This requirement pushed PeakHour to tap the Amazon Web Services cloud platform, which provides both compute capability to scale the AI algorithm and massive amounts of data storage to hold a data set that is growing by many gigabytes per day. To be immediately useful, he said, speed is of the essence: “It has to be fast in order to be relevant,” Ejtemai said.
“If we cannot measure traffic, it’s very difficult to optimise it.” “In order to optimise and manage traffic, we need to be able to predict it,” Ejtemai explained. Such modelling will become invaluable in future ‘smart cities’ where traffic flows will be managed by increasingly automated infrastructure – but it is already useful as a way of, for example, predicting changes in traffic flow if a particular road is closed for repaving. The AI engine has been applied to data from over 10,000 monitoring points across the Melbourne metropolitan area, which is collected by VicDoT’s Sydney Coordinated Adaptive Traffic System (SCATS) road-monitoring system and gives PeakHour updated location-based data about traffic volume every 5 minutes.Ĭombined with regularly updated point-in-time speed data from over 20,000 collection points provided by geographical systems vendor TomTom – which has been tracking worldwide changes in traffic flow during the pandemic – PeakHour’s AI has been able to reliably predict how traffic will look anywhere in Melbourne, up to three hours in advance. “We bring them together and innovate, and try to do something that has not been done before, but is based on deep knowledge in these areas.” “I believe the key is to have a multi-disciplinary approach and have people who are experts in different domains,” PeakHour founding CEO Omid Ejtemai told Information Age.
PeakHour Urban Technologies, a startup that was founded last year to apply AI to complex traffic modelling, joined the Victorian Department of Transport (VicDoT) and Telstra in a collaborative project co-ordinated by the University of Melbourne’s Australian Integrated Multimodal EcoSystem (AIMES).īy assembling a highly technical team of less than 10 people – including traffic engineering experts, software developers and software engineers – the company has developed bespoke AI algorithms and is tuning them against massive volumes of traffic data, which is collected in real time from a variety of sources. Web surveys showed dramatic changes in public perception of the Rapid Rehab approach from initial reluctance and objection to positive support.Victoria’s extended ‘circuit breaker’ lockdown may be testing the patience of the state’s residents, but its major changes in traffic flow have been a valuable source of new data for an AI algorithm that can accurately predict local traffic flows three hours into the future. The estimated benefits of accelerated reconstruction on this project included a 28% reduction in agency cost and 29% time value savings to road users, compared to the traditional approach of using repeated nighttime closures.
As a result, traffic demand through the construction work zone was reduced by 20% and the maximum peak-hour delay was reduced by 50%. This “Rapid Rehab” project adopted state-of-practice technologies to accelerate construction, mitigate traffic disruptions, and propagate project information. The operations, estimated to take 10 months using traditional nighttime closures, were completed in two 9 - day continuous closures with round-the-clock (about 210 h for each direction) operations. Badly deteriorated truck lanes in both directions along a 4.5 - km stretch of I-15 were rebuilt from the gravel base up. This case study paper presents an innovative fast-track approach applied to a heavily trafficked urban freeway reconstruction project in Southern California.