Social Media Data Analysis for Intelligent Transportation Systems

Transportation is the major tribulations in urban cities. Traditional transportation approach are not smart for traffic management so Intelligent transport systems works with automated system and gives better solutions for smart parking, electronic toll collection, traffic monitoring and planning etc by accessing data from different sources. Among passive data, Social media data is widely sourced for Intelligent Transportation Systems (ITS), users communicate and interact online by writing blogs through face book, twitter and other social media applications. This data from social media can be used for transportation analysis like traffic prediction, accident zones analysis etc. unfortunately the data sourced is highly unstructured and massive in volume leading to complex processing for data usage. Big data helps in processing, storing and analyzing this data. In this paper we summarize main research on review topics in the field of data preprocessing techniques for noisy data removal, data concentration methods for filtering the data, feature selection methods to extract the main features, abnormal behavior of data to identify the suspicious behavior accounts and road condition prediction for detecting the road patterns and traffic behavior by supervised learning and unsupervised machine learning algorithms, this paper discusses towards the end on future research directions in social media analysis in ITS.