Citation

Dabiri S, Heaslip K. Expert Syst. Appl. 2019; 118: 425-439.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.eswa.2018.10.017

PMID

unavailable

Abstract

In recent years, several studies have harnessed Twitter data for detecting traffic incidents and monitoring traffic conditions. Researchers have utilized the bag-of-words representation for converting tweets into numerical feature vectors. However, the bag-of-words not only ignores the order of tweet's words but suffers from the curse of dimensionality and sparsity. A common approach in literature for dimensionality reduction is to build the bag-of-words on the top of pre-defined traffic keywords. The immediate criticisms to such a strategy are that the pre-defined set of keywords may not include all traffic keywords and the tweet language is subjected to change over time. To address these shortcomings, we utilize the power of deep-learning architectures for both representing tweets in numerical vectors and classifying them into three categories: 1) non-traffic, 2) traffic incident, and 3) traffic information and condition. First, we map tweets into low-dimensional vector space through word-embedding tools, which are also capable of measuring the semantic relationship between words. Supervised deep-learning algorithms including convolutional neural network (CNN) and recurrent neural network (RNN) are then deployed on the top of word-embedding models for detecting traffic events. For training and testing our proposed model, a large volume of traffic tweets is collected through Twitter API endpoints and labeled through an efficient strategy. Experimental results on our labeled dataset show that the proposed approach achieves clear improvements over state-of-the-art methods.

Language: en

Keywords

Deep learning; Recurrent and Convolutional Neural Networks; Traffic information systems; Twitter