scholarly journals INVESTIGATING FACTORS AFFECTING ROAD FREIGHT OVERLOADING THROUGH THE INTEGRATED USE OF BLR AND CART: A CASE STUDY IN CHINA

Transport ◽  
2020 ◽  
Vol 35 (3) ◽  
pp. 236-246
Author(s):  
Yikai Chen ◽  
Kai Wang ◽  
Yu Zhang ◽  
Renjia Luo ◽  
Shujun Yu ◽  
...  
Keyword(s):  
Operation Mode ◽  
Regression Tree ◽  
Binary Logistic Regression ◽  
Statistical Modelling ◽  
Road Transport ◽  
Classification And Regression Tree ◽  
Factors Affecting ◽  
Road Freight ◽  
Efficiency And Effectiveness ◽  
Freight Vehicles

Overloading of road freight vehicles accelerates road damage, creates unfair competition in the transport market, and increases safety risk. There is a dearth of research on the mining of data of highway Freight Weight (FW), and this paper therefore aims to discover factors affecting road freight overloading based on highway FW data, with a view of developing strategies to mitigate such occurrences. A comprehensive sampling survey of road freight transportation was conducted in Anhui Province (China). Vehicle Characteristics (VC), Operation Mode (OM), and transportation information from a total of 3248 trucks were collected. In order to take advantage of the strengths associated with both statistical modelling techniques and non-parametric methods, a Classification And Regression Tree (CART) technique was integrated with Binary Logistic Regression (BLR) to reveal the factors affecting road freight overloading. The classification efficacy test shows that the BLR–CART method outperformed the BLR method in term of accuracy. It is also revealed that the factors affecting overloading of freight vehicles are the Type of Transportation (ToT), Rated Load (RL), OM, FW during the investigation period, interaction between RL and FW, and interaction among RL, FW, and Average Haul Distance (AHD). Road transport authorities should pay greater attention to these factors in order to improve efficiency and effectiveness of overloading inspection.

scholarly journals Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment

2019 ◽  
Vol 11 (5) ◽  
pp. 1327 ◽  
Author(s):  
Bei Zhou ◽  
Zongzhi Li ◽  
Shengrui Zhang ◽  
Xinfen Zhang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Geometric Mean ◽  
Regression Tree ◽  
Classification Performance ◽  
Classification And Regression Tree ◽  
Factors Affecting ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Hit And Run ◽  
Reported Data ◽  
Cart Model

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

scholarly journals Mental State of Central Sterile Supply Department Staff During COVID-19 Epidemic and CART Analysis

2020 ◽  
Author(s):  
Wei Pan ◽  
Juan Hu ◽  
Liangying Yi
Keyword(s):  
Mental State ◽  
Factor Score ◽  
Measurement Data ◽  
Regression Tree ◽  
General Information ◽  
Classification And Regression Tree ◽  
Psychological Resilience ◽  
Chi Square ◽  
Factors Affecting ◽  
Cart Analysis

Abstract Background: During COVID-19 epidemic, the central sterile supply department (CSSD) staff need to handle a large number of devices, utensils and non-disposable protective articles used by suspected or confirmed COVID-19 patients. This may bring psychological stress to the CSSD staff. However, the mental state of the CSSD staff during COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and the relevant influencing factors during COVID-19 epidemic.Methods: Conduct the questionnaire survey with the general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) among 423 CSSD staff from 35 hospitals in Sichuan Province, China. Analyse the data in SPSS 24.0, use classification and regression tree (CART) to analyse variables, and find variation between groups. Perform chi-square test on enumeration data, and perform t-test and analysis of variance on measurement data.Results: The CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = -0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = -0.477, P < 0.01). The job, age and political status of the CSSD staff were the main factors affecting their mental state. The CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01). Conclusion: During the epidemic, the CSSD staff’s psychological resilience was at a low level, and the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention shall be paid to the mental health of the CSSD staff, and it is necessary to take the protective measures regarding the risk factors at work to ensure they can maintain a good mental state during the epidemic.

