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Lee J, Song J. Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:9392. [PMID: 38067764 PMCID: PMC10708828 DOI: 10.3390/s23239392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/16/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023]
Abstract
Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.
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Affiliation(s)
| | - JaeSeung Song
- Depatment of Convergence Engineering for Intelligent Drone, Sejong University, Gwangjin-gu, Seoul 05006, Republic of Korea;
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Scarano A, Rella Riccardi M, Mauriello F, D'Agostino C, Pasquino N, Montella A. Injury severity prediction of cyclist crashes using random forests and random parameters logit models. ACCIDENT; ANALYSIS AND PREVENTION 2023; 192:107275. [PMID: 37683568 DOI: 10.1016/j.aap.2023.107275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/09/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023]
Abstract
Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.
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Affiliation(s)
- Antonella Scarano
- University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.
| | - Maria Rella Riccardi
- University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.
| | - Filomena Mauriello
- University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.
| | - Carmelo D'Agostino
- Department of Technology and Society, Faculty of Engineering, LTH Lund University, Lund, Sweden.
| | - Nicola Pasquino
- University of Naples Federico II Department of Electrical Engineering and Information Technologies Via Claudio 21, 80125 Naples, Italy.
| | - Alfonso Montella
- University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.
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Rella Riccardi M, Mauriello F, Scarano A, Montella A. Analysis of contributory factors of fatal pedestrian crashes by mixed logit model and association rules. Int J Inj Contr Saf Promot 2022; 30:195-209. [DOI: 10.1080/17457300.2022.2116647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Maria Rella Riccardi
- Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy
| | - Filomena Mauriello
- Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy
| | - Antonella Scarano
- Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy
| | - Alfonso Montella
- Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy
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Wang Y, Xu J, Liu X, Zheng Z, Zhang H, Wang C. Analysis on Risk Characteristics of Traffic Accidents in Small-Spacing Expressway Interchange. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9938. [PMID: 36011573 PMCID: PMC9408132 DOI: 10.3390/ijerph19169938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/02/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Many small-spacing interchanges (SSI) appear when the density of the expressway interchanges increases. However, the characteristics of traffic accidents in SSI have not been explained clearly. Therefore, this paper systematically takes the G3001 expressway in Xi'an as the research object to explore the accident characteristics of SSI. Firstly, the expressway is divided into four sections. Furthermore, their safety can be evaluated by the number of accidents per unit distance of 100 million vehicles (NAP). Subsequently, eight indexes, such as mean spacing distance (MSD), are selected to explain the cause affecting expressway safety by developing the least square support vector machine (LSSVM). Secondly, the difference between SSI and normal-spacing interchanges (NSI) is clarified by statistical analysis. Finally, LSSVM, random forest, and logistic regression models are built using 12 indicators, such as the time spent exploring the causes of serious accidents. The results show that the inner ring NAP in Sections I and II with SSI is 27.2 and 33.7, higher than in other sections. The density, annual average daily traffic, and MSD adversely affect expressway traffic safety. The road condition mainly influences the serious traffic accidents in the SSI. This study can provide the theoretical basis for traffic management and accident prevention in the SSI of the expressway.
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Affiliation(s)
- Yanpeng Wang
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Jin Xu
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
- Chongqing Key Laboratory of “Human-Vehicle-Road” Cooperation and Safety for Mountain Complex Environment, Chongqing Jiaotong University, Chongqing 400074, China
| | - Xingliang Liu
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Zhanji Zheng
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Heshan Zhang
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
| | - Chengyu Wang
- School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
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Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest. SUSTAINABILITY 2022. [DOI: 10.3390/su14074101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers’ injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver’s age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers’ injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers.
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Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain. SUSTAINABILITY 2022. [DOI: 10.3390/su14063188] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The study aims to investigate the factors that are associated with fatal and severe vehicle–pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended.
