scholarly journals Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost

2021 ◽  
Vol 13 (2) ◽  
pp. 926
Author(s):  
Manze Guo ◽  
Zhenzhou Yuan ◽  
Bruce Janson ◽  
Yongxin Peng ◽  
Yang Yang ◽  
...  
Keyword(s):  
Traffic Management ◽  
Significant Risk ◽  
Logistic Models ◽  
Classification Problem ◽  
Gradient Boosting ◽  
Traffic Crashes ◽  
Crash Data ◽  
Extreme Gradient Boosting ◽  
Pedestrian Traffic ◽  
Pedestrian Crashes

Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians’ safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature’s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level’s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.

scholarly journals An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

2021 ◽  
Vol 7 ◽  
pp. e586
Author(s):  
Pritul Dave ◽  
Arjun Chandarana ◽  
Parth Goel ◽  
Amit Ganatra
Keyword(s):  
Waiting Time ◽  
Traffic Management ◽  
Green Light ◽  
Gradient Boosting ◽  
Optimal Time ◽  
Traffic Light ◽  
Ensemble Technique ◽  
Extreme Gradient Boosting ◽  
On The Road ◽  
Smart Traffic

The traffic congestion and the rise in the number of vehicles have become a grievous issue, and it is focused worldwide. One of the issues with traffic management is that the traffic light’s timer is not dynamic. As a result, one has to remain longer even if there are no or fewer vehicles, on a roadway, causing unnecessary waiting time, fuel consumption and leads to pollution. Prior work on smart traffic management systems repurposes the use of Internet of things, Time Series Forecasting, and Digital Image Processing. Computer Vision-based smart traffic management is an emerging area of research. Therefore a real-time traffic light optimization algorithm that uses Machine Learning and Deep Learning Techniques to predict the optimal time required by the vehicles to clear the lane is presented. This article concentrates on a two-step approach. The first step is to obtain the count of the independent category of the class of vehicles. For this, the You Only Look Once version 4 (YOLOv4) object detection technique is employed. In the second step, an ensemble technique named eXtreme Gradient Boosting (XGBoost) for predicting the optimal time of the green light window is implemented. Furthermore, the different implemented versions of YOLO and different prediction algorithms are compared with the proposed approach. The experimental analysis signifies that YOLOv4 with the XGBoost algorithm produces the most precise outcomes with a balance of accuracy and inference time. The proposed approach elegantly reduces an average of 32.3% of waiting time with usual traffic on the road.

scholarly journals Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

2020 ◽  
Vol 5 (8) ◽  
pp. 62
Author(s):  
Clint Morris ◽  
Jidong J. Yang
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Crash Analysis ◽  
Suitable Alternative ◽  
Crash Data ◽  
Extreme Gradient Boosting ◽  
Modern Machine

Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways.

scholarly journals Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11 ◽  
Author(s):  
Shubo Wu ◽  
Quan Yuan ◽  
Zhongwei Yan ◽  
Qing Xu
Keyword(s):  
Risk Factors ◽  
Statistical Analysis ◽  
Injury Severity ◽  
Northern China ◽  
Time Of Day ◽  
Road User ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Pedestrian Crashes ◽  
Crash Injury Severity

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

Development of Urban Road Order Index Based on Driving Behavior and Speed Variation

2019 ◽  
Vol 2673 (7) ◽  
pp. 466-478 ◽  
Author(s):  
Ying Yao ◽  
Xiaohua Zhao ◽  
Yunlong Zhang ◽  
Jianming Ma ◽  
Jian Rong ◽  
...  
Keyword(s):  
Traffic Management ◽  
Driving Behavior ◽  
Traffic Crashes ◽  
Crash Data ◽  
Speed Variation ◽  
Driving Behaviors ◽  
Congestion Index ◽  
Navigation Software ◽  
Relationship Of

