Traffic prediction system is one of the principal components of an intelligent traffic system (ITS). This system relies on data collected through vehicle-to-vehicle communication, probe vehicle monitoring, speed estimation, and vehicle counting based on vehicle tracking to predict the state of traffic. One of the most demanding tasks in traffic prediction is traffic congestion prediction. Upon predicted traffic congestion, traffic flow control and other intervention can be performed to prevent or at least reduce future congestion, which eases potentially disastrous effects of traffic congestion on the environment, society, and the economy. Traffic congestion prediction is a challenging task in several aspects. First, the prediction needs to be precise. Second, it needs to be made promptly so that any intervention can be meaningful. Third, the system needs the capacity to process a huge amount of data to provide the result for tens of thousands of locations in the map of a city simultaneously. This study proposes a traffic prediction system using Prophet and Spark Streaming. The entire system is built on Apache Spark, which is a Big data processing framework that can be scaled to process a huge amount of data. Spark Streaming is applied to process the streaming data and make real-time forecasting of the traffic flow. The Prophet model, which can capture long-range temporal sequences of data is used to predict traffic flow. The proposed system is shown to achieve good performance based on experimental results with the PEMS-BAY public transport dataset.
Real-Time Traffic Congestion Forecasting Using Prophet and Spark Streaming
Lecture Notes on Data Engineering and Communications Technologies
International Conference on Intelligence of Things ; 2022 ; Hanoi, Vietnam August 17, 2022 - August 19, 2022
2022-08-23
10 pages
Article/Chapter (Book)
Electronic Resource
English
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