Inferring Travel Purposes for Transit Smart Card Data Using

Understanding travel purposes is crucial for urban transportation planning and resource allocation. Conventionally, travel purposes are obtained from household travel survey, however, household travel survey is usually conducted every 5–10 years and only has 1–2% sampling size over the whole urban dwellers. Therefore, the information on travel purposes is very limited and usually biased which cause mismatch in urban and transportation planning. Meanwhile, many cities have accumulated a large amount of transit data, such as those from transit smart cards. Such data contains many individual traveling records, but has not been included to generate travel survey data because of lacking the information of travel purposes. To make fully use the data and to generate more comprehensive travel data, this study attempted to infer travel purposes for smart card data by a naïve Bayes probabilistic model. Experimental results demonstrated that proposed method could infer commuting activities with the accuracy of more than 95%, while the accuracy of predicting other activities was about 60%. This is a promising approach to integrated big data into transportation work routines.