| Summary: | Intelligent Transport System (ITS) are advanced artificially intelligent systems that
offer services pertaining to different traffic management and transport modes and
enable the users to make a more informed and safer decision about the route they
wish to take to reach their destination.
Speed is a very important parameter when it comes to Intelligent Transport Systems.
Predicting the speeds of vehicles at a future instant of time lets us know whether the
traffic is going to be fairly smooth or congested. Also while obtaining data from loop
detectors, sensors etc some of the values might be missing. Accurate prediction of
the speed values can lead to the creation of low-dimensional models and also for
missing data imputation.
Loads of work has been done recently on predicting the speed values for a single link
at a particular instant of time. However, our motivation was to predict speed values
for multiple horizons simultaneously. Partial Least Squares (PLS), N-way PLS and
Higher Order Partial Least Squares (HO-PLS) are the proposed models for this
approach.
266 links were selected at random and the different prediction algorithms were
trained. We were successful in predicting the speeds for 5 minutes, 1 0 minutes, 15
minutes and 30 minutes. N-way PLS slightly proved to be the best method for
multiple horizon speed prediction for this particular dataset.
|
|---|