The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State

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.

scholarly journals Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

2021 ◽  
Vol 13 (2) ◽  
pp. 14-23
Author(s):  
Mehran Amini ◽  
Hatwagn Miklos F. ◽  
Gergely Mikulai ◽  
Laszlo T. Koczy
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Flow Simulation ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Macroscopic Model ◽  
Route Guidance ◽  
Flow State ◽  
Macroscopic Modeling ◽  
The Road

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.

Adaptive Fuzzy Urban Traffic Flow Control Using a Cooperative Multi-Agent System based on Two Stage Fuzzy Clustering

2009 ◽  
Author(s):  
Fatemeh Daneshfar ◽  
Javad RavanJamJah ◽  
Fathollah Mansoori ◽  
Hassan Bevrani ◽  
Bahram Zahir Azami
Keyword(s):  
Flow Control ◽  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Urban Traffic ◽  
Multi Agent System ◽  
Two Stage ◽  
Agent System ◽  
Adaptive Fuzzy ◽  
Multi Agent

scholarly journals Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 88
Author(s):  
Xiaoyuan Wang ◽  
Junyan Han ◽  
Chenglin Bai ◽  
Huili Shi ◽  
Jinglei Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Free Flow ◽  
Traffic Information ◽  
Flow State ◽  
Simulation Framework ◽  
Car Following ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Vehicle Information

With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

Prediction of traffic flow with small time granularity at intersection based on probabilistic network

2020 ◽  
Vol 39 (2) ◽  
pp. 1659-1670
Author(s):  
Wenbin Xiao ◽  
Shunying Zhu ◽  
Qiucheng Chen
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Small Time ◽  
Traffic Density ◽  
Flow Section ◽  
Flow State ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Probabilistic Network ◽  
Flow Prediction

In order to overcome the inaccuracy of current research results of traffic flow prediction, this paper proposes a prediction method for traffic flow with small time granularity at intersection based on probability network. This method takes one minute as time granularity, collects traffic data such as cross-section flow, section traffic flow velocity data, traffic density, road occupancy, section delay and steering ratio by using RFID technology, and analyzes and processes the data. By introducing Bayesian network in probabilistic network and combining K-nearest neighbor method, historical data and predicted traffic flow state are classified to realize the prediction of traffic flow with small time granularity at intersections. The experimental results show that this method has high prediction accuracy and reliability, and is a feasible traffic flow prediction method.

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