*3.2.3. Traffic only*

One of the main objectives of a machine-learning approach is to produce the most accurate prediction with a model as simple as possible. Since the temporal features seem to have a lower weight than the traffic features, we propose to build a model based on traffic only and assessing its reliability. Here, the number of attributes is three: %orange, %red, and PM2.5.

The linear regression model obtained after running the algorithm is as follows:


The prediction accuracy of the model is evaluated as

$$\mathbf{R} \qquad \mathbf{=} \mathbf{0.31}$$

$$\mathbf{R} \mathbf{M} \mathbf{S} \mathbf{E} = \mathbf{8.51}$$

Again, the model shows that the weight of the %orange parameter is the largest. The higher is the medium amount of traffic, the higher is the level of PM2.5. In terms of performance, this model based on two predictive features has an accuracy similar as the previous model with four features (r ≈ 0.3 in both cases).
