scholarly journals Analysis of the Effect of the Speed Factor on Highway Safety Using the Machine Learning Method

2021 ◽  
Vol 29 (4) ◽  
pp. 19-28
Author(s):  
Vahid Najafi Moghaddam Gilani ◽  
Milad Sashurpour ◽  
Sobhan Hassanjani ◽  
Seyed Mohsen Hosseinian
Keyword(s):  
Statistical Models ◽  
Logit Model ◽  
Goodness Of Fit ◽  
Highway Safety ◽  
Machine Learning Method ◽  
Independent Variables ◽  
Modeling Methods ◽  
Speed Factor ◽  
Rainy Weather ◽  
Sunny Weather

Abstract Speed is one of the most important factors that can significantly change the severity of accidents. Providing a model with predictive factors leads to designing traffic plans to promote safety. This study aims to create statistical models for accidents occurred on Firuzkuh highway, Iran. Moreover, the probability of each type of accident was determined using the logit model. Various modeling methods, such as backward, forward, and entering methods, were evaluated to find the best method. Finally, since the backward method had the best performance in terms of R2 and goodness of fit, the logit model of accidents was created. According to the model, the independent variables of the 12-24 hours, rainy weather, a speed of 81-95 and 96-110 km/h, the lack of attention ahead and the Pride brand of vehicle increased the severity of accidents, while the variables with negative coefficients of Tuesdays, the summer and spring seasons, sunny weather, a male driver, and daylight, reduced the severity of accidents.

scholarly journals Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Meisam Ghasedi ◽  
Maryam Sarfjoo ◽  
Iraj Bargegol
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Logit Model ◽  
Predictive Power ◽  
Dominant Role ◽  
Learning Approaches ◽  
Lighting Condition ◽  
Rainy Weather ◽  
Pedestrian Accidents ◽  
Effective Principal

AbstractThe purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

scholarly journals On the Accuracy of the Sine Power Lomax Model for Data Fitting

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 78-104
Author(s):  
Vasili B. V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Statistical Model ◽  
Simulation Study ◽  
Statistical Models ◽  
Goodness Of Fit ◽  
Data Fitting ◽  
Applied Science ◽  
Survival Distribution ◽  
Lomax Distribution ◽  
Model Based

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

Analysing pedestrian–vehicle conflicts behaviour at urban pedestrian crossings

2021 ◽  
pp. 1-25
Author(s):  
Babak Mirbaha
Keyword(s):  
Risk Taking ◽  
Logit Model ◽  
Video Recording ◽  
Elasticity Analysis ◽  
Gap Acceptance ◽  
Time To Collision ◽  
Binary Logit Model ◽  
Traffic Conditions ◽  
Rainy Weather ◽  
Binary Logit

Pedestrian safety has become a serious problem with the rapid growth of motorised vehicle in transportation system in developing counties. Pedestrians often respond differently to changes in surrounding and traffic conditions. A study was undertaken to investigate pedestrians’ gap acceptance and the parameters affecting their risk-taking behaviours based on time-to-collision and post-encroachment-time indexes. Three signalised intersections and two midblock crossings were selected in Qazvin, Iran. A total of 752 pedestrians were examined by video recording and field observation, and pedestrians’ gap acceptance behaviour was estimated by using binary logit model. Results showed that the average time to collision and post-encroachment time were 4.27 s and 1.44 s, respectively. In addition, the presence of children alongside the older pedestrians led to a less risk-taking crossing. Additionally, pedestrian risk-taking was reduced by increasing both time indexes. Rainy weather also reduced pedestrians’ risk-taking behaviour. Elasticity analysis indicated that parameters such as pedestrians’ conflict with vehicles at the first or second half of the crossings, walking with a child, speed of the approaching vehicle, the crossing type and running while crossing were the most important factors in pedestrian risk-taking.

scholarly journals Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

2019 ◽  
Vol 11 (18) ◽  
pp. 5003
Author(s):  
Li ◽  
Dai ◽  
Zhu ◽  
Liu
Keyword(s):  
Human Activities ◽  
Central Area ◽  
Trajectory Data ◽  
Transit Systems ◽  
Temporal Characteristics ◽  
Rainy Weather ◽  
Share Data ◽  
Gps Trajectory Data ◽  
Sunny Weather ◽  
Policy Proposals

Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.

scholarly journals Relative importance analysis with multivariate models: Shifting the focus from independent variables to parameter estimates

2020 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Joseph N. Luchman ◽  
Xue Lei ◽  
Seth Kaplan
Keyword(s):  
Statistical Models ◽  
Likelihood Estimation ◽  
Parameter Estimate ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Relative Importance ◽  
Multivariate Models ◽  
Independent Variables ◽  
Dependent Variables ◽  
Independent Variable

Conclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limited set of multivariate models. To accommodate multivariate models, the current work proposes shifting away from the concept of independent variable relative importance toward that of parameter estimate relative importance (PERI). This paper illustrates the PERI approach by comparing it to the evaluation of regression slopes and independent variable relative importance (IVRI) statistics to show the interpretive and methodological advantages of the new concept and associated methods. PERI’s advantages above standardized slopes stem from the same fit metric that is used to compute PERI statistics; this makes them more comparable to one another than standardized slopes. PERI’s advantages over IVRI stem from situations where independent variables do not predict all dependent variables; hence, PERI permits importance determination in situations where independent variables are nested in dependent variables they predict. We also provide recommendations for implementing PERI using dominance analysis with statistical models that can be estimated with maximum likelihood estimation combined with a series of model constraints using two examples.

scholarly journals Attitude Towards Zakah on Employment Income : Comparing Outcomes Between Single Score and Multidimensional Scores

