TGAE: Temporal Graph Autoencoder for Travel Forecasting

With the development of intelligent transportation systems, timely and accurate travel forecasting task has witnessed growing interest. Unlike most previous research that only considers the demand prediction in origin regions, this task aims to predict the origin-destination demand between all-regio...

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Vydáno v:	IEEE transactions on intelligent transportation systems Ročník 24; číslo 8; s. 1 - 13
Hlavní autoři:	Wang, Qiang, Jiang, Hao, Qiu, Meikang, Liu, Yifeng, Ye, Dongsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	demand prediction Forecasting Graph neural networks Graph theory Heuristic algorithms Intelligent transportation systems Links Optimization origin-destination Peer-to-peer computing Predictive models Task analysis temporal graph autoencoder temporal networks Traffic information Traffic management Transportation Transportation networks Travel forecasting Vehicle dynamics
ISSN:	1524-9050, 1558-0016
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Abstract	With the development of intelligent transportation systems, timely and accurate travel forecasting task has witnessed growing interest. Unlike most previous research that only considers the demand prediction in origin regions, this task aims to predict the origin-destination demand between all-region pairs. Its main challenges come from effectively capturing the direction, weight, and temporal information of links in dynamic traffic networks. To confront these challenges, we treat the dynamic traffic networks as multiple weighted directed network snapshots and propose a graph-based deep learning framework, Temporal Graph Autoencoder (TGAE). Specifically, TGAE encodes the fundamentally asymmetric nature of a directed graph via directed neighborhood aggregation and learns a pair of vector representations for each node. Meanwhile, we use the graph attention mechanism to capture the weight information of links. Next, TGAE preserves the temporal dependencies by independently reconstructing the existence and weight of links over two consecutive time steps. Furthermore, we employ the Long Short-Term Memory network (LSTM) to capture the evolution patterns of traffic networks and predict both the direction and weight of links based on the historical data. Experimental results demonstrate that TGAE outperforms several baseline methods on the travel forecasting task, which can help traffic management, resource preallocation, and services optimization.
AbstractList	With the development of intelligent transportation systems, timely and accurate travel forecasting task has witnessed growing interest. Unlike most previous research that only considers the demand prediction in origin regions, this task aims to predict the origin-destination demand between all-region pairs. Its main challenges come from effectively capturing the direction, weight, and temporal information of links in dynamic traffic networks. To confront these challenges, we treat the dynamic traffic networks as multiple weighted directed network snapshots and propose a graph-based deep learning framework, Temporal Graph Autoencoder (TGAE). Specifically, TGAE encodes the fundamentally asymmetric nature of a directed graph via directed neighborhood aggregation and learns a pair of vector representations for each node. Meanwhile, we use the graph attention mechanism to capture the weight information of links. Next, TGAE preserves the temporal dependencies by independently reconstructing the existence and weight of links over two consecutive time steps. Furthermore, we employ the Long Short-Term Memory network (LSTM) to capture the evolution patterns of traffic networks and predict both the direction and weight of links based on the historical data. Experimental results demonstrate that TGAE outperforms several baseline methods on the travel forecasting task, which can help traffic management, resource preallocation, and services optimization.
Author	Qiu, Meikang Liu, Yifeng Wang, Qiang Ye, Dongsheng Jiang, Hao
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References	ref35 ref15 ref37 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 jiao (ref13) 2021 ref17 ref16 ref38 zhao (ref43) 2018 ref19 ref18 arjovsky (ref39) 2017; 70 yu (ref6) 2017 ref24 ref46 ref23 ref26 ref25 ref20 ref22 ref44 ref21 zhu (ref45) 2018; 31 veli?kovi? (ref42) 2017 ref28 ref27 zhao (ref34) 2015; 5 ref29 jiang (ref12) 2022; 207 ref8 ref7 ref9 ref4 ref3 brody (ref41) 2021 ref5 ref40 kazemi (ref14) 2020; 21
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SubjectTerms	demand prediction Forecasting Graph neural networks Graph theory Heuristic algorithms Intelligent transportation systems Links Optimization origin-destination Peer-to-peer computing Predictive models Task analysis temporal graph autoencoder temporal networks Traffic information Traffic management Transportation Transportation networks Travel forecasting Vehicle dynamics
Title	TGAE: Temporal Graph Autoencoder for Travel Forecasting
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