This study uses driving behavior and speed variation data from navigation software to propose a traffic order index (TOI) for evaluating the order of traffic on urban roads. Based on the significance analysis of driving behaviors and speed variation under different road types and congestion levels, the TOI calculation method is proposed by using the order of preference by similarity to ideal solution (TOPSIS) method. Through a case study of an urban area in Beijing, the distribution of TOI under different road types and congestion levels is described, and the hourly and daily TOI heat maps are generated to show changes in TOI for urban roads during different periods. The relationships between TOI, congestion index (CI) and crash data were explored. A nonlinear relationship of TOI and CI was discovered, and roads with more crashes or longer crash durations were associated with a lower level of traffic order. The TOI could help traffic management departments better understand the order of traffic on roads, reveal causes of the poor level of traffic order in some roads, and more reasonably dispatch traffic police to handle traffic crashes.

scholarly journals APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES

2021 ◽  
pp. 1-33
Author(s):  
Stéphane Loisel ◽  
Pierrick Piette ◽  
Cheng-Hsien Jason Tsai
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Regression Tree ◽  
Classification Problem ◽  
Point Of View ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Extreme Gradient Boosting

Abstract Modeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.

Descriptive Analysis of Crashes Involving Pedestrians in Florida, 1990–1994

1998 ◽  
Vol 1636 (1) ◽  
pp. 138-145 ◽  
Author(s):  
Michael R. Baltes
Keyword(s):  
Injury Severity ◽  
Descriptive Analysis ◽  
Weather Condition ◽  
Time Of Day ◽  
Specific Sequence ◽  
Traffic Crashes ◽  
Crash Data ◽  
Sequence Of Events ◽  
Gender And Age ◽  
Pedestrian Crashes

A descriptive analysis was conducted of pedestrian crash data used to categorize pedestrian crashes according to a variety of factors, including pedestrian gender and age, time of day, pedestrian’s contributing cause of crash, injury severity, weather condition, road system identifier, and so forth, to the specific sequence of events perceived to influence the crash. The results reported are based on 5 years (1990–1994) of pedestrian crash data in Florida. The database contained 44,541 or 100 percent of the pedestrian crashes that were reported to law enforcement that occurred in Florida during this period. The process of categorizing pedestrian crashes in the manner described provides a valuable analytical tool for developing effective and practical countermeasures to reduce the deaths and injuries incurred by pedestrians involved in traffic crashes in Florida and elsewhere. Analysis of the pedestrian crash data can provide information about to whom and where, when, and how crashes occur in Florida.

scholarly journals Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Bo Sun ◽  
Tuo Sun ◽  
Pengpeng Jiao
Keyword(s):  
Traffic Management ◽  
Recognition System ◽  
Gradient Boosting ◽  
Traffic Flow Prediction ◽  
Extreme Gradient Boosting ◽  
Spatio Temporal ◽  
Traffic Systems ◽  
Segment Data ◽  
Relationship Of ◽  
Individual Adjustment

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions

2020 ◽  
pp. 253-262
Author(s):  
Mohammad Hamim Zajuli Al Faroby ◽  
Mohammad Isa Irawan ◽  
Ni Nyoman Tri Puspaningsih
Keyword(s):  
Protein Interactions ◽  
Interaction Analysis ◽  
Synthesis Process ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Central Function ◽  
Extreme Gradient Boosting ◽  
Main Protein ◽  
The Right ◽  
Roc Score

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces  features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of  and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.

scholarly journals Exploratory Analysis of Pedestrian Road Trauma in Finland

2021 ◽  
Vol 13 (12) ◽  
pp. 6715
Author(s):  
Steve O’Hern ◽  
Roni Utriainen ◽  
Hanne Tiikkaja ◽  
Markus Pöllänen ◽  
Niina Sihvola
Keyword(s):  
Cluster Analysis ◽  
Motor Vehicle ◽  
Seasonal Effects ◽  
Unique Element ◽  
Driver Behaviour ◽  
Crash Data ◽  
Safety Issues ◽  
Key Issues ◽  
Development Goals ◽  
Pedestrian Crashes

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

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