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Chen S, Shao H, Ji X. Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312725. [PMID: 34886451 PMCID: PMC8656871 DOI: 10.3390/ijerph182312725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/24/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022]
Abstract
Traffic accidents have significant financial and social impacts. Reducing the losses caused by traffic accidents has always been one of the most important issues. This paper presents an effort to investigate the factors affecting the accident severity of drivers with different driving experience. Special focus was placed on the combined effect of driving experience and age. Based on our dataset (traffic accidents that occurred between 2005 and 2021 in Shaanxi, China), CatBoost model was applied to deal with categorical feature, and SHAP (Shapley Additive exPlanations) model was used to interpret the output. Results show that accident cause, age, visibility, light condition, season, road alignment, and terrain are the key factors affecting accident severity for both novice and experienced drivers. Age has the opposite impact on fatal accident for novice and experienced drivers. Novice drivers younger than 30 or older than 55 are prone to suffer fatal accident, but for experienced drivers, the risk of fatal accident decreases when they are young and increases when they are old. These findings fill the research gap of the combined effect of driving experience and age on accident severity. Meanwhile, it can provide useful insights for practitioners to improve traffic safety for novice and experienced drivers.
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Montella A, Mauriello F, Pernetti M, Rella Riccardi M. Rule discovery to identify patterns contributing to overrepresentation and severity of run-off-the-road crashes. ACCIDENT; ANALYSIS AND PREVENTION 2021; 155:106119. [PMID: 33848813 DOI: 10.1016/j.aap.2021.106119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 03/04/2021] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
The main objective of this paper was to analyse the roadway, environmental, and driver-related factors associated with an overrepresentation of frequency and severity of run-off-the-road (ROR) crashes. The data used in this study refer to the 6167 crashes occurred in the section Naples-Candela of A16 motorway, Italy in the period from 2001 to 2011. The analysis was carried out using the rule discovery technique due to its ability of extracting knowledge from large amounts of data previously unknown and indistinguishable by investigating patterns that occur together in a given event. The rules were filtered by support, confidence, lift, and validated by the lift increase criterion. A two-step analysis was carried out. In the first step, rules discovering factors contributing to ROR crashes were identified. In the second step, studying only ROR crashes, rules discovering factors contributing to severe and fatal injury (KSI) crashes were identified. As a result, 94 significant rules for ROR crashes and 129 significant rules for KSI crashes were identified. These rules represent several combinations of geometric design, roadside, barrier performance, crash dynamic, vehicle, environmental and drivers' characteristics associated with an overrepresentation of frequency and severity of ROR crashes. From the methodological point of view, study results show that the a priori algorithm was effective in providing new information which was previously hidden in the data. Finally, several countermeasures to solve or mitigate the safety issues identified in this study were discussed. It is worthwhile to observe that the study showed a combination of factors contributing to the overrepresentation of frequency and severity of ROR crashes. Consequently, the implementation of a combination of countermeasures is recommended.
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Affiliation(s)
- Alfonso Montella
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125, Naples, Italy.
| | - Filomena Mauriello
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125, Naples, Italy.
| | - Mariano Pernetti
- University of Campania Luigi Vanvitelli, Department of Engineering, Via Roma 29, 81031, Aversa, CE, Italy.
| | - Maria Rella Riccardi
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125, Naples, Italy.
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Yahaya M, Guo R, Jiang X, Bashir K, Matara C, Xu S. Ensemble-based model selection for imbalanced data to investigate the contributing factors to multiple fatality road crashes in Ghana. ACCIDENT; ANALYSIS AND PREVENTION 2021; 151:105851. [PMID: 33383521 DOI: 10.1016/j.aap.2020.105851] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/25/2020] [Accepted: 10/16/2020] [Indexed: 06/12/2023]
Abstract
The study aims to identify relevant variables to improve the prediction performance of the crash injury severity (CIS) classification model. Unfortunately, the CIS database is invariably characterized by the class imbalance. For instance, the samples of multiple fatal injury (MFI) severity class are typically rare as opposed to other classes. The imbalance phenomenon may introduce a prediction bias in favour of the majority class and affect the quality of the learning algorithm. The paper proposes an ensemble-based variable ranking scheme that incorporates the data resampling. At the data pre-processing level, majority weighted minority oversampling (MWMOTE) is employed to treat the imbalanced training data. Ensemble of classifiers induced from the balanced data is used to evaluate and rank the individual variables according to their importance to the injury severity prediction. The relevant variables selected are then applied to the balanced data to form a training set for the CIS classification modelling. An empirical comparison is conducted through considering the variable ranking by: 1) the learning of single inductive algorithm with imbalanced data where the relevant variables are applied to the imbalanced data to form the training data; 2) the learning of single inductive algorithm with MWMOTE data and the relevant variables identified are applied to the balanced data to form the training data; and 3) the learning of ensembles with imbalanced data where the relevant variables identified are applied to the imbalanced data to form the training data. Bayesian Networks (BNs) classifiers are then developed for each ranking method, where nested subsets of the top ranked variables are adopted. The model predictions are captured in four performance indicators in the comparative study. Based on three-year (2014-2016) crash data in Ghana, the empirical results show that the proposed method is effective to identify the most prolific predictors of the CIS level. Finally, based on the inference results of BNs developed on the best subset, the study offers the most probable explanations to the occurrence of MFI crashes in Ghana.
