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Showing 1 to 5 of 5 for “"traffic incident detection"”.
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A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics
Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the …
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Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors
… amount of heterogeneous urban data, such as traffic data, crime activity statistics, social media messages, and street imagery. The development of methods for heterogeneous urban data-based event identification and impacts analysis for a variety of event topics and assumptions is the subject …
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Hybrid state estimation applications for joint traffic monitoring and incident detection
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms
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A Framework for Incident Detection and notification in Vehicular Ad-Hoc Networks
… of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over $75 billion in lost worker productivity and over 8.4 billion gallons of fuel. Further, the …
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A framework for smart traffic management using heterogeneous data sources
Traffic congestion constitutes a social, economic and environmental issue to modern cities as it can negatively impact travel times, fuel consumption and carbon emissions. Traffic forecasting and incident detection systems are fundamental areas of Intelligent Transportation Systems (ITS) that have …