MobilitApp: Analysing mobility data of citizens in the metropolitan area of Barcelona

MobilitApp is a platform designed to provide smart mobility services in urban areas. It is designed to help citizens and transport authorities alike. Citizens will be able to access the MobilitApp mobile application and decide their optimal transportation strategy by visualising their usual routes, their carbon footprint, receiving tips, analytics and general mobility information, such as traffic and incident alerts. Transport authorities and service providers will be able to access information about the mobility pattern of citizens to o er their best services, improve costs and planning. The MobilitApp client runs on Android devices and records synchronously, while running in the background, periodic location updates from its users. The information obtained is processed and analysed to understand the mobility patterns of our users in the city of Barcelona, Spain

Transient analysis of idle time in VANETs using Markov-reward models

International audienceThe development of analytical models to analyze thebehavior of vehicular ad hoc networks (VANETs) is a challengingaim. Adaptive methods are suitable for many algorithms (e.g.choice of forwarding paths, dynamic resource allocation, channelcontrol congestion) and services (e.g. provision of multimediaservices, message dissemination). These adaptive algorithms helpthe network to maintain a desired performance level. However,this is a difficult goal to achieve, especially in VANETs due to fastposition changes of the VANET nodes. Adaptive decisions shouldbe taken according to the current conditions of the VANET.Therefore, evaluation of transient measures is required for thecharacterization of VANETs. In the literature, different worksaddress the characterization and measurement of the idle (orbusy) time to be used in different proposals to attain a moreefficient usage of wireless network. The present work focuseson the idle time of the link between two VANET nodes, whichwe denote as Tidle. Specifically, we have developed an analyticalmodel based on a straightforward Markov reward chain (MRC)to obtain transient measurements of Tidle. Numerical results fromthe analytical model fit well with simulation results

A Multimetric Predictive ANN-Based Routing Protocol for Vehicular Ad Hoc Networks

Vehicular networks support intelligent transportation system (ITS) to improve drivers’ safety and traffic efficiency on the road by exchanging traffic-related information between vehicles and also between vehicles and infrastructure. Routing protocols that are designed for vehicular networks should be flexible and able to adapt to the inherent dynamic network characteristics of these kind of networks. Therefore, there is a need to have effective vehicular communications, not only to make mobility more efficient but also to reduce collateral issues such as pollution and health problems. Nowadays, the use of machine learning (ML) algorithms in wireless networks are on the rise, including vehicle networks that can benefit from possible data-driven predictions. This work aims to contribute to the design of a smart ML-based routing protocol for vehicular ad hoc networks (VANETs) used to report traffic-related messages in urban environments. We propose a new ML-based forwarding algorithm to be used by the current vehicle holding a given packet to predict which vehicle within its transmission range is the best next-hop to forward that packet towards its destination. Our algorithm is based on a neural network designed from a dataset that contains data records that are captured during simulated urban scenarios. Simulation results show how our ML-based proposal improves the performance of our multimetric routing protocol for VANETs in urban scenarios in terms of packet delivery probability. The performance evaluation of MPANN shows packet losses lower than 20% (and average packet delays below 0.04 ms) for different vehicles’ densities, in completely new scenarios but of similar complexity than the Barcelona scenario used to train the model. Even for much more complex scenarios (with narrow curvy streets), our proposal is able to reduce the packet losses in 20% with respect to the multimetric routing protocol as well as the average packet delays in 0.04 ms