Abstract
Big data have been used widely in many areas, including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted to improve the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized, and off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.
Highlights
Based on a regression model, a cell probe (CP)-based method is proposed to estimate the vehicle speed with the normal location update (NLU) procedure and the consecutive handoff (HO) event as inputs, and the proposed method achieved a 97.63% accuracy [28]
While taxi GPS trajectory datasets are widely used in the literature, there is a concern that taxi drivers are not the best representatives since they are experienced drivers with a higher possibility of finding optimal routes
This study focused on the tasks of traffic estimation and prediction and presents an up-to-date collection of the available datasets and tools as a reference for those who seek public resources
Summary
The first definition of big data is “Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. Traffic estimation and prediction are the two most significant tasks, which are the focus of this study. Significant progress has been achieved in previous studies for traffic estimation and prediction with the appearance of big data. Some of the relevant studies are based on private data, whose results are impossible to replicate In this survey, we focus on open datasets, especially large-volume and multi-modal datasets. We point out the challenges and future directions of utilizing big data for traffic estimation and prediction tasks.
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