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Optimal location (OL) queries are a type of spatial queries particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries,...
In this paper the feasibility of artificial neural network technology for air fine particles pollution prediction of main traffic route was discussed. The concentration data of PM2.5, PM5 and PM10 were measured in Zhongshan road, the main traffic route of Chongqing, China. Parameter Φ of emission capacity of motor vehicles was used as the independent variable of prediction model. RBF and BP neural...
In the view of the worsening congestion situation of freeway and related urban expressway. Based on fuzzy metering and neural network theories. A fuzzy control method which chooses d-value of mainline traffic state and expected state and ramp traffic state as input variables, ramp metering rate as output variable was raised. accordingly a ramp metering algorithm was put forward to establish a five-layer...
Established the computational model about the safe distance of vehicles. In order to simulate the dynamic model of rear-end, based on VB software to build a freeway rear-end simulation system. Simulation system provides an important means for in-depth study on rear-end probability. To investigate the non-linear relationship of probability and impact factors of rear-end, established probability of...
Freeway congestion problem can be addressed employing many different measures. Ramp metering is the most widely used control measure, and is an efficient way to control and upgrade freeway traffic by regulating the number of vehicles entering the freeway. This paper proposes an iterative learning approach for the freeway density control under ramp metering in a macroscopic level traffic environment...
Road traffic accident prediction is to provide traffic safety change trend for road safety administrator institution.Based on the analysis of road traffic accident,a set of RBF neural network model which are applied to forecast traffic accident are presented.The RBF neural network model used to predict and extrapolate the number of death and economy lose of 2000~2006.The result shows the forecasting...
The Full Velocity Difference Model of traffic flow is extended by taking into account the leading acceleration. The method of linear stability analysis is applied to the new car following model, which shows the stability range of the improved model is obviously larger than FVD and FVAD model. Numerical simulation shows that the extended model can improve the traffic flow by reducing traffic jam.
To simulate the human decision-making in steering to follow a planned path, we modeled the driver's learning process by using an idea of target position. The proposed model dealt with the driver's decision-making mechanisms in tracking a target position by means of Cloud Neural Network. Results from the simulation of lane changing showed the capability of the proposed model to simulate a driver's...
This paper presents a classifier based on a net neural MLP (Multiple Layers Perceptron) using the algorithm of Levenberg-Marquardt to do the identification of the load of vehicles through the magnetic profile collected in equipment of control of traffic.
In order to study the impact of drivers' distance cognition difference on traffic safety in dynamic environment of daytime and night-time, a real road tests was carried out by asking 19 drivers randomly selected to percept the distances of obstacles with different distances and velocities on daytime and nighttime. The values of cognition are obtained by statistical methods. The distance cognition...
Traffic accident records data mining is very important to understand why traffic accidents occurred frequently under some driving, environment, and vehicle conditions. There are many reasons can lead to accident, and their relationships are complex, it is very difficult to build a correct evaluation model. To overcome this problem, statistical models such as neural network, fuzzy logic, decision tree...
In order to improve the Vehicle Weigh in Motion System with precision, the paper introduces Nerve Net Algorithm to error analysis. The article sets up a neural network model by determining the neural network input and output variables. Then, the function is defined by net training on MATLAB software. Finally through the experimental verification of Nerve Net Algorithm improving Weigh in Motion System...
Traffic signal is a measure of traffic command, which distributes right of way to the traffic flow in time domain. Through comparative study between RBF network and BP network, this paper has selected RBF network to control the traffic single. Green Light Interval is decided by the measurement parameters of detector in the actuated signal control. In the usually, the time phase number of the system...
Short-term prediction of intelligent traffic flow is in favor of road unblocked and vehicle waiting strategy. The algorithm of short-term prediction of intelligent traffic flow based on back propagation(BP) neural network and autoregressive integrated moving average (ARIMA) model can solve it partially. Firstly, Establishing a BP neural network sub-model and ARIMA sub-model, Then taking BP neural...
This paper focuses on traffic flow forecasting approach based on soft computing tools. The soft computing tools used is Particle Swarm Optimization (PSO) with Wavelet Network Model(WNM). The forecast of short-term traffic flow in timely and accurate is one of important contents of intelligent transportation system research. The modelling of traffic characteristics and the prediction of future traffic...
Vehicle Class is an important parameter in road traffic measurement. In this paper authors developed an algorithm to find the accuracy of the system for vehicle classification using different techniques. The algorithm mainly reads the inference system and applies various input samples, check the class of each sample and calculate the accuracy. The class is identified by checking the wheel base, ground...
Through comprehensive analysis to urban traffic system, it raises an evaluation system. The theoretical frame of ecological traffic evaluation is presented. On the basis of setting up grade evaluation, it adopts membership function and gets quantized evaluation index of urban ecological traffic. Because performance of the common measure that can't be common between different indices, it utilize matter...
In order to promote the use of simulation in the traffic navigation and management, the behaviors and existing simulation models of the moving objects (such as micro-cellular automaton model, Poisson distribution of microscopic traffic simulation model and section start.) in traffic flow have been researched, integrated and improved in this paper, and provides the Road Network-Based Cellular Automata...
With rapid development of modern intelligent traffic management, the measuring speed radar becomes an important device. Using Doppler principle, speed can be measured by obtaining frequency deviation signal of moving target. Traditional measuring speed method uses analog filter whose measuring accuracy is low and measuring speed is slow, so it's difficult to meet the requirements of modern intelligent...
A novel method utilizing state space neural network (SSNN) with adaptive filters is proposed to estimate the traffic flow parameters. The SSNN's network topology is derived from delays and stops estimation problem, so the design of SSNN reflects the relationships that exist in physical traffic systems. To improve SSNN effectiveness, the adaptive filters is proposed to train the SSNN instead of conventional...
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