As one of the important tools for spatial feature extraction, graph convolution has been applied in a wide range of fields such as traffic flow prediction. However, current popular works of graph convolution cannot guarantee spatio-temporal consistency in a long period. The ignorance of correlational dynamics, convolutional locality and temporal comprehensiveness would limit predictive accuracy. In this paper, a novel Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve traffic flow prediction in highway transportation. Three temporal resolutions of data sequence are effectively integrated by self-attention to extract characteristics; multi-dynamic graphs and their weights are dynamically created to compliantly combine the varying characteristics; a dedicated gated kernel emphasizing highly relative nodes is introduced on these complete graphs to reduce overfitting for graph convolution operations. Experiments on two public datasets show our work better than state-of-the-art baselines, and case studies of a real Web system prove practical benefit in highway transportation.

According to the FWHA, there is a need for early-stage research to support emerging advances in artificial intelligence to help solve complex issues in highway transportation. A new Federal Highway Administration (FHWA) program aims to transform highways with artificial intelligence. Some of the areas where the technology is expected to play a future role include improved safety, environmental mapping, bridge capacity insights, and smart parking. The Exploratory Advanced Research (EAR) Program is currently soliciting proposals up until December 5. It will then award contracts or engage in cooperative agreements.

As part of Department of Transportation's plans to modernize the U.S.'s highway infrastructure, the agency issued a new contract opportunity seeking artificial intelligence and data analytics-based solutions. A program under the agency's Federal Highway Administration called for proposals featuring artificial intelligence technology to improve the current national highway design system. The technology developed within this contract will impact the planning, construction and maintenance of the nation's highways. A spokesperson for the FHWA informed Nextgov the projects' goal is to successfully transform highway transportation with AI technology, and that, following the projects' individual outcomes, officials will decide how to implement it across the transportation sector. "With the growing number of maturing and commercial applications, there still is a need for early state research to support emerging advances in AI that can solve even more complex questions in highway transportation," the RFI notes.

The Federal Highway Administration launched an Exploratory Advanced Research Program this week to usher in "transformational changes and truly revolutionary advances" in highway engineering and intermodal transportation on roads across the United States. According to a new broad agency announcement, the administration is accepting research effort proposals--with the deliberate intent of awarding either contracts or cooperative agreements--that address three trendy topics in emerging tech: blockchain for highway transportation, artificial intelligence for highway transportation, and incorporating trashed plastic into asphalt cement to reduce waste. "This program supports scientific investigations and studies that advance the current knowledge and state-of-the-art in the sciences and technologies employed in the planning, design, construction, operation, maintenance and management of the nation's highways," officials wrote in the announcement. "Strategically, this research will enable and expedite the development of revolutionary approaches, methodologies, and breakthroughs required to drive innovation and greatly improve the efficiency of highway transportation." The agency's EAR programs aim to produce strong public-private partnerships that catalyze solutions through "longer-term, higher risk" research.