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contributor authorChunyan Shuai
contributor authorWenCong Wang
contributor authorGeng Xu
contributor authorMin He
contributor authorJaeyoung Lee
date accessioned2022-05-07T20:46:43Z
date available2022-05-07T20:46:43Z
date issued2022-03-30
identifier otherJTEPBS.0000660.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4282889
description abstractReal-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters.
publisherASCE
titleShort-Term Traffic Flow Prediction of Expressway Considering Spatial Influences
typeJournal Paper
journal volume148
journal issue6
journal titleJournal of Transportation Engineering, Part A: Systems
identifier doi10.1061/JTEPBS.0000660
journal fristpage04022026
journal lastpage04022026-9
page9
treeJournal of Transportation Engineering, Part A: Systems:;2022:;Volume ( 148 ):;issue: 006
contenttypeFulltext


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