The behavior of any traffic flow is sensitive to the speed pattern of the vehicles involved. The heavier the traffic, the more sensitive the behavior is to speed changes. Focusing on air traffic flow, weather condition has a major role in the deviations of aircraft operational speed from the desired speed and causes surplus delays. In this paper, the effects of wind on delays in a terminal area are analyzed using a Cellular Automaton (CA) model. Cellular automata are discrete models that are widely used for simulating complex emerging properties of dynamic systems. A one-dimensional cellular array is used to model the flow of the terminal traffic into a wind field. The proposed model, due to the quickness and acceptable level of accuracy, can be utilized online in the tactical phase of air traffic control processes and system-level decision-makings, where quick response and system behavior are needed. The modeled route is an RNAV STAR route to Atlanta International Airport. The model is verified by real traffic data in a non-delayed scenario. Based on simulation results, the proposed model exhibits an acceptable level of accuracy (3–15% accuracy drop), with worthy time and computational efficiency (about 2.9 seconds run time for a 2-hour operation).

We study 4-objective dynamic route planning based on real road network and real traffic data from Aarhus, Denmark. Our 4 objectives include total vehicular emission cost (TEC), travel time, number of turns, and distance. The first two objectives are time-dependent and traffic-dependent. The third objective is a novel objective for simpler routing and better user experience. The fourth objective is standard for all route planning problems. We provide a thorough explanation of how to design NSGA-II to solve the 4-objective dynamic route planning problem. Our experiments produce a diverse set of solutions for this problem and provide user the flexibility of selecting a path based on their preferences of the four objectives. Our work provides a good framework for many-objective dynamic route planning and can be extended to other techniques such as manyobjective Particle Swarm Optimzation (PSO) and many-objective Ant Colony Optimization (ACO).

Arctic remote sensing is crucial for studying and preserving the vulnerable Arctic environment. Researchers use ground penetrating radars (GPRs) to understand climate change and ice properties, but maneuvering a bulky GPR on the inhospitable Arctic terrain is difficult. Suspending the GPR from a drone can be a solution to operating a large GPR, however, proper control and stabilization of the drone-GPR system pose a challenge. In this paper, we contribute to the literature by analyzing the response of closed-loop feedback fractional-order and integer-order proportional-integral-derivative (PID) controllers at minimizing the payload sway for a drone-based cable-suspended payload system. Both controllers were tuned using particle swarm optimization and simulated to experience delays and external disturbances. Results indicate that both controllers had comparable time responses. The fractional-order controller was more robust to disturbances but was also sensitive to system delays. The findings of this study can be considered for future drone development.