Short-Term Prediction Model of Taxi Passenger Demand Based on Operation Systems

doi:10.12068/j.issn.1005-3026.2016.09.005

Abstract

Abstract: As a critical component of urban transportation systems， the service level of taxis is significantly affected by taxi planning and dispatching. The objective of this study is to estimate and predict taxi passenger demand to support for planning and dispatching. Firstly， the data collection of the in-vehicle taxi GPS system and fare collection system are analyzed in the paper. In terms of data analysis， the traditional grid partition of taxi demand is improved by adding other factors， such as topography， buildings， and road network. The developed partition preserves the completeness of passenger demand in a grid. And then， an easy-to-use estimation method of grid-based demand is presented by the usage of real-time taxi GPS system and fare collection system. Finally， an artificial neural network (ANN) model is developed to predict short-term taxi demand. The structure of the ANN model is designed based on the functional characteristics of the input-output pairing correlation. Taking the field data from taxi operation system as an example， the performance of proposed estimation and prediction models is evaluated and validated. The results reveal that the proposed ANN prediction model significantly outperforms the existing auto-regression-moving-average (ARMA) model in terms of the reduction of 32% on average absolute percentage error. Moreover， the probability of absolute percentage error greater than 50% for both ANN and ARMA models is 10% and 23%， respectively.

Key words: taxi operations, passenger demand estimation model, grid, short-term prediction, artificial neural network(ANN)

CLC Number:

U491.5⁺4

LIN Yong-jie， ZOU Nan. Short-Term Prediction Model of Taxi Passenger Demand Based on Operation Systems[J]. Journal of Northeastern University Natural Science, 2016, 37(9): 1235-1240.

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