Traffic flow prediction of expressway traffic emergency recovery and reconstruction period(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年05期
- Page:
- 139-145
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Traffic flow prediction of expressway traffic emergency recovery and reconstruction period
- Author(s):
- ZHAO Peng; WANG Jianwei; SUN Maopeng; ZHOU Yaxin
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- Keywords:
- traffic engineering; expressway; emergency; DCRNN model; reconstruction
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to improve the capacity of emergency management of expressway emergencies, provides references and help for emergency recovery and reconstruction or construction for prediction congestion. A method for predicting the traffic volume of an expressway road network during the recovery and reconstruction period after an expressway emergency was proposed, based on previous research results. Based on the diffusion convolution and a sequencetosequence learning framework, the proposed method captured the spacetime correlation of a time series using a sampling technique. With this method, the distribution of the traffic volume timeaccuracy was first realized by establishing a road network traffic volume distribution model based on highway network charging data, and the expressway road network traffic volume predictive model was then constructed using a diffusion convolution recurrent neural network (DCRNN). The DCRNN model was used to capture the spatial correlation of the traffic volume, and effectively solved the timedependent problem of the traffic volume by using the predetermined sampling encoderdecoder structure. The advantage of the model lies in modeling the spatial property of traffic as a directed graph diffusion process rather than as a traditional grid division, which clearly describes the randomness of the traffic dynamics. To verify the accuracy and validity of the model further, the widely used ARIMA model and the machine learning BP neural network model were selected to calculate and compare the same instance data. The results show that the first 15 days of data in the traffic volume after an emergency of a highway network in Hebei Province Shijiazhuang is used as the training set, and the data after 7 days is verified. When the number of iterations reached 60, the accuracy of the model that calculate the traffic volume per 15 minutes interval reached 0.95. The forecasting method has a lower mean absolute error (MAE) and mean absolute percentage error (MAPE), which can effectively compensate for the defects of the unitized neural networkforecasting model, which only creates a time series prediction. Therefore, the model has high accuracy and practicability. 4 tabs, 2 figs, 22 refs.
Last Update: 2018-10-23