2020 ◽  
Author(s):  
Kamil Md Idris ◽  
Ahmad Mahdzan Ayob
Keyword(s):  
Principal Component Analysis ◽  
Goodness Of Fit ◽  
Principal Component ◽  
Complex Object ◽  
Factor Scores ◽  
Multivariate Logistic Regression ◽  
Independent Variables ◽  
Compliance Behavior ◽  
Single Score ◽  
The Relationship

Study on attitude towards regulated social activities have been carried out in many areas (such as tax and zakah payment). However, many of these studies applied a single score of attitude in their analyses. Such a procedure, to some researchers is considered less informative, especially in the study of a complex attitude which has several dimensions. Many researchers have suggested that attitude towards a complex object should be studied by decomposing the object or issue into smaller and less complex elements on the basis of component parts, specific functions, or particular contexts. Thus, this paper offers a comparative study of outcomes between attitude measured by a single summative score and attitude measured by multidimensional factor scores. The object of attitude in this paper is zakah on employment income by eligible Muslim. In the first approach, a total of 24 items of attitude were used to represent the single score of attitude. In the second approach, principal component analysis with varimax ratation was first applied to determine the underlying dimensions of attitude. Each dimension was then named and treated as anew variable, each measured by the factor scores. Both approach were applied separately to an analysis on compliance behavior of zakah on employment income. Results suggest that attitude measured by multidimensionality scores is more informative as compared to the single summative score. Futher, the use of multidimensional scores in multivariate logistic regression improved the goodness of fit of the model over that of the single score of attitude. Thus, this improvement affects the interpretation of the whole model with respect to the relationship between the independent variables and the dependent variable, which is zakah compliance.  

scholarly journals Floral Phenology and Pollination Ecology of Punica granatum L. in Kathmandu, Nepal

1970 ◽  
Vol 11 ◽  
pp. 115-124 ◽  
Author(s):  
Subodh Adhikari ◽  
Mahesh Kumar Adhikari
Keyword(s):  
Ecological Study ◽  
Punica Granatum ◽  
Normal Range ◽  
Open Flower ◽  
Flower Visit ◽  
Floral Phenology ◽  
Mature Flower ◽  
Rainy Weather ◽  
Sunny Weather ◽  
Punica Granatum L

The phenological and ecological study on Punica granatum L., a cultivated and wild species found in outer Himalayan ranges and warm inner valleys (alt. 700 - 2700m), was carried out during April and May of 2006 and 2007 in Kathmandu Valley. The study covered blooming time, size of flower, its correlation and interaction with the visitors and pollinators. The prime pollinator was Apis cerena along with A. mellifera. Normal range of the length of a full blooming flower (mature flower) was 4.1 to 4.7 cm (in bagged flower) and 3.8 to 4.9 cm (in open flower). The fruiting rate was higher in case of the open flowers than the bagged one. Visitor's/pollinator's flower visit rate (visits/time) was found higher (most effective) in morning with sunny weather (766 times out of 1365). Similarly, the least effective time was dawn and dusk with cloudy and rainy weather (2 times each out of 1365). Key words: floral phenology; visitors/pollinators DOI: 10.3126/njst.v11i0.4133Nepal Journal of Science and Technology 11 (2010) 115-124

scholarly journals Weighted Variables Using Best-Worst Scaling in Ordered Logit Models for Public Transit Satisfaction

2020 ◽  
Vol 12 (13) ◽  
pp. 5318
Author(s):  
Iván Manuel Mendoza-Arango ◽  
Eneko Echaniz ◽  
Luigi dell’Olio ◽  
Eduardo Gutiérrez-González
Keyword(s):  
Logit Model ◽  
Multinomial Logit Model ◽  
Ordered Logit ◽  
Worst Case ◽  
Independent Variables ◽  
Public Transport System ◽  
Ordered Logit Model ◽  
Specific Attribute ◽  
Best Worst Scaling ◽  
Level Of Importance

Customer overall satisfaction regarding a public transport system is dependent on the satisfaction of the users with the attributes that make up the service, as well as the contribution that each of these attributes makes to explain the overall satisfaction. A common way of analysing the contribution of service attributes to explain overall satisfaction is through the use of ordered logit or probit models. This article presents an ordered logit model that considers the weighting of independent variables through the explicit importance calculated on the basis of a best-worst case 1 choice task. For the calculation of importance, a multinomial logit model has been estimated which considers the heterogeneity of the sample through systematic variations in user tastes. In this way, it is possible to establish a level of importance of each specific attribute for each type of user. The results show that the importance varies considerably depending on different socio-economic and mobility-base variables. On the other hand, the inclusion of the weighted variables in the ordered logit model improves its fit. Therefore, the results make possible to develop policies focused on improving satisfaction on specific user targets.

Modélisation des erreurs pour le système de prévision PRÉVIS

2004 ◽  
Vol 31 (5) ◽  
pp. 892-897 ◽  
Author(s):  
Mario Lefebvre
Keyword(s):  
Statistical Models ◽  
Goodness Of Fit ◽  
Pareto Distribution ◽  
Laplace Distribution ◽  
Critical Threshold ◽  
Gaussian Distributions ◽  
Goodness Of Fit Tests ◽  
Generalized Pareto ◽  
Forecasting System

This paper examines models for the errors in forecasts of river and (or) watershed flows produced by the PREVIS forecasting system, which is used by Alcan, among other companies. We analyzed the following statistical models: generalized Pareto, Laplace, and Gaussian distributions, depending on the flow value forecasted by PREVIS. These models enable us to quantify the precision of the forecasts produced by PREVIS, as well as the risk of seeing the flow exceed a certain critical threshold, given the forecasted flow.Key words: modeling, Laplace distribution, Pareto distribution, goodness-of-fit tests, critical threshold.

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