scholarly journals KEY FACTORS AFFECTING RAIL SERVICE QUALITY IN THE NORTHERN ITALY: A DECISION TREE APPROACH

Transport ◽  
2014 ◽  
Vol 29 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Rocío De Oña ◽  
Laura Eboli ◽  
Gabriella Mazzulla
Keyword(s):  
Service Quality ◽  
Regression Tree ◽  
Northern Italy ◽  
Classification And Regression Tree ◽  
Key Factors ◽  
Factors Affecting ◽  
Service Characteristics ◽  
Transit Services ◽  
The City ◽  
Tree Approach

This work concerns with the analysis of transit service quality on the basis of the perceptions directly expressed by the passengers of the services. The transit services supporting the research are offered by rail operators of the Northern Italy, and particularly by regional and suburban lines connecting different towns of the hinterland of the city of Milan, and express lines connecting Milan with the Malpensa airport. The experimental data were collected in a survey conducted in May 2012, and addressed to a sample of more than 16,000 passengers. Passengers expressed their opinions about service characteristics such as safety, cleanliness, comfort, information, personnel. The tool chosen for evaluating service quality is a Classification and Regression Tree Approach (CART), useful for identifying the characteristics mostly influencing the overall service quality. We found that service characteristics like ‘Windows and Doors Working’, ‘Courtesy and Competence on Board’, ‘Information at Stations’, ‘Punctuality of Runs’, ‘Courtesy and Competence in Station’ and ‘Regularity of Runs’ mainly influence service quality.

scholarly journals Tree-Based Models for Predicting Mortality in Gram-Negative Bacteremia: Avoid Putting the CART before the Horse

2015 ◽  
Vol 60 (2) ◽  
pp. 838-844 ◽  
Author(s):  
Nathaniel J. Rhodes ◽  
J. Nicholas O'Donnell ◽  
Bryan D. Lizza ◽  
Milena M. McLaughlin ◽  
John S. Esterly ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Regression Tree ◽  
Binary Logistic Regression ◽  
Predictive Performance ◽  
Threshold Value ◽  
Apache Ii ◽  
Classification And Regression Tree ◽  
Apache Ii Score ◽  
Receiver Operating Characteristic Curves ◽  
Gram Negative Bacteremia

ABSTRACTIncreasingly, infectious disease studies employ tree-based approaches, e.g., classification and regression tree modeling, to identify clinical thresholds. We present tree-based-model-derived thresholds along with their measures of uncertainty. We explored individual and pooled clinical cohorts of bacteremic patients to identify modified acute physiology and chronic health evaluation (II) (m-APACHE-II) score mortality thresholds using a tree-based approach. Predictive performance measures for each candidate threshold were calculated. Candidate thresholds were examined according to binary logistic regression probabilities of the primary outcome, correct classification predictive matrices, and receiver operating characteristic curves. Three individual cohorts comprising a total of 235 patients were studied. Within the pooled cohort, the mean (± standard deviation) m-APACHE-II score was 13.6 ± 5.3, with an in-hospital mortality of 16.6%. The probability of death was greater at higher m-APACHE II scores in only one of three cohorts (odds ratio for cohort 1 [OR1] = 1.15, 95% confidence interval [CI] = 0.99 to 1.34; OR2= 1.04, 95% CI = 0.94 to 1.16; OR3= 1.18, 95% CI = 1.02 to 1.38) and was greater at higher scores within the pooled cohort (OR4= 1.11, 95% CI = 1.04 to 1.19). In contrast, tree-based models overcame power constraints and identified m-APACHE-II thresholds for mortality in two of three cohorts (P= 0.02, 0.1, and 0.008) and the pooled cohort (P= 0.001). Predictive performance at each threshold was highly variable among cohorts. The selection of any one predictive threshold value resulted in fixed sensitivity and specificity. Tree-based models increased power and identified threshold values from continuous predictor variables; however, sample size and data distributions influenced the identified thresholds. The provision of predictive matrices or graphical displays of predicted probabilities within infectious disease studies can improve the interpretation of tree-based model-derived thresholds.

