Journal of South China University of Technology(Natural Science) >
Speed Prediction for Road Around Large Scale Activities Venues Considering Multiple Factors Synergism
Received date: 2023-06-05
Online published: 2024-01-25
Supported by
the National Natural Science Foundation of China(52072011)
Large scale activities can cause a sudden increase and dissipation of traffic flow in the area around the venue, resulting in occasional and uncertain fluctuations of the road network operation in the surrounding area. The existing methods are insufficient to capture the evolution mechanism of traffic flow under the influence of multi-dimensional factors in special events at the prediction scale. In order to fully exploit the information of time series and influencing factor features of road section speed and effectively deal with the coupling mechanism between different influencing features in speed prediction, this paper proposed a speed prediction model (MC-LSTM) combining Interpretable Machine Learning and Long Short-Term Memory network. Firstly, the study combined the characteristics of large scale activities to construct the set of influencing factors. Then it used the XGBoost algorithm to evaluate the relative importance of the impact of activity scale, nature and other factors characteristics on the speed of road sections around the venue. It quantified the synergistic utility of multiple factors on the operation state of the road network around the venue, fused LSTM networks, considered the time-dependent relationship of traffic state, captureed the temporal correlation of different historical periods, and accurately predicted the speed of road sections around the venue during the activity. MC-LSTM was validated by taking the road network around large scale activities venues in Beijing for six consecutive months. The results indicate that the prediction accuracy of the MC-LSTM model can reach more than 94.5%, which is better than that of XGBoost model considering multiple factors synergism, LSTM model considering only single factor features and the LSTM model not considering external features. It proved that the model proposed in this paper has better validity and stability. This study can provide a decision basis for optimizing the traffic organization of the road network around the large scale activities venues and formulating traffic control and security measures.
WENG Jiancheng , WU Mingzhu , WEI Ruicong , WANG Jingjing , MAO Lizeng . Speed Prediction for Road Around Large Scale Activities Venues Considering Multiple Factors Synergism[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(8) : 34 -44 . DOI: 10.12141/j.issn.1000-565X.230386
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