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Affiliation(s)
- Mahama Yahaya
- School of Transportation and Logistics, Southwest Jiaotong University, West Park, High-Tech District, Chengdu, China 611756; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, West Park, High-Tech District, Chengdu, 611756, China
| | - Runhua Guo
- Department of Civil Engineering, Suite 217, Heshangheng Bldg, Tsinghua University, 100084, Beijing, China
| | - Xinguo Jiang
- School of Transportation and Logistics, Southwest Jiaotong University, West Park, High-Tech District, Chengdu, China 611756; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, West Park, High-Tech District, Chengdu, 611756, China.
| | - Kamal Bashir
- Department of Information Technology, Karare University, Omdurman, 12304, Sudan
| | - Caroline Matara
- Department of Civil and Construction Engineering, University of Nairobi, 30197, Nairobi, Kenya
| | - Shiwei Xu
- Guangzhou Transportation Planning Institute, 510030, Guangzhou, China
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Yahaya M, Guo R, Fan W, Bashir K, Fan Y, Xu S, Jiang X. Bayesian networks for imbalance data to investigate the contributing factors to fatal injury crashes on the Ghanaian highways. ACCIDENT; ANALYSIS AND PREVENTION 2021; 150:105936. [PMID: 33338913 DOI: 10.1016/j.aap.2020.105936] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/23/2020] [Accepted: 11/30/2020] [Indexed: 06/12/2023]
Abstract
The crash data are often predominantly imbalanced, among which the fatal injury (or minority) crashes are significantly underrepresented relative to the non-fatal injury (or majority) ones. This unbalanced phenomenon poses a huge challenge to most of the statistical learning methods and needs to be addressed in the data preprocessing. To this end, we comparatively apply three data balance methods, i.e., the Synthetic Minority Oversampling Technique (SMOTE), the Borderline SMOTE (BL-SMOTE), and the Majority Weighted Minority Oversampling (MWMOTE). Then, we examine different Bayesian networks (BNs) to explore the contributing factors of fatal injury crashes. The 2016 highway crash data of Ghana are retrieved for the case study. The results show that the accuracy of the injury severity classification is improved by using the preprocessed data. Highest improvement is observed on the data preprocessed by the MWMOTE technique. Statistical verification is done by the Wilcoxon signed-rank test. The inference results of the best BNs show the significant factors of fatal crashes which include off-peak time, non-intersection area, pedestrian involved collisions, rural road environment, good tarred road, roads without shoulders, and multiple vehicles involved crash.
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Affiliation(s)
- Mahama Yahaya
- School of Transportation and Logisitics, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, No. 999, Xi'an Road, Chengdu, Sichuan, PR China.
| | - Runhua Guo
- Department of Civil Engineering, Suit 217, Heshanheng Bldg, Tsinghua University, Beijing 10084, PR China.
| | - Wenbo Fan
- School of Transportation and Logisitics, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, No. 999, Xi'an Road, Chengdu, Sichuan, PR China.
| | - Kamal Bashir
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, PR China.
| | - Yingfei Fan
- School of Transportation and Logistics Engineering, Taiyuan University of Science and Technology, 66 Waliu Road, Wanbailin District, Taiyuan, China 030024.
| | - Shiwei Xu
- Guangzhou Transport Planning Research Institute, No. 10 Guangwei Road, Guangzhou, PR China.
| | - Xinguo Jiang
- School of Transportation and Logisitics, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, No. 999, Xi'an Road, Chengdu, Sichuan, PR China.
