Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems.  present an approach based on trip matching and ensemble learning, in which  leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding final destination and travel time for ongoing taxi trips, as a solution for the ECML/PKDD Discovery Challenge 2015 competition. The results of our empirical evaluation show that our approach is effective and very robust, which led our team -- BlueTaxi -- to the 3rd and 7th position of the final rankings for the trip time and destination prediction tasks, respectively. Given the fact that the final rankings re computed using a very small test set (with only 320 trips)  believe that our approach is one of the most robust solutions for the challenge based on the consistency of our good results across the test sets.
['Hoang Thanh Lam', 'Ernesto Diaz-Aviles', 'Alessandra Pascale', 'Yiannis Gkoufas', 'Bei Chen']
I am writing a research paper on a idea Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
[{"from": "human", "value": "I am writing a research paper on a idea Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems.  present an approach based on trip matching and ensemble learning, in which  leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding final destination and travel time for ongoing taxi trips, as a solution for the ECML/PKDD Discovery Challenge 2015 competition. The results of our empirical evaluation show that our approach is effective and very robust, which led our team -- BlueTaxi -- to the 3rd and 7th position of the final rankings for the trip time and destination prediction tasks, respectively. Given the fact that the final rankings re computed using a very small test set (with only 320 trips)  believe that our approach is one of the most robust solutions for the challenge based on the consistency of our good results across the test sets."}]