scholarly journals Occupational Health and Job Satisfaction Assessment of Bus Rapid Transit (BRT) Drivers

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Alireza Samerei ◽  
Kayvan Aghabayk ◽  
Alireza Soltani
Keyword(s):  
Mental Health ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Practical Application ◽  
Bus Drivers ◽  
Factors Affecting ◽  
Urban Bus

Several studies have focused on ergonomics of commercial and urban bus drivers; however, there exists a dearth of research on BRT drivers. This study was conducted to investigate the factors affecting the BRT drivers' mental health and satisfaction. The study was carried out on 171 BRT drivers in Tehran, Iran. The required data were collected through two questionnaires. The Classification and Regression Tree (CART) and Hierarchical clustering (HC) was used to extract factors affecting mental health and satisfaction of BRT drivers. The important factors affecting BRT drivers' mental health were: dispute with passengers, depression, BMI, criminal behaviours of passengers, driver's retirement conditions, driver's family conditions, fatigue and the rostering. In addition, the most important factors affecting driver satisfaction were: bus repairs, driver's seat and the sound inside the cabin. Possible practical application includes: creating a counseling and psychotherapy unit and improving the quality of buses and repairment.

scholarly journals Mental State of Central Sterile Supply Department Staff During COVID-19 Epidemic and CART Analysis 

2020 ◽  
Author(s):  
Wei Pan ◽  
Juan Hu ◽  
Liangying Yi
Keyword(s):  
Mental State ◽  
Factor Score ◽  
Measurement Data ◽  
Regression Tree ◽  
General Information ◽  
Classification And Regression Tree ◽  
Psychological Resilience ◽  
Chi Square ◽  
Factors Affecting ◽  
Cart Analysis

Abstract Background: During the COVID-19 epidemic, the central sterile supply department (CSSD) staff handled many devices, implements and non-disposable protective articles used by suspected or confirmed COVID-19 patients. As a result, the CSSD staff may have experienced psychological stress, however, the mental state of the CSSD staff during the COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and relevant influencing factors experienced during the COVID-19 epidemic.Methods: The survey utilising a general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) was conducted with 423 CSSD staff members from 35 hospitals in Sichuan Province, China. Data was analysed in SPSS24.0. Classification and regression tree (CART) was utilised to analyse variables and find variation between groups. A chi-square test was performed on enumeration data, and t-test and analysis of variance were performed on measurement data.Results: The CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = -0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = -0.477, P < 0.01). The job position, age, and political status of the CSSD staff were the main factors affecting their mental state; for example, the CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01). Conclusion: During the epidemic, the CSSD staff’s psychological resilience was at a low level; the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention should be given to the mental health of the CSSD staff, including taking protective measures regarding the risk factors to ensure they can maintain a healthy mental state.

scholarly journals Mental state of central sterile supply department staff during COVID-19 epidemic and CART analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wei Pan ◽  
Juan Hu ◽  
Liangying Yi
Keyword(s):  
Mental State ◽  
Factor Score ◽  
Measurement Data ◽  
Regression Tree ◽  
General Information ◽  
Classification And Regression Tree ◽  
Psychological Resilience ◽  
Chi Square ◽  
Factors Affecting ◽  
Cart Analysis

Abstract Background During the COVID-19 epidemic, the central sterile supply department (CSSD) staff handled many devices, implements and non-disposable protective articles used by suspected or confirmed COVID-19 patients. As a result, the CSSD staff may have experienced psychological stress, however, the mental state of the CSSD staff during the COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and relevant influencing factors experienced during the COVID-19 epidemic. Methods The survey utilising a general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) was conducted with 423 CSSD staff members from 35 hospitals in Sichuan Province, China. Data was analysed in SPSS24.0. Classification and regression tree (CART) was utilised to analyse variables and find variation between groups. A chi-square test was performed on enumeration data, and t-test and analysis of variance were performed on measurement data. Results The CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = − 0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = − 0.477, P < 0.01). The job position, age, and political status of the CSSD staff were the main factors affecting their mental state; for example, the CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01). Conclusion During the epidemic, the CSSD staff’s psychological resilience was at a low level; the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention should be given to the mental health of the CSSD staff, including taking protective measures regarding the risk factors to ensure they can maintain a healthy mental state.

