This research paper uses agent-based modeling to estimate the potential demand for an autonomous mobility-on-demand (AMoD) system tailored to the needs of older adults in Winnipeg, Canada, highlighting the importance of considering demographic factors and spatial heterogeneity in transportation planning.
Utilizing street-view video sequences for constructing time-space diagrams offers valuable insights into traffic patterns and transportation infrastructure design.
Deep learning with CNN-LSTM architecture enhances traffic flow prediction using cellular automata-based models.
Estimating traffic demands and paths in road networks using Dynamic Programming.
Designing an incentive-compatible vertiport reservation mechanism for efficient coordination in advanced air mobility.
Optical flow technique improves moving object detection and tracking for autonomous vehicles.
Optimizing car parking allocation on university campuses.
Proposing a new approach using genetic programming for efficient and explainable traffic signal control.
Reinforcement learning improves safety, efficiency, and stability in mixed traffic.
Efficient traffic management is crucial for the operation of on-demand urban air mobility systems, maximizing throughput and reducing passenger waiting times.