TY - GEN
T1 - Predicting passenger’s public transportation travel route using smart card data
AU - Yang, Chen
AU - Chen, Wei
AU - Zheng, Bolong
AU - He, Tieke
AU - Zheng, Kai
AU - Su, Han
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Transit prediction is a important task for public transport institutions and urban planners to provide better transit scheduling and urban planning. In recent years, there are a lot of research on traffic prediction, but the existing works focus predicting the monolithic traffic trend, and few works focus on passenger’s public transportation travel route. In this paper, we study the passenger’s travel route and duration prediction. We propose a prediction model based on LSTM neural network to predict passenger’s travel route and duration. Specifically, we leverage multimodal embedding to extract passenger’s features which are highly related to passenger’s travel route and then use a LSTM-based model to improve the prediction accuracy. To verify the effectiveness of our model, we conduct extensive experiments using a real dataset which is collected from Brisbane in Australia for four months. The experimental results show that the accuracy of our model is better than baseline models.
AB - Transit prediction is a important task for public transport institutions and urban planners to provide better transit scheduling and urban planning. In recent years, there are a lot of research on traffic prediction, but the existing works focus predicting the monolithic traffic trend, and few works focus on passenger’s public transportation travel route. In this paper, we study the passenger’s travel route and duration prediction. We propose a prediction model based on LSTM neural network to predict passenger’s travel route and duration. Specifically, we leverage multimodal embedding to extract passenger’s features which are highly related to passenger’s travel route and then use a LSTM-based model to improve the prediction accuracy. To verify the effectiveness of our model, we conduct extensive experiments using a real dataset which is collected from Brisbane in Australia for four months. The experimental results show that the accuracy of our model is better than baseline models.
KW - Multimodal embedding
KW - Smart card
KW - Transit prediction
UR - http://www.scopus.com/inward/record.url?scp=85051101288&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-96893-3_15
DO - 10.1007/978-3-319-96893-3_15
M3 - Article in proceeding
AN - SCOPUS:85051101288
SN - 9783319968926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 213
BT - Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
PB - Springer
T2 - 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Y2 - 23 July 2018 through 25 July 2018
ER -