Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

Big Data is data that grows beyond the capability of traditional data mining methods. A big voluminous, heterogeneous, and speediness of data generated from numerous sensors attached near roadside infrastructure and devices associated with the vehicles. We need to design Big data models for highly scalable, techniques for complex data types, robust to noise, insensitive to the input order, ability to handling high-dimensional data, interpretability, and usability. There are many challenges in enormous amount of data to stockpile, processing, and operate. Apache Spark, Hadoop, and Flink are the platforms of Big Data Analytics. Massive information processing in transportation systems facing difficulties in terms of data privacy, security, data storage, and data quality. Various Big Transportation data analytics are performed, such as video and audio analytics, image analytics, and historical analytics. The globe is monitored and managed with Web of Devices (WoD) efficiently and effectively.