Are there any temporal feature selection method to extract the most significant information for road traffic forecasting?

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Omar S. Al-Kadi

If you are considering a computer vision approach for the time lags, the following work might be of interest. A pattern analysis approach using multilevel thresholding for categorising the road congestion state can be found here::

Article Road scene analysis for determination of road traffic density

Kundan Meshram

Dear Sir,

pl check these articles. it will helpful for your research work.

Article Learning Traffic as Images: A Deep Convolutional Neural Netw...

https://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-243.pdf

https://arxiv.org/ftp/arxiv/papers/1705/1705.02699.pdf

Arturo Geigel

Rajae ,

Several points to consider on your question:

What are the specific features that you are selecting to do the forecasting such as raw image as in article 1 from Kunan, which I think will provide mixed results as opposed to more specific high level features such as density, road size, vehicle size(determines maneuvering capability, etc.)

The features will determine more than likely the time interval. For example, saturation (density of cars that will start to decrease the flow below a threshold gamma) will come faster for a smaller road than for a big one and you will have to decrease the time slice for it.

The optimal time interval can be determined by using multiple time frames at first to determine average time to saturation for a specific road. Small two or one lane roads will saturate faster upon x number of cars than a 5 lane

Rajae Haouari

Thank you very much for your replies.

But apparently, my question was ambiguous. My research is in data analysis field. I work with numerical and statistical values. My dataset contains records of vehicle counts (traffic volume) and average speed collected every 5 minutes. Therefore, I need an approach to find the optimal number of past values of the traffic volume that need to be used to predict its values in the future (15min, 30min, 45min, and 1h).