scholarly journals Optimization Method of Intersection Signal Coordinated Control Based on Vehicle Actuated Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Chen Zhao-Meng ◽  
Liu Xiao-ming ◽  
Wu Wen-Xiang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Control Method ◽  
Coordinated Control ◽  
Optimization Method ◽  
Early Start ◽  
Numerical Examples ◽  
Bandwidth Optimization ◽  
Real Time Traffic ◽  
Late Start

Traditional timing green wave control with predetermined cycle, split, and offset cannot adapt for dynamic real-time traffic flow. This paper proposes a coordinated control method for variable cycle time green wave bandwidth optimization integrated with traffic-actuated control. In the coordinated control, green split is optimized in real time by the measured presence of arriving and/or standing vehicles in each intersection and simultaneously green waves along arterials are guaranteed. Specifically, the dynamic bound of green wave is firstly determined, and then green early-start and green late-start algorithms are presented respectively to accommodate the fluctuations in vehicle arrival rates in each phase. Numerical examples show that the proposed method improves green time, expands green wave bandwidth, and reduces queuing.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

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.

Vietnamese vehicles speed detection with video-based and deep learning for real-time traffic flow analysis system

2021 ◽  
Author(s):  
Phuoc Ha Quang ◽  
Phong Pham Thanh ◽  
Tuan Nguyen Van Anh ◽  
Son Vo Phi ◽  
Binh Le Nhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Analysis ◽  
Real Time Traffic ◽  
Analysis System ◽  
Traffic Flow Analysis

scholarly journals Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-pu Lu ◽  
Zhi-yuan Sun ◽  
Wen-cong Qu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Flow ◽  
Optimization Model ◽  
Bilevel Optimization ◽  
Data Driven ◽  
Stage Model ◽  
Flow State ◽  
Real Time Traffic ◽  
State Identification

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

An New Approach of Real-Time Traffic Flow Prediction Based on Intelligent Transportation Technology

2014 ◽  
Vol 988 ◽  
pp. 715-718
Author(s):  
Jia Yang Li ◽  
Qin Xue ◽  
Jin De Liu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Short Term ◽  
New Approach ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Combination Approach

Short-term traffic flow forecasting is a core problem in Intelligent Transportation System .Considering linear and nonlinear, this paper proposes a short-term traffic flow intelligent combination approach. The weight of four forecasting model is given by the correlation coefficient and standard deviation method. The experimental results show that the new approach of real-time traffic flow prediction is higher precision than single method.

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