Is linear regression fit for this data I am predicting number of vehicles in 4 traffic junctions.

So, I have following columns in my dataset :


DateTime
Junction_ID
Number_of_vehicles


At the first glance , this problem may look like Time series regression. But, the data given seems like Linear Regression problem.

So, I have applied linear regression in the following manner :


Used get_dummies extensively for all the columns. I used dummy variables for 31 days,24 hours ,7 days of weeks and 4 Junction Ids.
Then applied Linear Regression model 

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(train_data,train_vehicles)

clf.fit(x_train,y_train)

import math

pred=clf.predict(x_test)

pred.shape #got result as (12030,)

result = []
for x in pred:
result.append(math.ceil(x))

from sklearn.metrics import mean_squared_error

score=mean_squared_error(y_test, result)
rmse=math.sqrt(score)
print('RMSE is :', rmse)



I am getting RMSE value as 10.636853077462394

My questions are :


Since RMSE value is on lower side , can I say this model is decent ?
Is there any other approach which I can use on this dataset ?
Do I need to check for colinearity ?
How can I check if multiple variables are interrelated ?
Should I go for non-linear regression on this dataset ?
For the first question, it is important to recall that RMSE has the same unit as the dependent variable. It means that there is no absolute good or bad threshold, however you can define it based on your DV. For a datum which ranges from 0 to 1000, an RMSE of 0.7 is small, but if the range goes from 0 to 1, it is not small.

I would do some feature engineering (Create more variables: Time of Day, Day of Week, Month etc...) and run it through a Neural Network and then check for accuracy.  You may want to check if there is any correlation between the 4 junctions as well but if you run a NN, you don't have to.