Learning to Predict the Duration of an Automobile Trip

Simon Handley

In the near future large numbers of automobiles will each contain a GPS unit that accurately estimates the vehicle’s current location. We are interested in mining useful information from these potentially-huge behavioural databases. In this paper we focus on inferring the duration of a future automobile trip from the durations of past trips. We compare our system with a benchmark system that assumes that the speed at each step along a route is constant for all times and dates. We hypothesize that a learning approach can produce models of trip durations that outperform this benchmark system. Lacking a large database of observed trip durations on which to test this hypothesis, we created a semi-artificial database from real traffic speed data collected from San Diego freeways. We found that, indeed, our learning approach can learn predictors that are more useful than the benchmark system.


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