Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

2017 ◽  
Vol 18 (4) ◽  
pp. 890-901 ◽  
Author(s):  
Ruimin Ke ◽  
Zhibin Li ◽  
Sung Kim ◽  
John Ash ◽  
Zhiyong Cui ◽  
...  
2020 ◽  
Vol 14 (12) ◽  
pp. 1517-1523
Author(s):  
Shuang Li ◽  
Faliang Chang ◽  
Chunsheng Liu ◽  
Nanjun Li

2021 ◽  
Author(s):  
Hengye Yang ◽  
Dongheng Jing ◽  
Vahid Tarokh ◽  
Gregory Bewley ◽  
Silvia Ferrari

2021 ◽  
Vol 62 (2) ◽  
Author(s):  
Jason D. Christopher ◽  
Olga A. Doronina ◽  
Dan Petrykowski ◽  
Torrey R. S. Hayden ◽  
Caelan Lapointe ◽  
...  

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.


2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.


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