scholarly journals Consumers’ Intention to Purchase Electric Vehicles: Influences of User Attitude and Perception

2021 ◽  
Vol 13 (12) ◽  
pp. 6778
Author(s):  
Zulfiqar Ali Lashari ◽  
Joonho Ko ◽  
Junseok Jang
Keyword(s):  
Electric Vehicles ◽  
Effective Diffusion ◽  
Regression Tree ◽  
Binary Logistic Regression ◽  
Marketing Strategies ◽  
Transport Systems ◽  
Factors Affecting ◽  
Intention To Purchase ◽  
Negative Impacts ◽  
Survey Responses

Electric vehicles (EVs) have been developed as an efficient solution to reduce automobile emissions. To ensure the effective diffusion of EVs in current transport systems, it is vital to understand the factors affecting consumers’ intentions to purchase EVs. To provide insights for this understanding, this study aims to investigate such factors with a particular focus on users’ attitudes and perceptions. A questionnaire survey was conducted in September 2019 among potential consumers in the major cities of South Korea. A total of 1500 valid survey responses were obtained, and investigations using binary logistic regression and regression tree were conducted for an empirical analysis. The results showed that among attitudinal attributes, environmental and economic perceptions concerning EV use were the strongest predictors for an EV purchase. In addition, technological concerns were found to have negative impacts on EV purchase intentions. The findings of this study could provide reasonable guidelines for establishing marketing strategies and serve as a reference for EV stakeholders to improve the applicability of current policies regarding EV adoption.

scholarly journals The issue of addressing the lack of parking spaces for road freight transport in cities - a case study

2020 ◽  
Vol 10 (1) ◽  
pp. 209-215
Author(s):  
Jan Lizbetin ◽  
Ladislav Bartuska
Keyword(s):  
Urban Areas ◽  
Multicriteria Decision Making ◽  
Road Transport ◽  
Decision Making Process ◽  
Topsis Method ◽  
The Czech Republic ◽  
Road Freight ◽  
Rest Areas ◽  
Freight Vehicles

AbstractStatic traffic (parking of vehicles) is one of the most problematic areas of transport in urban areas. In particular, parking areas for heavy freight vehicles in city areas cause problems connected, for example, with insufficient capacity or inadequate equipment. In the Czech Republic the regional concepts for the location of parking lots for trucks have not been developed - rest areas are built mainly on highways. Drivers are forced into other roads to search for alternative parking spaces and thus jeopardize the safety of the cargo to be transported. Because of the lack of such parking areas, drivers are forced to violate the European Agreement Concerning the Work of Crews of Vehicles Engaged in International Road Transport (AETR) or, due to this agreement, to park the truck before the driver’s work shift ends, thereby reducing the efficiency of full driver usage. The paper deals with the issue of rest areas location for road freight vehicles in the selected area. The first part of the paper characterizes variant solutions of parking areas in a particular selected area, which were evaluated on the basis of an analysis of the current conditions. The second part of the paper introduces the evaluation of individual variants using the TOPSIS method, which was chosen as the most appropriate method of multicriteria decision making process.

scholarly journals A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 344
Author(s):  
Hyunsuk Kim ◽  
Woojin Kim ◽  
Jungsook Kim ◽  
Seung-Jun Lee ◽  
Daesub Yoon ◽  
...  
Keyword(s):  
Human Factors ◽  
Response Times ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Traffic Density ◽  
Automated Driving ◽  
Stabilization Time ◽  
Factors Affecting ◽  
Cart Analysis ◽  
Level 3

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

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