Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

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

Visualization of flow fields in the web platform

Visualization of vector fields plays an important role in research activities nowadays -- Web applications allow a fast, multi-platform and multi-device access to data, which results in the need of optimized applications to be implemented in both high-performance and low-performance devices -- Point trajectory calculation procedures usually perform repeated calculations due to the fact that several points might lie over the same trajectory -- This paper presents a new methodology to calculate point trajectories over highly-dense and uniformly-distributed grid of points in which the trajectories are forced to lie over the points in the grid -- Its advantages rely on a highly parallel computing architecture implementation and in the reduction of the computational effort to calculate the stream paths since unnecessary calculations are avoided, reusing data through iterations -- As case study, the visualization of oceanic currents through in the web platform is presented and analyzed, using WebGL as the parallel computing architecture and the rendering Application Programming Interfac

Hardware-accelerated Web Visualization of Vector Fields. Case Study in Oceanic Currents

Visualization of vector fields plays an important role in research activities nowadays -- Increasing web applications allow a fast, multi-platform and multi-device access to data -- As a result, web applications must be optimized in order to be performed heterogeneously as well as on high-performance as on low capacity devices -- This paper presents a hardware-accelerated scheme for integration-based flow visualization techniques, based on a hierarchical integration procedure which reduces the computational effort of the algorithm from linear to logarithmic, compared to serial integration methodologies -- The contribution relies on the fact that the optimization is only implemented using the graphics application programming interface (API), instead of requiring additional APIs or plug-ins -- This is achieved by using images as data storing elements instead of graphical information matrices -- A case study in oceanic currents is implemente

Visualisation of flow fields in the web platform

Visualization of vector fields plays an important role in research activities nowadays. Web applications allow a fast, multi-platform and multi-device access to data, which results in the need of optimized applications to be implemented in both high and low-performance devices. The computation of trajectories usually repeats calculations due to the fact that several points might lie over the same trajectory. This paper presents a new methodology to calculate point trajectories over a highly-dense and uniformly-distributed grid of points in which the trajectories are forced to lie over the points in the grid. Its advantages rely on a highly parallel computing implementation and in the reduction of the computational effort to calculate the stream paths since unnecessary calculations are avoided by reusing data through iterations. As case study, the visualization of oceanic streams in the web platform is presented and analyzed, using WebGL as the parallel computing architecture and the rendering engine

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

Trajectory Clustering for the Classification of Eye-Tracking Users With Motor Disorders

[Abstract] This paper presents a pilot study completed in the framework of the INTERAAC project. The aim of the project is to develop a new human-computer interaction (HCI) solution based on eye-gaze estimation from webcam images for people with motor disorders such as cerebral palsy, neurodegenerative diseases, and spinal cord injury that are otherwise unable to use a keyboard or mouse. In this study, we analyzed cursor trajectories recorded during the experiment and validated that users with different diseases can be automatically classi ed in groups based on trajectory metrics. For the clustering, Ward's method was used. The metrics are based on speed and acceleration statistics from full fi ltered tracks. The results show that the participants can be grouped into two main clusters. The main contribution of this work is the evaluation of the clustering techniques applied to eye-gaze trajecto- ries for the automatic classi cation of users diseases based on a real experiment carried with the help of three clinical partners in Spain.This work has been funded by the Spanish Ministry of Economy and Competitiveness, under the call Retos-Colaboración 2015 of the the National Programme for Research Aimed at the Challenges of Society 2009-2016 (RTC-2015-4327-1)https://doi.org/10.17979/spudc.978849749808

Concerns on Design and Performance of a Local and Global Dynamic Map

Current real-time data collection systems for urban transportation and mobility allow enhancing digital maps with up-to-date situational information. This information is of great interest for short-term navigation and route planning as well as for medium- to long-term mobility data analysis, as it provides a finer time-varying detail of the urban movement infrastructure. In this work, we present our ongoing work to design a representation of a unique urban movement space graph as a local and global dynamic map approach. We address the concerns that must be considered when handling different scales of geographic areas inside a city, according to the application