Predicting taxi passenger demand using artificial neural networks
Abstract: In this report a machine learning method using artificial neural networks to estimate taxi demand in different geographical zones in the city of Stockholm is proposed. An attempt to determine the most important input features that affect taxi ridership is performed and a network architecture is conceived and trained using taxi ridership data from a major taxi company operating in the city. The results show that except for the two basic input parameters, the hour of the day and the zone, the day of the week is clearly the most important factor. Also days after payment and month of the year seems to be mildly relevant factors while rain and temperature hardly affect the results at all. The final network model conceived was capable of estimating taxi demand in Stockholm with an average error of 2.73 rides and a success rate of 46 % of the rides using a boundary of 30 % or 1 ride.
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