Predicting passenger’s public transportation travel route using smart card data

Chen Yang, Wei Chen, Bolong Zheng, Tieke He, Kai Zheng, Han Su*

*Kontaktforfatter

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Abstract

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.

OriginalsprogEngelsk
TitelWeb and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
Antal sider15
ForlagSpringer
Publikationsdato1 jan. 2018
Sider199-213
ISBN (Trykt)9783319968926
ISBN (Elektronisk)978-3-319-96893-3
DOI
StatusUdgivet - 1 jan. 2018
Begivenhed2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, Kina
Varighed: 23 jul. 201825 jul. 2018

Konference

Konference2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Land/OmrådeKina
ByMacau
Periode23/07/201825/07/2018
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind10988 LNCS
ISSN0302-9743

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