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Singer G, Marudi M. Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic. ENTROPY 2020; 22:e22080871. [PMID: 33286642 PMCID: PMC7517475 DOI: 10.3390/e22080871] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/05/2020] [Accepted: 08/05/2020] [Indexed: 01/19/2023]
Abstract
In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. In such an application, some of the potential classification errors could have critical consequences. The classification tool will enable the spread of the epidemic to be tracked and controlled by yielding insights regarding the relationship between local containment measures and the daily growth factor. In order to benefit maximally from a variety of ordinal and non-ordinal algorithms, we also propose an ensemble majority voting approach to combine different algorithms into one model, thereby leveraging the strengths of each algorithm. We perform experiments in which the task is to classify the daily COVID-19 growth rate factor based on environmental factors and containment measures for 19 regions of Italy. We demonstrate that the ordinal algorithms outperform their non-ordinal counterparts with improvements in the range of 6–25% for a variety of common performance indices. The majority voting approach that combines ordinal and non-ordinal models yields a further improvement of between 3% and 10%.
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Affiliation(s)
- Gonen Singer
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
- Correspondence:
| | - Matan Marudi
- Department of Industrial Engineering, Tel-Aviv University, Tel Aviv-Yafo 39040, Israel;
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Montella A, de Oña R, Mauriello F, Rella Riccardi M, Silvestro G. A data mining approach to investigate patterns of powered two-wheeler crashes in Spain. ACCIDENT; ANALYSIS AND PREVENTION 2020; 134:105251. [PMID: 31402051 DOI: 10.1016/j.aap.2019.07.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 06/27/2019] [Accepted: 07/26/2019] [Indexed: 06/10/2023]
Abstract
Powered two-wheelers (PTWs) are growing globally each year as they are considered an attractive alternative to cars (flexible, small, affordable, fast and easy to park), especially on congested traffic situations. However, PTWs represent an important challenge for road safety. In fact, in 2016, Spain ranked fifth in terms of PTW fatalities among EU 28. For this reason, this paper aims to investigate which are the patterns among crash characteristics contributing to PTW crashes in Spain. Data from 78,611 crashes involving PTWs occurred in Spain in the period 2011-2013 were analyzed. The analysis was performed by using classification trees and rules discovery which are suitable models aimed at extracting knowledge and identifying valid and understandable patterns from large amounts of data previously unknown and indistinguishable. The response variables assessed in this study were severity and crash type. As a result, several combinations of road, environmental and drivers' characteristics associated with severity and typology of PTW crashes in Spain were identified. Based on the analysis results, several countermeasures to solve or mitigate the safety issues identified in the study were proposed. From the methodological point of view, study results show that both the classification trees and the a priori algorithm were effective in providing non-trivial and unsuspected relations in the data. Classification trees structure allowed a simpler understanding of the phenomenon under study while association discovery provided new information which was previously hidden in the data. Given that the results of the two different techniques were never contradictory, we recommend using classification trees and association discovery as complementary approaches since their combination is effective in exploring data providing meaningful insights about PTW crash characteristics and their interdependencies.
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Affiliation(s)
- Alfonso Montella
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy.
| | - Rocìo de Oña
- University of Granada, TRYSE Research Group, Department of Civil Engineering, Spain
| | - Filomena Mauriello
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy
| | - Maria Rella Riccardi
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy
| | - Giuseppe Silvestro
- University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy
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Abstract
Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification.
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The Effects of Differences in Individual Characteristics and Regional Living Environments on the Motivation to Immigrate to Hometowns: A Decision Tree Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9132748] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Population decline and rural–urban population disparities are serious problems in Japan. This study aims to investigate the relationship between people’s motivations to migrate to their hometowns (“U-turn migration”) and their evaluations of the living environments of both their hometowns and current places of residence. An online questionnaire survey was conducted for people living in multiple places in Japan. By using the data of respondents’ U-turn motivations and their evaluations of living environments, we conducted a decision tree analysis to quantitatively describe the multilayered relationship between the environments and people’s motivations, and we focused on patterns that can ameliorate the population disparities. These are the major findings: first, living environments in both the hometown and at the current place of residence affected the U-turn motivations. Second, respondents were divided into several groups based on similar U-turn motivation structures, and with different demographic characters among the groups. Additionally, the evaluations of some living environments tend to depend on the city size or geographic locations. Although some groups’ U-turn migrations may improve population disparities, the improvement and maintenance of living environments are necessary. The results can help local governments in identifying the living environments that need development, and in estimating the feasibility of U-turn migrations.
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