Incident detection using data from social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) on 15/03/2018, available online: https://ieeexplore.ieee.org/document/8317967/citations#citations The accepted version of the publication may differ from the final published version.© 2017 IEEE. Due to the rapid growth of population in the last 20 years, an increased number of instances of heavy recurrent traffic congestion has been observed in cities around the world. This rise in traffic has led to greater numbers of traffic incidents and subsequent growth of non-recurrent congestion. Existing incident detection techniques are limited to the use of sensors in the transportation network. In this paper, we analyze the potential of Twitter for supporting real-time incident detection in the United Kingdom (UK). We present a methodology for retrieving, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Our approach can detect traffic related tweets with an accuracy of 88.27%.Published versio

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Effect of ancilla's structure on quantum error correction using the 7-qubit Calderbank-Shor-Steane code

In this work we discuss the ability of different types of ancillas to control the decoherence of a qubit interacting with an environment. The error is introduced into the numerical simulation via a depolarizing isotropic channel. After the correction we calculate the fidelity as a quality criterion for the qubit recovered. We observe that a recovery method with a three-qubit ancilla provides reasonable good results bearing in mind its economy. If we want to go further, we have to use fault-tolerant ancillas with a high degree of parallelism, even if this condition implies introducing new ancilla verification qubits.Comment: 24 pages, 10 Figures included. Accepted in Phys. Rev. A 200

A hypercyclic finite rank perturbation of a unitary operator

A unitary operator $V$ and a rank $2$ operator $R$ acting on a Hilbert space \H are constructed such that $V+R$ is hypercyclic. This answers affirmatively a question of Salas whether a finite rank perturbation of a hyponormal operator can be supercyclic.Comment: published in Mathematische Annale