scholarly journals Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Qichun Bing ◽  
Ciyun Lin ◽  
Nan Yang ◽  
Duo Mei
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Similarity Search ◽  
Prediction Method ◽  
Flow Data ◽  
Short Term ◽  
Similar Time ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Advanced Traffic Management System

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

2015 ◽  
Vol 22 (2) ◽  
pp. 779-786 ◽  
Author(s):  
Meng Meng ◽  
Chun-fu Shao ◽  
Yiik-diew Wong ◽  
Bo-bin Wang ◽  
Hui-xuan Li
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Short Term ◽  
Two Stage ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

2020 ◽  
Vol 32 (6) ◽  
pp. 821-835
Author(s):  
Jing Luo
Keyword(s):  
Quantile Regression ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Time Data ◽  
Short Term ◽  
Internet Of Vehicles ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

Sign in / Sign up

Export Citation Format

Share Document