Abstract
Traffic flow prediction is essential for traffic management, pollution reduction, and public safety, and is an important component of Intelligent Transportation Systems (ITS). Since most of the links are not outfitted with traffic sensors, extracting traffic flow data in real-time scenarios has proven to be challenging. Moreover, the parameters that affect traffic flows (such as accidents, road closures, and public events) are mostly unexpected, which makes traffic flow prediction a complex task. Hence, this paper plans to design an enhanced prediction model for traffic flow using a Modified Hidden Markov Model (MHMM). The input features subjected to prediction via MHMM are “Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Indicator (RSI), and Rate of Change (ROC)” respectively. In fact, the modification in HMM relies on the optimal tuning of state numbers using the Mean Fitness-oriented Dragonfly Algorithm (MF-DA). Finally, the betterment of implemented work is compared and proved over the conventional models.
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