scholarly journals Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Wang ◽  
Shimin Lin ◽  
Jingfeng Yang ◽  
Nanfeng Zhang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Theory Model ◽  
Point Of View ◽  
Urban Traffic

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

scholarly journals Analysis of Intra-Urban Traffic Accidents Using Spatiotemporal Visualization Techniques

2014 ◽  
Vol 15 (3) ◽  
pp. 227-232 ◽  
Author(s):  
Ali Soltani ◽  
Sajad Askari
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Kernel Estimation ◽  
Temporal Distribution ◽  
Spatiotemporal Analysis ◽  
Urban Traffic ◽  
High Rate ◽  
Spatiotemporal Visualization ◽  
Visualization Techniques

Abstract Road traffic accidents (RTAs) rank in the top ten causes of the global burden of disease and injury, and Iran has one of the highest road traffic mortality rates in the world. This paper presents a spatiotemporal analysis of intra-urban traffic accidents data in metropolitan Shiraz, Iran during the period 2011-2012. It is tried to identify the accident prone zones and sensitive hours using Geographic Information Systems (GIS)-based spatio-temporal visualization techniques. The analysis aimed at the identification of high-rate accident locations and safety deficient area using Kernel Estimation Density (KED) method. The investigation indicates that the majority of occurrences of traffic accidents were on the main roads, which play a meta-region functional role and act as a linkage between main destinations with high trip generation rate. According to the temporal distribution of car crashes, the peak of traffic accidents incident is simultaneous with the traffic congestion peak hours on arterial roads. The accident-prone locations are mostly located in districts with higher speed and traffic volume, therefore, they should be considered as the priority investigation locations to safety promotion programs.

scholarly journals Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzyc-Mean Clustering and Advanced Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
He Huang ◽  
Qifeng Tang ◽  
Zhen Liu
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Prediction Accuracy ◽  
Intelligent Transportation System ◽  
Prediction Models ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Flow Prediction ◽  
Adaptive Correction

Forecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzyc-mean clustering method (FCM) and advanced neural network (NN) was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

scholarly journals Measuring the Road Traffic Intensity using Neural Network with Computer Vision

2018 ◽  
Vol 10 (1) ◽  
pp. 184
Author(s):  
Muhammad Hamdan ◽  
Othman Omran Khalifah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Intensity ◽  
The Road ◽  
Surveillance Camera ◽  
On The Road ◽  
Alternative Routes

Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another program running a neural network classification which can classify and distinguish the vehicle type is on the road. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated.

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

scholarly journals Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinshuan Peng ◽  
Yiming Shao
Keyword(s):  
Neural Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Time Window ◽  
Driving Behavior ◽  
Practical Significance ◽  
Accurate Identification ◽  
Risky Driving ◽  
Bayesian Filter ◽  
Identification Rate

Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

scholarly journals Research on Decision Evaluation Model of HOV Lane Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ding Lv ◽  
Qunqi Wu ◽  
Bo Chen ◽  
Yahong Jiang
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Evaluation Model ◽  
Path Selection ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Travel Efficiency ◽  
Hov Lanes ◽  
The City

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

scholarly journals Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

2018 ◽  
Vol 30 (4) ◽  
pp. 407-417
Author(s):  
Yifan Sun ◽  
Jinglei Zhang ◽  
Xiaoyuan Wang ◽  
Zhangu Wang ◽  
Jie Yu
Keyword(s):  
Neural Network ◽  
Traffic Safety ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Physiological Data ◽  
Recognition Model ◽  
Driving Behaviors ◽  
Model Based ◽  
Safety Research

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF  neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and  driving and traffic safety maintenance.

Sign in / Sign up

Export Citation Format

Share Document