scholarly journals Highway traffic model-based density estimation

Author(s):  
Irinel-Constantin Morarescu ◽  
Carlos Canudas-de-Wit
2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.


1975 ◽  
Vol 12 (S1) ◽  
pp. 303-309
Author(s):  
Herbert Solomon

The trajectory of a car traveling at a constant speed on an idealized infinite highway can be viewed as a straight line in the time-space plane. Entry times are governed by a Poisson process with intensity parameter A leading to all trajectories as random lines in a plane. The Poisson distribution of number of encounters of cars on the highway is developed through random line models and non-homogeneous Poisson fields, and its parameter, which depends on the specific random measure employed, is obtained explicitly.


Author(s):  
Heng-Da Cheng ◽  
Haining Du ◽  
Liming Hu ◽  
Chris Glazier

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.


2019 ◽  
Vol 23 (19) ◽  
pp. 9397-9412 ◽  
Author(s):  
Zheng-Tao Xiang ◽  
Zhan Gao ◽  
Tao Zhang ◽  
Kai Che ◽  
Yu-Feng Chen

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