Diverter Decision Aiding for In-Flight Diversions

It was determined that artificial intelligence technology can provide pilots with the help they need in making the complex decisions concerning en route changes in a flight plan. A diverter system should have the capability to take all of the available information and produce a recommendation to the pilot. Phase three illustrated that using Joshua to develop rules for an expert system and a Statice database provided additional flexibility by permitting the development of dynamic weighting of diversion relevant parameters. This increases the fidelity of the AI functions cited as useful in aiding the pilot to perform situational assessment, navigation rerouting, flight planning/replanning, and maneuver execution. Additionally, a prototype pilot-vehicle interface (PVI) was designed providing for the integration of both text and graphical based information. Advanced technologies were applied to PVI design, resulting in a hierarchical menu based architecture to increase the efficiency of information transfer while reducing expected workload. Additional efficiency was gained by integrating spatial and text displays into an integrated user interface

Stitching together the fabric of space and society: an investigation into the linkage of the local to regional continuum

To date, space syntax models have focused typically on relatively small areas up to the city scale. There have been very few models that take into account the entire network up to the regional scale, so the cumulative effects of micro-scale connections on regional networks is unknown, and the performance of the regional network as a function of the local area cannot be assessed. As such, a complete understanding of the ways in which regional centres are co-dependent and cities relate to their surrounding sub-centres is lacking. This study models the entire road network at the regional scale, by dispensing with axial lines entirely and moving to a road-centre line model of the UK, the Ordnance Survey's Integrated Transport Network (ITN) layer. This layer includes the topological connections between roads, so that a complete topological model of the road network including the directionality of streets can be constructed quickly. A region of the North of England - including Manchester, Bradford, Sheffield and Leeds - is analysed. Regional level angular analysis is shown to correlate well with overall movement in the network, while local level metric analysis is shown to correlate with the population density. It is hypothesised that combined measures that link the global to the local will uncover discontinuities in the continuum of space, and that these disruptions to the network will correspond to social deprivation. However, although such discontinuities exist, experimental linkage of the analysis to deprivation indices by census areas shows little conclusive evidence. In particular, it is clear that the complex web of spatial factors uncovered need investigation with more sensitive tools and smaller units of aggregation. The study highlights the need for a set of combined measures using microscopic spatial, economic, demographic, and land-use data, in order to further understand the relationship of spatial factors with social activity, while reinforcing standard space syntax results at the regional level

Learning Behavior Models for Interpreting and Predicting Traffic Situations

In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees

Graph-Theoretical Analysis of the Swiss Road and Railway Networks Over Time

Recent research of complex networks has significantly contributed to the understanding how networks can be classified according to its topological characteristics. However, transport networks attracted less attention although their importance to economy and daily life. In this work the development of the Swiss road and railway network during the years 1950-2000 is investigated. The main difference between many of the recently studied complex networks and transport networks is the spatial structure. Therefore, some of the well-established complex network measures may not be applied directly to characterise transport networks but need to be adapted to fulfil the requirements of spatial networks. Additionally, new approaches to cover basic network characteristics such as local network densities are applied. The focus of the interest hereby is always not only to classify the transport network but also to provide the basis for further applications such as vulnerability analysis or network development. It could be showed that the proposed measures are able to characterise the growth of the Swiss road network. To proof the use of local density measures to explain the robustness of a network however needs further researc

Spatial sustainability in cities: organic patterns and sustainable forms

Because the complexity of cities seems to defy description, planners and urban designers have always been forced to work with simplified concepts of the city. Drawn from natural language, these concepts emphasize clear hierarchies, regular geometries and the separation of parts from wholes, all seemingly at variance with the less orderly complexity of most real cities. Such concepts are now dominating the debate about sustainability in cities. Here it is argued that space syntax has now brought to light key underlying structures in the city, which have a direct bearing on sustainability in that they seem to show that the spatial form of the self-organised city, as a foreground network of linked centres at all scales set into a background network of mainly residential space, is already a reflection of the relations between environmental, economic and socio-cultural forces, that is between the three domains of sustainability. Evidence that this is so in all three domains is drawn from recent and new research, and a concept of spatial sustainability is proposed focused on the structure of the primary spatial structure of the city, the street network

REAL-TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research

Flight Planning in Free Route Airspaces

We consider the problem of finding cheapest flight routes through free route airspaces in a 2D setting. We subdivide the airspace into regions determined by a Voronoi subdivision around the points from a weather forecast. This gives rise to a regular grid of rectangular regions (quads) with every quad having an associated vector-weight that represents the wind magnitude and direction. Finding a cheapest path in this setting corresponds to finding a piece-wise linear path determined by points on the boundaries of the quads. In our solution approach, we discretize such boundaries by introducing border points and only consider segments connecting border points belonging to the same quad. While classic shortest path graph algorithms are available and applicable to the graphs originating from these border points, we design an algorithm that exploits the geometric structure of our scenario and show that this algorithm is more efficient in practice than classic graph-based algorithms. In particular, it scales better with the number of quads in the subdivision of the airspace, making it possible to find more accurate routes or to solve larger problems

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available