Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Champahom, Thanapong; Jomnonkwao, Sajjakaj; Chatpattananan, Vuttichai; Karoonsoontawong, Ampol; Ratanavaraha, Vatanavongs

doi:10.1155/2019/2568978

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…Thus, the RSA or IHL will proceed a few years at a time. Previous research about road safety in Thailand used to suggest a policy about RSA and IHL such as Jomnonkwao et al [61], Champahom et al [67], Se et al [68]. However, they suggest only generally but did not specify.…”

Section: Conclusion and Implementationmentioning

confidence: 99%

“…In Thailand, the campaign or the training topic of awareness of collision types are a low priority. The previous research recommended that the DOH should highlight some crash types such as rear-end crashes, single crashes and head-on crashes [42,67,68]. These studies suggest some policies, for example, the driver should be aware of the appropriate headway with the front vehicle.…”

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confidence: 99%

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Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

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Section: Conclusion and Implementationmentioning

confidence: 99%

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confidence: 99%

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

et al. 2021

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“…Obstruction due to the stopped vehicle at road shoulder or emergency lane without signaling has the highest mean since this situation might lead to the rear-end collision for motorcyclist. Rear end collision with higher fatality rate was expected when the front vehicle stopped, either on the roadside or on the shoulder or at traffic light [30].…”

Section: Discussion 41 Factors Provoking Riding Anger Of Motorcyclistsmentioning

confidence: 99%

Factors that cause anger among motorcyclists: exploratory factor analysis

Liew

Hamidun

Hamzah

2022

IJPHS

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Anger arising while riding a motorcycle is becoming a common problem on the road and it may be caused by many factors. The main objective of this study is to examine factors that could provoke anger among motorcyclists while riding. This study was based on self-reported questionnaires and comprised of 407 motorcyclists who owned valid riding licenses. exploratory factor analysis (EFA) indicated that the sample fit was suitable for factor analysis. In total, eight factors (unsafe or inappropriate actions, rude or sluggish actions, road conditions and design, police or enforces presence, illegal actions on the road, hazards on the road, rainy condition and obstruction on the road) with eigenvalues above 1.0 and 59 variables that ranged from 0.411 to 0.803, were generated. Cronbach’s Alpha analysis indicated that all factors’ values were greater than 0.60 and mean for all factors were reliable and had strength of consistency. This study provides the preliminary findings of factors and variables that could provoke riding anger among motorcyclists while on the road in the Malaysian context. The factors causing riding anger in this study have a similarity with driving anger factors that were developed by other countries and this indicates the validity of riding anger factors to provoke anger among motorcyclists on the road.

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“…A specific independent variable is assigned to each decision node together with its threshold value (the basis of a division on two branches). When the leaf node is reached, its content presents the expected value of dependent variable for independent variables meeting the rules found in split nodes [36]. The decision tree creates the flowchart that categorize the selectedby the built-in algorithm -types of data.…”

Section: Decision Trees-classifiermentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

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Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Cited by 19 publications

References 30 publications

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Factors that cause anger among motorcyclists: exploratory factor analysis

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

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