The time required for a vehicle to travel different routes in the daily traffic of large cities varies and changes constantly, impacting the daily lives of everyone dwelling in those cities. Trying to predict such time is essential to evolve in understanding the behavior of vehicle traffic. On the other hand, due to the vast amount of data generated in this context, it is necessary to use new ways of dealing with this problem. This paper presents an exploratory analysis of the behavior of batch and stream learning algorithms for predicting the trip duration time for vehicles going through different routes. We understand that batch learning algorithms are not necessarily adequate for being used in stream mining situations. However, we would like to have a testbed to analyze the behavior of stream learning algorithms. For our experimental analysis, we used real data from three specific routes. The results show that the use of data stream learning for this domain yields promising results

Leandro Miranda

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