffic flow across Auckland’s motorway network. Join us to hear how.Register now
Tue 17 May 9:00 AM - 9:45 AM
Did you know the number of lanes in each direction on the Auckland Harbour Bridge changes multiple times a day according to traffic demand? When demand changes, the Harbour Bridge monitoring dashboard advises when the barriers should be moved to keep traffic flowing.
Local councils and agencies gather some data on how our roads are being used to ensure our networks run smoothly and to plan for future infrastructure needs. To help build a more complete picture, WSP estimates traffic volumes using machine learning algorithms and GPS data.
Since the outbreak of COVID-19 in 2020, Auckland System Management (ASM) have been monitoring the change in network demand due to different COVID restrictions. Data across the motorway network is being collected and processed to understand the impact of COVID on our roads.
Join us to hear how WSP is keeping Auckland moving.
Team Leader - Digital Automation
Sam is Team Leader of the Transport Digital Automation team at WSP where he leads the development and implementation of digital tools to accelerate transport infrastructure design. Sam has a Master’s Degree and several years’ experience in Civil Engineering, with interests in programming, data analysis and machine learning.
Dan is a Transportation Engineer at WSP and the Journey Optimisation Manager at the Auckland Systems Management. He has a conjoint degree in Civil Engineering and Commerce, and is currently pursuing a Ph.D. in impact evaluation. At the ASM, Dan’s team is responsible for providing technical advice and assist in evidence-based decision making.
Rivindu is a data analyst at WSP and a Ph.D. candidate at the University of Auckland with experience in data analysis, python programming, innovation and start-ups. He holds a first-class honours degree in Electrical and Electronics Engineering from the University of Auckland. His research interests focus on Machine Learning, Deep Learning, Reinforcement Learning, Traffic Forecasting, and Intelligent Transportation Systems.