Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Extending TRANSIMS Technology to an Integrated Multilevel Representation

The TRANSIMS system developed at Los Alamos in the USA over the past decade is a world leader in providing an integrated land-use transportation dynamical model for large areas with a million or more inhabitants. TRANSIMS uses standard survey data to create synthetic micropopulations, including family structure, to simulate trip making and emergent traffic dynamics. We propose to extend TRANSIMS by adapting it to a new multi-level representation, allowing dynamics to be algebraically integrated at the micro-, meso- and macro-levels. The new representation builds a lattice hierarchy in a way that integrates non-partitional hierarchies of links and routes based on the usual hierarchy of geographical zones, e.g. neighbourhoods, districts, cities, counties and countries. Applying the representation to a big city starts by defining sets of zones at different levels. At the first level, N, is the street. This can be subdivided to building plots at level N-1, buildings at level N-2, and even rooms at level N-3. At level N+1 are the neighbourhoods, at level N+2 is the set of district zones (each of them containing the different neighbourhoods in the previous level), and at the top level N+3 (in this case), is just one zone, the city itself. If a larger study area is to be considered, we would have a whole set of N+3 zones defining N+4-level areas, and so on, extending to the level of counties, countries or even continents. This paper will explain the fundamentals of TRANSIMS technology and compare it to other systems. We will show how TRANSIMS and the new multi-level representation can be brought together to give new insights into the macro-dynamics of very large road systems such as London, England and even the whole of Europe

Travel Planning Ability in Right Brain-Damaged Patients: Two Case Reports

Planning ability is fundamental for goal-directed spatial navigation. Preliminary findings from patients and healthy individuals suggest that travel planning (TP)—namely, navigational planning—can be considered a distinct process from visuospatial planning (VP) ability. To shed light on this distinction, two right brain-damaged patients without hemineglect were compared with a control group on two tasks aimed at testing VP (i.e., Tower of London-16, ToL-16) and TP (i.e., Minefield Task, MFT). The former requires planning the moves to reach the right configuration of three colored beads on three pegs, whereas the latter was opportunely developed to assess TP in the navigational environment when obstacles are present. Specifically, the MFT requires participants to plan a route on a large carpet avoiding some hidden obstacles previously observed. Patient 1 showed lesions encompassing the temporoparietal region and the insula; she performed poorer than the control group on the ToL-16 but showed no deficit on the MFT. Conversely, Patient 2 showed lesions mainly located in the occipitoparietal network of spatial navigation; she performed worse than the control group on the MFT but not on the ToL-16. In both cases performances satisfied the criteria for a classical dissociation, meeting criteria for a double dissociation. These results support the idea that TP is a distinct ability and that it is dissociated from VP skills

Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Open source environment to define constraints in route planning for GIS-T

Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version

Decoding the urban grid: or why cities are neither trees nor perfect grids

In a previous paper (Figueiredo and Amorim, 2005), we introduced the continuity lines, a compressed description that encapsulates topological and geometrical properties of urban grids. In this paper, we applied this technique to a large database of maps that included cities of 22 countries. We explore how this representation encodes into networks universal features of urban grids and, at the same time, retrieves differences that reflect classes of cities. Then, we propose an emergent taxonomy for urban grids

Neural systems supporting navigation

Highlights: • Recent neuroimaging and electrophysiology studies have begun to shed light on the neural dynamics of navigation systems. • Computational models have advanced theories of how entorhinal grid cells and hippocampal place cells might serve navigation. • Hippocampus and entorhinal cortex provide complementary representations of routes and vectors for navigation. Much is known about how neural systems determine current spatial position and orientation in the environment. By contrast little is understood about how the brain represents future goal locations or computes the distance and direction to such goals. Recent electrophysiology, computational modelling and neuroimaging research have shed new light on how the spatial relationship to a goal may be determined and represented during navigation. This research suggests that the hippocampus may code the path to the goal while the entorhinal cortex represents the vector to the goal. It also reveals that the engagement of the hippocampus and entorhinal cortex varies across the different operational stages of navigation, such as during travel, route planning, and decision-making at waypoints

Design of a multiple bloom filter for distributed navigation routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes. © 2013 IEEE