Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects. (English) Zbl 1389.68093

Yang, Shuo; Deng, Wei; Cheng, Long

J. Southeast Univ., Engl. Ed. 33, No. 1, 106-114 (2017).

Summary: In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods: the support vector machine (SVM) model, the radial basis function

•  Scholarpedia
•  Wikipedia

neural network (RBFNN) model and the multinomial logit (MNL) model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performance were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a small sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns and exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.

MSC:

68T05	Learning and adaptive systems in artificial intelligence
68T10	Pattern recognition, speech recognition

Keywords:

trip chaining patterns; support vector machine; recognition performance; sensitivity analysis