Abstract. Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per 1x1 km 2 area.
To estimate the number of vacant taxis, Santi et al. proposes a predictive model REF based on a Bayesian classifier that can address uncertainty using an error-based learning method and detect the adequacy of data by calculating information entropy.
Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City
AmI
Computer Science