TY - JOUR
T1 - Short-Term Traffic Flow Prediction Based on Road Network Topology
AU - Jin, Feng
AU - Zhao, Baicheng
N1 - Publisher Copyright:
© 2019 Editorial Department of Journal of Beijing Institute of Technology.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).
AB - Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).
KW - Gated recurrent unit (GRU)
KW - Intelligent transportation systems
KW - Road network topology
KW - Traffic flow prediction
UR - http://www.scopus.com/inward/record.url?scp=85076675189&partnerID=8YFLogxK
U2 - 10.15918/j.jbit1004-0579.18001
DO - 10.15918/j.jbit1004-0579.18001
M3 - Article
AN - SCOPUS:85076675189
SN - 1004-0579
VL - 28
SP - 383
EP - 388
JO - Journal of Beijing Institute of Technology (English Edition)
JF - Journal of Beijing Institute of Technology (English Edition)
IS - 3
ER -