Aims and scope
The journal will publish original research papers applying big data techniques to transportation problems. The problems big data techniques are applied to can range from improvement in real-time transportation operations, transportation planning to near term prediction of crash risk. The paper may provide novel ideas about improved data ingestion, data curation, data archiving, data visualization, data security etc. for better transportation decision making. The data being used in the paper should at least satisfy on of the 3 V’s of the Gartner’s definition of big data i.e., high volume, high velocity or high variety. Also, knowledge discovery should use holistic approach instead of sampling and aggregation techniques to be considered as big data application. The techniques could involve CPU, RAM, GPU based high performance computing for batch analytics, stream processing, big data visualization, deep learning, or distributed computing such as block chain applications for enhanced security and smart contracts.