# Prediction Model of Traffic Flow Driven Based on Single Data in Smart Traffic Systems

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## Abstract

The traffic flow series is a typical time series with some time-varying rules. Because the living and working hours of the house have certain rules, the traffic flow series has a strong periodic variation law. Deterministic predictions can more accurately find the change values of traffic flows, while interval predictions can find the range of changes in traffic flows. Both prediction methods play an important role in improving intelligent transportation systems. In this chapter, traffic flow prediction models are built using historical data from the traffic flow series. The models are divided into the deterministic prediction model and interval prediction model, which are composed of BP prediction model, WD-BP prediction model, BP-GARCH interval prediction model, and WD-BP-GARCH interval prediction model. It can be seen from the experimental results that the predictive performance of the WD-BP predictive model is higher than the BP predictive model in the deterministic forecast of traffic flow. In the interval prediction of traffic flow, BP neural network is used to establish a deterministic prediction model, and the GARCH model is used to calculate the uncertainty of forecasting traffic flow.

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