• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Mou, L. (Mou, L..) | Zhao, P. (Zhao, P..) | Chen, Y. (Chen, Y..)

Indexed by:

Scopus

Abstract:

Real-time and accurate short-term traffic flow prediction can effectively improve the efficiency and safety of the transportation system. However, complex traffic systems are highly nonlinear and random, which makes short-term traffic flow prediction a challenging issue. In recent years, deep-learning based methods have been widely applied in short-term traffic flow prediction. Particularly, the long short-term memory neural network (LSTM) model bears great potential for its capability in learning from temporal information. In this paper, an improved LSTM model is used to predict the short-term traffic flow of a target road section of the East 4th Ring Road of Beijing, and to analyze the influence of different input configuration on prediction accuracy as well. Experimental results demonstrate that feeding upstream flow and velocity information does improve its overall performance. Especially after traffic flow information is fed with corresponding temporal information, the accuracy of traffic flow prediction has been significantly improved. © ASCE.

Keyword:

Author Community:

  • [ 1 ] [Mou, L.]Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing Univ. of Technology, Beijing, China
  • [ 2 ] [Zhao, P.]Dept. of Information, Beijing Univ. of Technology, Beijing, China
  • [ 3 ] [Chen, Y.]Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing Univ. of Technology, Beijing, China

Reprint Author's Address:

  • [Chen, Y.]Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing Univ. of TechnologyChina

Show more details

Related Keywords:

Related Article:

Source :

CICTP 2019: Transportation in China - Connecting the World - Proceedings of the 19th COTA International Conference of Transportation Professionals

Year: 2019

Page: 2411-2422

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

Online/Total:724/5309636
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.