The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far are based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density, and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Vehicle routing problems with drones equipped with multi-package payload compartments

The vehicle routing problem with drones (VRP-D) consists of designing combined truck-drone routes and schedules to serve a set of customers with specific requests and time constraints. In this paper, VRP-D is extended to include a fleet of drones equipped with multi-package payload compartments to serve more customers on a single trip. Moreover, a drone can return to a truck, different from the one from which it started, to swap its depleted battery and/or to pick up more packages. This problem, denoted as VRP-D equipped with multi-package payload compartments (VRP-D-MC), aims to maximize total profit. In this work, an adaptive multi-start simulated annealing (AMS-SA) metaheuristic algorithm is proposed to efficiently solve this problem. Experimental results show that the proposed algorithm outperforms the current state-of-the-art algorithms for VRP-D in terms of solution quality. Extensive analyses have been conducted to provide managerial insights. The analyses carried out show (i) the benefits of using drones equipped with different compartment configurations, (ii) the incremental total profit obtainable using a combined truck-drone fleet rather than a fleet of trucks, (iii) the benefit of swapping drone battery while picking up the items to deliver, and (iv) the impact of the packages load on the consumption energy of battery drone. It is also demonstrated that the different intensification and diversification mechanisms adopted improve the convergence of the traditional SA

Game-theoretical approach to decentralized multi-drone conflict resolution and emergent traffic flow operations

This paper introduces decentralized control concepts for drones using differential game theory. The approach optimizes the behavior of an ego drone, assuming the anticipated behavior of the opponent drones using a receding horizon approach. For each control instant, the scheme computes the Nash equilibrium control signal which is applied for the control period. This results in a multi-drone conflict resolution scheme that is applied to all drones considered. The paper discusses the approach and presents the numerical algorithm, showing several examples that illustrate the performance of the model. We examine at the behavior of the ego drone, and the resulting collective drone flow operations. The latter shows that while the approach aims to optimize the operation cost of the ego drone, the experiments provide evidence that resulting flow operations are very efficient due to the self-organization of various flow patterns. The presented work contributes to the state of the art in providing a generic approach to multi-drone conflict resolution with good macroscopic flow performance characteristics. The approach enables relatively straightforward inclusion of error due to sensing and communication. The approach also allows for including different risk levels (e.g., for malfunctioning of sensor and communication technology), priority rules, regulations, and higher-level control signals (e.g., routing, dynamic speed limits).Comment: Submitted to the TRB Annual Meeting 202