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title: Computational Intelligence for Traffic and Mobility
Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness
Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals
In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of
...Intelligent Traffic System for Sustainable Mobility
Artificial Intelligence Traffic Analysis Framework for Smart Cities
Efficient and fair traffic flow management for on-demand air mobility
A method of controlling an artificial intelligence (AI) mobility device can include acquiring personal information of a driver and
...Dynamic Traffic Flow to Promote Sustainable Mobility
Dynamic traffic flow is one of the main challenges for a state or country after a city is created. Since the
...Internet of Vehicles – System of Systems Distributed Intelligence for Mobility Applications
Urban Traffic Monitoring from Mobility Data
Artificial Intelligence and Forecasting of Traffic Accidents Using GIS
AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility
Systems and methods for energy efficient mobility using machine learning and artificial intelligence
Systems and methods for energy efficient mobility are provided. The method may include identifying a vehicle moving on a road
...SYSTEMS AND METHODS FOR ENERGY EFFICIENT MOBILITY USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Systems and methods for energy efficient mobility are provided. The method may include identifying a vehicle moving on a road
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Application of Artificial Intelligence Approach for Optimizing Management of Road Traffic
V2I-Based Intelligent Mobility Data Fusion for Self-Adaptive Traffic Signal Control
Applications of Multi-Source Traffic Data on Mobility Analysis for Urban Road Network
Mobility analysis for road network is a critical foundation for urban traffic operation and management. Multi
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A Multi-Criteria Clustering Method for UAS Traffic Management and Urban Air Mobility
Assessing the Suitability of Different Machine Learning Approaches for Smart Traffic Mobility