An Approach for Detecting Traffic Events Using Social Media

Abstract

Nowadays almost everyone has access to mobile devices that offer better processing capabilities and access to new information and services, the Web is undoubtedly the best tool for sharing content, especially through social networks. Web content enhanced by mobile capabilities, enable the gathering and aggregation of information that can be useful for our everyday lives as, for example, in urban mobility where personalized real-time traffic information, can heavily influence users' travel habits, thus contributing for a better way of living. Current navigation systems fall short in several ways in order to satisfy the need to process and reason upon such volumes of data, namely, to accurately provide information about urban traffic in real-time and the possibility to personalize the information presented to users. The work presented here describes an approach to integrate, fuse and process tweet messages from traffic agencies, with the objective of detecting the geographical span of traffic events, such as accidents or road works. Tweet messages are considered in this work given their uniqueness, their real time nature, which may be used to quickly detect a traffic event, and their simplicity. We also address some imprecisions ranging from lack of geographical information, imprecise and ambiguous toponyms, overlaps and repetitions as well as visualization to our data set in the UK, and a qualitative study on the use of the approach using tweets in other languages, such as Greek. Finally, we present an application scenario, where traffic information is processed from tweets massages, triggering personalized notifications to users through Google Cloud Messaging on Android smartphones. The work presented here is still part of on-going work. Results achieved so far do not address the final conclusions but form the basis for the formalization of a domain knowledge along with the urban mobility services.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

“…Firstly, several works use OSNs as real-time data streams to detect certain events or incidents with respect the traffic of a city [ 46 , 60 , 61 , 62 ]. For this task, different classification algorithms, like Support Vector Machines or Random Forest, along with Natural Language Processing (NLP) techniques are combined.…”

Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques

“…The last group is oriented to the amount of data collected by all the technology currently available in the community. Among them can be found data from social networks [12,17,50,51], data collected from sensors of a smartphone [13], structured data [52,53], data detected from video cameras [14,54,55], and traffic sensor data [56]. These datasets are widely used by deep learning, hybrid, and extreme learning methods.…”