Predicting number of cars I am predicting the number of cars from a traffic dataset.
Here is my data dictionary:
The ‘Traffic-Major-Roads(kilometres)’ file contains the following variables (variable names
are in bold):

Year - Traffic volumes are shown for each year from 2000 onwards.
CP (count point) – a unique reference for the road link that links the AADFs to the road
network.
ONS GOR Name – the former Government Office Region that the CP sits within.
ONS LA Name – the local authority that the CP sits within.
Road – this is the road name (for instance M25 or A3).
RCat – the classification of the road type (see data definitions for the full list).
S Ref E – Easting coordinates of the CP location.
S Ref N – Easting coordinates of the CP location.
A-Junction – The road name of the start junction of the link
B-Junction – The road name of the end junction of the link
LenNet – Total length of the network road link for that CP (in kilometres).
PC – Traffic volume (in thousands of vehicle kilometres) for pedal cycles.
2WMV – Traffic volume (in thousands of vehicle kilometres) for two-wheeled motor
vehicles.
Car - Traffic volume (in thousands of vehicle kilometres) for Cars and Taxis.
Bus – Traffic volume (in thousands of vehicle kilometres) for Buses and Coaches
LGV – Traffic volume (in thousands of vehicle kilometres) for LGVs.
HGVR2 – Traffic volume (in thousands of vehicle kilometres) for two-rigid axle HGVs.
HGVR3 – Traffic volume (in thousands of vehicle kilometres) for three-rigid axle HGVs.
HGVR4 – Traffic volume (in thousands of vehicle kilometres) for four or more rigid axle
HGVs.
HGVA3 – Traffic volume (in thousands of vehicle kilometres) for three or fourarticulated axle HGVs.
HGVA5 – Traffic volume (in thousands of vehicle kilometres) for five-articulated axle
HGVs.
HGVA6 – Traffic volume (in thousands of vehicle kilometres) for six-articulated axle
HGVs.
HGV – Traffic volume (in thousands of vehicle kilometres) for all HGVs.
AMV – Traffic volume (in thousands of vehicle kilometres) for all motor vehicles.

I need to predict the variable AMV.
So, I have one-hot encoded Road, and kept date, time in my features.
But, number of Roads being very large. I have too many features.
Can you please suggest how should I proceed?
I am having too many features.


No, you don't :).

First of all, it is highly likely that not all of them are important for the prediction that you want to make.

I would highly recommend using a CART Random Forest for regression of the variable of interest. It literally requires minimal coding if you choose to do it in python using the RF algorithm from the sklearn package.

It's big advantage is that it is straightforward to use and understand and, moreover, it provides you with the learnt feature_importances_ of all inputs
after training, so that you can exclude the least important ones and speed up the inference/training in the future.

-EDIT-

To understand the difference between Classification and Regression Decision trees, check this helpful link.

The decision tree implementations for regression are commonly the C4.5, the C5.0 or the CART algorithm. The one that is used by sklearn is CART, please take a look at section 1.10.6 in this link. 

A good example of how to use the sklearn Decision Tree for regression is this.