Development of Accident Prediction Model for High Speed Corridors in India

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

Download Full-text

Research on Surface Roughness Prediction Model for High-Speed Milling Inclined Plane of Hardened Steel

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.2044 ◽

2010 ◽

Vol 97-101 ◽

pp. 2044-2048 ◽

Cited By ~ 1

Author(s):

Yuan Ling Chen ◽

Bao Lei Zhang ◽

Wei Ren Long ◽

Hua Xu

Keyword(s):

Surface Roughness ◽

Prediction Model ◽

High Speed ◽

Hardened Steel ◽

Prediction Errors ◽

Inclined Plane ◽

Workpiece Surface ◽

Factors Influencing ◽

Generalized Regression Neural Networks ◽

Roughness Prediction

As the factors influencing the workpiece surface roughness is complexity and uncertainty, according to orthogonal experimental results, the paper established Empirical regression prediction model and generalized regression neural networks (GRNN) for prediction of surface roughness when machining inclined plane of hardened steel in high speed , moreover, compared their prediction errors. The results show that GRNN model has better prediction accuracy than empirical regression prediction model and can be better used to control the surface roughness dynamically.

Download Full-text

Research of High-Speed Railway Running Economic Benefit Based on Gray Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1273 ◽

2012 ◽

Vol 253-255 ◽

pp. 1273-1277

Author(s):

Xue Dong Du ◽

Na Ren

Keyword(s):

Data Analysis ◽

Prediction Model ◽

Economic Benefit ◽

High Speed ◽

Accurate Prediction ◽

Gray Model ◽

Practical Application ◽

High Speed Railway ◽

Railway Stations ◽

Train Operation

The research of high-speed railway running economic benefit is important to timely know well the train operation state for the railway administration. A prediction model of high-speed railway running economic benefit is proposed in this article based on Gray model. The Gray model is a good example to make accurate prediction of the development of matters. According to the data analysis of Beijing and Shanghai railway stations, we can know that the result of prediction model is accurate, so the prediction based on Gray model is scientific and reasonable in the practical application.

Download Full-text

Prediction Model and Verification of Wheel Wear in High-speed Trains

Journal of Mechanical Engineering ◽

10.3901/jme.2016.02.144 ◽

2016 ◽

Vol 52 (2) ◽

pp. 144 ◽

Cited By ~ 2

Author(s):

Peng HAN

Keyword(s):

Prediction Model ◽

High Speed ◽

Wheel Wear ◽

High Speed Trains

Download Full-text

Predicting the Performance of High-Speed Railway Bridge Using Regression Neural Network Approach

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.873.220 ◽

2017 ◽

Vol 873 ◽

pp. 220-224 ◽

Cited By ~ 1

Author(s):

Young Chan Kim ◽

Mosbeh R. Kaloop ◽

Jong Wan Hu

Keyword(s):

Neural Network ◽

Prediction Model ◽

High Speed ◽

Short Term ◽

Good Tool ◽

Neural Network Approach ◽

High Speed Railway ◽

Railway Bridges ◽

Bridge Girder ◽

Artificial Neural Network Ann

The performance prediction of High-speed railway bridges (HSRB) is vital to detect the behavior of bridges under different train’s speeds. This study aims to design a prediction model using the artificial neural network (ANN) to assess the performance of Yonjung high-speed bridge. A short-term health monitoring system is used to collect the behavior of bridge with different high-speed train’s speeds. The statistical analysis is utilized to evaluate the bridge under speeds 165 to 403 Km/h. The evaluation of bridge and prediction model showing that the bridge is safe, and the ANN is shown a good tool can be used to estimate a prediction model for the displacement of bridge girder.

Download Full-text