scholarly journals The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
Yichuan Peng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Variation Coefficient ◽  
Support Vector ◽  
Safety Measures ◽  
Speed Variation ◽  
Surrogate Safety Measures ◽  
The Impact ◽  
The Relationship

The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

scholarly journals A machine learning-based study of the impact of traffic flow changes on air pollution combined with meteorological conditions: the case of Oslo

2021 ◽  
Author(s):  
Cong Cao
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Support Vector Machine ◽  
Traffic Flow ◽  
Arrival Time ◽  
Meteorological Conditions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Traffic Volumes ◽  
The Impact

In this paper, we explore the impact of changes in traffic flow on local air pollution under specific meteorological conditions by integrating hourly traffic flow data, air pollution data and meteorological data, using generalized linear regression models and advanced machine learning algorithms: support vector machines and decision trees. The geographical location is Oslo, the capital of Norway, and the time we selected is from February 2020 to September 2020; We also selected 24-hour data for May 11 and 16 of the same year, representing weekday and holiday traffic flow, respectively, as a subset to further explore. Finally, we selected data from July 2020 for robustness testing, and algorithm performance verification.We found that: the maximum traffic flow on holidays is significantly higher than that on weekdays, but the holidays produce less concentration of {NO}_x throughout the month; the peak arrival time of {NO}_x,\ {NO}_2and NO concentrations is later than the peak arrival time of traffic flow. Among them, {NO}_x has a very significant variation, so we choose {NO}_x concentration as an air pollution indicator to measure the effect of traffic flow variation on air pollution; we also find that {NO}_xconcentration is negatively correlated with hourly precipitation, and the variation trend is like that of minimum air temperature. We used multiple imputation methods to interpolate the missing values. The decision tree results yield that when traffic volumes are high (>81%), low temperatures generate more concentrations of {NO}_x than high temperatures (an increase of 3.1%). Higher concentrations of {NO}_x (2.4%) are also generated when traffic volumes are low (no less than 22%) but there is some precipitation ≥ 0.27%.In the evaluation of the prediction accuracy of the machine learning algorithms, the support vector machine has the best prediction performance with high R-squared and small MAE, MSE and RMSE, indicating that the support vector machine has a better explanation for air pollution caused by traffic flow, while the decision tree is the second best, and the generalized linear regression model is the worst.The selected data for July 2020 obtained results consistent with the overall dataset.

Traffic Safety Evaluation of Uncontrolled Intersections using Surrogate Safety Measures under Mixed Traffic Conditions

2015 ◽  
Vol 2512 (1) ◽  
pp. 81-89 ◽  
Author(s):  
P. Vedagiri ◽  
Deepak V. Killi
Keyword(s):  
Traffic Safety ◽  
Safety Evaluation ◽  
Mixed Traffic ◽  
Safety Measures ◽  
Traffic Conditions ◽  
Management Measures ◽  
Large Populations ◽  
Accident Data ◽  
Surrogate Safety Measures ◽  
The Impact

In the developing world, with increases in population, the number of vehicles is increasing tremendously. Traffic safety on roads has become a major concern even with advancements in technology and infrastructure. Traffic safety assessments and accident prediction based on accident data is a reactive approach. There are known drawbacks related to the reliability of accident data, especially in developing countries with large populations, such as India. It is, however, unethical to wait for accidents to occur before drawing statistically accurate conclusions regarding safety impacts. To overcome this impediment, one needs to develop accurate models that rely on surrogate safety measures (SSMs) for effective safety evaluations. The main advantage associated with the use of these models is that they can model crashes more frequently than in the real world and thereby imply an efficient and more statistically reliable proximal measure of traffic safety. The objective of this study is to examine the impact of management measures on traffic safety at a three-arm uncontrolled intersection with the use of microsimulation modeling under mixed traffic conditions. This examination was done by developing a unique methodology of measuring one SSM, postencroachment time (PET). This paper describes improvement in the accuracy of crash predictions by proposing a methodology to calculate PET.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

scholarly journals Prediction of preload attenuation of ball screw based on support vector machine

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879916 ◽  
Author(s):  
Kai Chen ◽  
Li Zu ◽  
Li Wang
Keyword(s):  
Support Vector Machine ◽  
Working Condition ◽  
Friction Torque ◽  
Ball Screw ◽  
Support Vector ◽  
Theoretical Derivation ◽  
Mechanical Field ◽  
Changing Trend ◽  
Engineering Significance ◽  
The Relationship

Ball screw is a mechanical device widely used in mechanical field. The reverse clearance of ball screw will reduce its precision. In order to eliminate the reverse clearance, it is necessary to apply preload to the ball screw. It is very difficult to measure the preload in real time, and the data are large and time-consuming. By using machine learning method to predict and supervise preload, the changing trend of working condition of ball screw can be evaluated in advance, and the working precision of screw is controlled, which has important engineering significance. In this article, the relationship between the preload and the friction torque is obtained through theoretical derivation and experimental verification. Then, the support vector machine is used as a tool to model the friction torque of ball screw with the parameters of material, lubrication, and revolution, and predict the value and trend of preload to complete the supervision and prediction of the preload of the ball screw. By comparing the experimental results, it is proved that the support vector machine is feasible in predicting and supervising the attenuation of the preload of ball screw.

scholarly journals Understanding nitrogen transport in the unsaturated zone with fluctuations in groundwater depth

2021 ◽  
Author(s):  
Jianmin Bian ◽  
Qian Wang ◽  
Siyu Nie ◽  
Hanli Wan ◽  
Juanjuan Wu
Keyword(s):  
Support Vector Machine ◽  
Unsaturated Zone ◽  
Prediction Models ◽  
Groundwater Depth ◽  
Support Vector ◽  
Nitrogen Transport ◽  
Irrigation Area ◽  
Leaching Loss ◽  
The Relationship ◽  
Hydrus 1D

Abstract Fluctuations in groundwater depth play an important role and are often overlooked when considering the transport of nitrogen in the unsaturated zone. To evaluate directly the variation of nitrogen transport due to fluctuations in groundwater depth, the prediction model of groundwater depth and nitrogen transport were combined and applied by least squares support vector machine and Hydrus-1D in the western irrigation area of Jilin in China. The calibration and testing results showed the prediction models were reliable. Considering different groundwater depth, the concentration of nitrogen was affected significantly with a groundwater depth of 3.42–1.71 m, while it was not affected with groundwater depth of 5.48–6.47 m. The total leaching loss of nitrogen gradually increased with the continuous decrease of groundwater depth. Furthermore, the limited groundwater depth of 1.7 m was found to reduce the risk of nitrogen pollution. This paper systematically analyzes the relationship between groundwater depth and nitrogen transport to form appropriate agriculture strategies.

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