Transportation
Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply — demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique.
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This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.
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<p>This is <span style="font-family: Verdana, Arial, Helvetica, sans-serif;">Charlotte street netowrk data.</span></p>
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This dataset contains road network information of Chengdu with travel time data during four time slots: weekday peak hour, weekday off-peak hour, weekend peak hour and weekend off-peak hour.
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This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.
The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.
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During the five-month surveying period, 459 individuals are recruited either on site or via Internet, more than 17 million GPS tracking points over 3,766 days are obtained. A total of 318 respondents’ socio-economic attributes, demographic information, and frequently visited locations are also collected via web-based survey. Among them, 267 volunteers completed more than 5 days of continuous smartphone-based GPS tracking, and participated in the PR survey to provide activity chain information corresponding to the GPS data.
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