Simplified Monte Carlo Model of Real-Time Traffic Flow Prediction

2011 ◽  
Vol 97-98 ◽  
pp. 867-871
Author(s):  
Jian Ping Xing ◽  
Ling Guo Meng ◽  
Can Sun ◽  
Jian Wen Li
Keyword(s):  
Monte Carlo ◽  
Real Time ◽  
Traffic Flow ◽  
Time Evolution ◽  
Monte Carlo Model ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Speed Up ◽  
Visual Interface

Simplified Monte Carlo collision model of real-time traffic flow prediction is proposed. In this model, two different road cells, roads and crosses, are configured. Vehicle distribution is generated by real-time traffic flow randomly. Based on two-dimensional topology, Monte Carlo collision between road and vehicles promote time evolution of the system. Monte Carlo collision is the core of the model and traffic flow is study target. The solution of relationship equations of road and vehicles is very simple in this model to speed up the computing. In addition, parameters can be corrected and configured at any time in the process of time evolution. Experimental results show that the model has the advantages of real-time, visual interface, easy configuration, and can be corrected by real-time feedback. The model can not only simulate and predict macroscopic data, such as flow, velocity distribution, but also follow the track of each vehicle in detail. So, the model can be used in researching both macroscopic and microscopic characteristics of vehicle movement.

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.

scholarly journals Traffic Flow Prediction with Relevance Vector Machine

2021 ◽  
Author(s):  
W.-Z. Xiong ◽  
X.-M. Shen ◽  
H.-J. Li ◽  
Z. Shen
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Sequence Data ◽  
Machine Method ◽  
Traffic Flow Prediction ◽  
Correlation Vector ◽  
Model Based ◽  
Flow Prediction ◽  
Short Period

Abstract Real-time prediction of traffic flow values in a short period of time is an importantelement in building a traffic management system. The uncertainty, complexity andnonlinearity of traffic flow data make it difficult to predict traffic flow in real time,and the accurate traffic flow prediction has been an urgent problem in the industry.Based on the research of scholars, a traffic flow prediction model based on thecorrelation vector machine method is constructed. The prediction accuracy of thecorrelation vector machine is better than that of the logistic regression and supportvector machine methods, and the correlation vector machine method has the functionof generating prediction error range for the actual traffic sequence data. Theprediction results are very satisfactory, and the prediction speed is significantlyfaster than the other two models, which meets the requirement of real-time trafficflow prediction and is suitable for real-time online prediction, and the predictionaccuracy of the used method is relatively high. The three-way comparison analysisshows that the traffic flow prediction by the correlation vector machine methodcan describe the nonlinear characteristics of traffic flow change more accurately,and the model performance and real-time performance are better. The case studyshows that the traffic flow prediction model based on the correlation vector machinecan improve the speed and accuracy of prediction, which is very suitablefor traffic flow prediction estimation with real-time requirements, and provides ascientific method for real-time traffic flow measurement.

scholarly journals A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 38 ◽  
Author(s):  
Zhihong Yao ◽  
Yibing Wang ◽  
Wei Xiao ◽  
Bin Zhao ◽  
Bo Peng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Signal Control ◽  
Dynamic Traffic ◽  
Traffic Flow Prediction ◽  
Adaptive Signal Control ◽  
Flow Prediction ◽  
Vehicle Delay ◽  
Rolling Optimization ◽  
Adaptive Signal

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

scholarly journals 3D Markov Process for Traffic Flow Prediction in Real-Time

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 147 ◽  
Author(s):  
Eunjeong Ko ◽  
Jinyoung Ahn ◽  
Eun Kim
Keyword(s):  
Markov Process ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Flow Prediction ◽  
Flow Prediction
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