Abstract:
The main aim of intelligent transportation systems is the ability to accurately
predict traffic characteristics like traffic occupancy, speed, flow, and accident
based on historic and real-time data collected by these systems in
transportation networks. The main challenge of a huge quantity of traffic data
collected automatically, stored, and processed by these systems is the way of
handling and extracting the required traffic data to formulate the prediction
traffic characteristic model. In this research, the required traffic data of a
specified road link in the UK are extracted from the big raw data of the Split,
Cycle, and Offset Optimization Technique (SCOOT) system by designing a
C++ extractor program. In addition, short-term traffic prediction models are
created by using a deep learning technique called a Nonlinear Autoregressive
Exogenous (NARX) neural network to find accurate and exact traffic
occupancy. Three scenarios of time intervals which are 10 minutes, 20
minutes, and 30 minutes are considered for analyzing the prediction accuracy.
The results showed that the prediction models for the 30 minutes interval
scenario have very good accuracy in estimating the future traffic occupancy
compared to other scenarios of time intervals. In addition, the testing and
validation study showed that the prediction models for 30 minutes intervals for
particular road link yield better accuracy than 10 minutes and 20 minutes
intervals.
