Victorian Minister for Transport Ben Carroll who attended this morning’s launch said managing a complex transport network presents many real-time challenges. The Victorian Department of Transport provided traffic data and insight to support the creation of the application. We are using a multidisciplinary approach, combining deep knowledge of mobility with vast amounts of real-time data analytics to predict and optimise traffic in large cities,” PeakHour Urban Technologies Founding CEO Omid Ejtemai said. “Pioneering AI in forecasting real-time traffic lies at the heart of this effort.

Industry partner PeakHour Urban Technologies developed the application’s AI core engine which runs on AWS and powers the engine’s predictive capabilities.ĪWS provides PeakHour Urban Technologies the scalability to ingest, store, and process large amounts of traffic data, the ability to adapt to an ever-changing transport network, and the breadth and depth of cloud services to support PeakHour Urban Technologies with its AI solutions. “If we can upscale the application to provide more accurate prediction with machine learning and real-time data, it will soon be possible to substantially reduce delays in hotspots across Melbourne and many locations across the globe.” “The application observes the nature of traffic and figures out complex traffic patterns across the network through machine learning built into the technology,” Professor Sarvi said. Transport engineering expert and AIMES Director Professor Majid Sarvi said the application can also optimise traffic signals for on-road vehicles, freight, and public transport such as buses and trams. University of Melbourne’s Australian Integrated Multimodal EcoSystem (AIMES) brought together PeakHour Urban Technologies, the Victorian Department of Transport, and Telstra to create a large-scale AI application hosted on Amazon Web Services (AWS), which can predict traffic conditions across Melbourne.

The effectiveness of the proposed model and algorithm are evaluated using a real-world data set.Launched today, a world first project seeks to use artificial intelligence (AI) to predict traffic congestion up to three hours ahead, optimising traffic in large cities and improving road safety as part of the University’s smart cities ecosystem. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. Using cumulative input–output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor.

Peakhour urban technologies