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Author:

Gao, Yacong (Gao, Yacong.) | Zhou, Chenjing (Zhou, Chenjing.) | Rong, Jian (Rong, Jian.) | Wang, Yi (Wang, Yi.) | Liu, Siyang (Liu, Siyang.)

Indexed by:

EI Scopus SCIE

Abstract:

Accurate traffic speed forecasting not only can help traffic management departments make better judgments and improve the efficacy of road monitoring but also can help drivers plan their driving routes and arrive safely and smoothly at their destination. This paper focuses on the lack of traffic speed data and proposes a method for traffic speed forecasting based on the multitemporal traffic flow volume of the previous and later moment states. First, according to traffic flow volume data, the different traffic patterns of previous and later moment states were extracted. Second, the performance of five forecasting models, namely, long short-term memory (LSTM), backpropagation (BP), classification and regression trees, k-nearest neighbor, and support vector regression, were compared. Finally, the model with the best prediction results was used to conduct sensitivity analysis experiments for different traffic patterns. Through a real-data case study, we found that the LSTM model has the highest prediction accuracy compared to other models in both time and space. This traffic pattern "previous = 3 and later = 3" can forecast traffic speed more accurately, and its forecasting ability is robust across a range of scenarios.

Keyword:

Forecasting Roads Data mining Traffic control Traffic speed forecasting traffic patterns Prediction algorithms Predictive models deep learning Data models traffic flow theory

Author Community:

  • [ 1 ] [Gao, Yacong]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Yi]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Liu, Siyang]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Zhou, Chenjing]Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China
  • [ 5 ] [Rong, Jian]Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China

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Source :

IEEE ACCESS

ISSN: 2169-3536

Year: 2022

Volume: 10

Page: 82384-82395

3 . 9

JCR@2022

3 . 9 0 0

JCR@2022

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 13

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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