Multilayer Information Management System for personalized urban pedestrian routing

The present paper aims to describe the work carried out inside the ARGUS project to design and develop a software tool that manages heterogeneous cartographical datasets in order to offer personalized routing services. The project is focused on guiding blind and visually impaired in urban and rural environments with the help of binaural sounds. The navigation algorithm in the ARGUS smartphone application relies on GPX tracks containing the path to follow and informative points of interest along the path. These GPX files, previously recorded or created on demand, are downloaded from the remote service platform where the Multilayer Information Management System is hosted. This module handles, on one hand, crowdsourced data from OpenStreetMap and ARGUS users and, on the other hand, cartography from individual city providers. Moreover the system defines a set of spatial attributes to categorize the most relevant and signifcant types of urban elements for the target user group, which are represented as geographical point or lines, enabling users to decide which type of objects have a positive effect such as tactile pavements, negative or neutral effect during the trace of a path. This user specified aproach affect the route finding by changing the routing weights. Different levels of visual impairment and skills from one user to another, as well as personal preferences, make this module a decisive configurable abstraction layer for the route calculation module

Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells

Significant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power

CogITS: Cognition-enabled network management for 5G V2X Communication

The 5G promise for ubiquitous communications is expected to be a key enabler for transportation efficiency. However, the consequent increase of both data payload and number of users derived from new Intelligent Transport Systems makes network management even more challenging; an ideal network management will need to be capable of self-managing fast moving nodes that sit in the 5G data plane. Platooning applications, for instance, need a highly flexible and high efficient infrastructure for optimal road capacity. Network management solutions have, then, to accommodate more intelligence in its decision-making process due to the network complexity of ITS. This paper proposes this envisioned architecture namely Cognition-enabled network management for 5G V2X Communication (CogITS). It is empowered by machine learning to dynamically allocate resources in the network based on traffic prediction and adaptable physical layer settings. Preliminary proof-of-concept validation results, in a platooning scenario, show that the proposed architecture can improve the overall network latency over time with a minimum increase of control message traffic