VicRoads

About: VicRoads is a government organization based out in Kew, Victoria, Australia. It is known for research contribution in the topics: Poison control & Psychomotor vigilance task. The organization has 67 authors who have published 86 publications receiving 2309 citations. The organization is also known as: Roads Corporation of Victoria.

Effects of recycled concrete aggregates on properties of asphalt concrete

Abstract: Recycled concrete aggregates (RCA) are produced by crushing demolished concrete elements. RCA differ from fresh aggregates due to the cement paste attached to the surface of the original natural aggregates after the process of recycling. This highly porous cement paste and other contaminations contribute to the lower particle density and higher porosity, variation in the quality of the RCA and the higher water absorption. This paper presents some of the results of an investigation on the possible application of RCA in asphalt concrete. It was found that all the volumetric properties (except the percentage of air voids), resilient modulus and creep values of asphalt specimens containing RCA as coarse aggregates were relatively lower compared with the values found for similar specimens made with only fresh aggregates. Some of these measured properties were within the acceptable recommended limits, for the RCA sample used in this study.

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Abstract: The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.