# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 4 Spatiotemporal Traffic Prediction Models
### The Artificial Neural Network Approach  
In the 1990s, artificial neural networks (ANNs) became a popular approach for binary and numerical data prediction. ANNs are data-driven machine learning algorithms that work similarly to smoothing algorithms in terms of learning the patterns from the data. It also works similarly to regression algorithms, in that, they are designed to capture a relationship between the input and output using cross-sectional data. In the literature, the ANN approach has mixed results regarding the performance of neural networks compared with other prediction methods, where neural networks work best with high-frequency data [36]. As we can see from Figure 3, a basic ANN model has an input layer and an output layer [37]. All the layers in between the input and output layers are denoted as hidden layers. The neurons between different layers are connected via an edge associated with a certain weight. The ANN computes the values of these neurons in association with their weights and forwards the values to an activation function. An activation function maps the aggregated values from the input layer to the output layer.  
One of the earliest studies on traffic modeling using ANNs was proposed by [PERSON] in 1997 [38], where she designed a traffic modeling system based on ANNs. The system has the capability to simulate the traffic flow for connected junctions and then model the traffic flow over a wide range of intersections. The study confirms the potential of using ANNs in traffic prediction modeling and recommends further investigation.  
A predictive model based on the ANN approach was introduced by [PERSON] et al. [39] to predict traffic accidents and improve traffic safety. They discussed integrating backpropagation neural networks with genetic algorithms to identify potential jamming spots that were likely to cause traffic accidents. The model analyzes the traffic conditions and then produces samples of the possible road accident spots. Additionally, they applied the model on real-time data to predict traffic accident spots. Their conclusion was that integrating ANNs and genetic algorithms as a hybrid genetic algorithm backpropagation (GA-BP) model helped in optimizing the network. The computational overhead of this process produces the local minimum problem, which means that the ANN will continue training the data and updating the network's weights until it reaches the lowest point of the error function. The model has the ability to classify the static factors and the dynamic factors within the road traffic conditions to achieve a high prediction accuracy.  
[PERSON] et al. [40] proposed a study to model historical traffic data using the ANN approach. The study focused on predicting the traffic flow at each main intersection in the city of Istanbul. The model predicted the traffic based on a specific scenario, such as during holidays and school hours. One of the model's features is informing the drivers of the traffic status for the next hour. This study proposed testing the feasibility of applying the ANN approach in the traffic modeling domain. The findings of this study show that  
Figure 3: The basic components of an ANN.  
ANNs successfully provide accurate predictions in different scenarios. However, the lack of long-term data might enhance the results, as was recommended by the authors.