Factors Affecting Overtaking Behaviour On Single Carriageway Road: Case Study at Jalan Kluang-Kulai

Hassan, Sitti Asmah; Puan, Othman Che; Mashros, Nordiana; Sukor, Nur Sabahiah Abdul

doi:10.11113/jt.v71.3765

Cited by 18 publications

(7 citation statements)

References 6 publications

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“…Finally, the GGDM analysis resulted with twenty six (26) GATB factors (see Figure 7 as the hierarchy structure of the GTAB factors); within which 'F27-Dangerous overtaking' and 'F3-Slow once realizing bike lanes for cyclist crossing' have received the highest Greenery Value Impact (GVI), 97% and 91% respectively. It was observed that higher overtaking speed is associated with the aggressive behavior in overtaking maneuvers, therefore, the overtaking driver perceives a shorter gap with the oncoming traffic, hence a shorter overtaking time and safety margin (Hassan et al, 2014). In contrast, dangerous overtaking at an intersection has become the voluntary risk factors that contributes to accident likelihood (Clarke et al, 2005).…”

Section: Resultsmentioning

confidence: 99%

Drivers’ adaptive travel behaviors towards green transportation development: a critical review

Shafaghat

Keyvanfar

Muslim

2016

AoT

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The transportation professionals integrated the concept Green in various dimensions of transportation, such as, green vehicle, green highway. The current study has established a new dimension to green transportation, which is called Green Driver as whom substantially contributes to less emission and fuel consumption, and higher-safety. The research established the driver’s Green Adaptive Travel Behaviors (GATB), in particular, that is referred to voluntary personal and lifestyle behaviors on less energy consumption and emission. The methodology was designed into two phases. Phase one was to investigate driver’s GATBs through systematic literature review process and content analysis method. The second phase was to verify greenery value impact (GVI) of the finalized list of drivers’ GATBs through an expert input study and Grounded Group Decision Making (GGDM) method. Total twenty six (26) GATB factors have been determined. Amongst, the factor ‘F27- Dangerous overtaking’ has received the highest value (97%) followed with ‘F3- Slow once realizing bike lanes for cyclist crossing’ (91%). In contrast, ‘F4- Realize visual Obstacles to manage the speed’ and ‘F21- Brake with smooth deceleration’ has received the lowest value (77%) among other factors. Two of the initial factors;‘F5-Use traffic calming devices’ (55%), and ‘F24- Change highest possible gear’ (69%) could not reach the 70% saturation; hence, they have been dropped from the list of GATB factors. Indeed, the GATB efforts are not limited to technology and practice; but also can include education and enforcement to driving regulations in order to interconnect driver, technology, environment, and vehicle. The research concluded with an innovative technique used as the decision support tool to evaluate the greenery grade of any individual driver on committing to less emission, less fuel consumption, and higher safety in traveling. As future study, the Green driver behaviour index assessment model will be developed based on this study outputs.

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Section: Resultsmentioning

confidence: 99%

Drivers’ adaptive travel behaviors towards green transportation development: a critical review

Shafaghat

Keyvanfar

Muslim

2016

AoT

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“…Previous studies in Malaysia collected driving data using surveys/question naires [24,25,32,33,35,36,38], observations [21][22][23][27][28][29][30]37] and simulations [26,34]. Most of these techniques have been criticized for being biased [2][3][4][5]13,14,72].…”

Section: Discussionmentioning

confidence: 99%

“…2014 Observations [21][22][23] 2015 Questionnaire [24,25] 2015 Simulations [26] 2015 Observations [27] 2016 Observations [28][29][30] 2017 Non-naturalistic experiments [31] 2018 Questionnaire [32,33] 2018 Simulations [34] 2019 Questionnaire [35,36] 2020 Observations [37] 2020 Questionnaire [38] In previous publications outside of Malaysia, experts were consulted to identify aggressive behaviors. Japan's risk consultants assigned scores to aggressive behaviors in [40].…”

Section: Year Type Of Data Collection Referencementioning

confidence: 99%

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

Al-Hussein

Por

Kiah

et al. 2022

IJERPH

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The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the “safe/aggressive” categorization being a state that describes a driver’s conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver’s behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety.

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“…Particularmente, no caso de manobras de ultrapassagem em rodovias de pista simples bidirecional, estão envolvidos diversos fatores que dependem exclusivamente dos condutores envolvidos, como: o julgamento e tomada de decisão de ultrapassagem do motorista, os riscos que o condutor está disposto a assumir, as ações do motorista que está sendo ultrapassado e as ações dos outros motoristas nas proximidades, além da velocidade e tamanho dos veículos envolvidos. Por outro lado, deve-se considerar os comportamentos inapropriados, como: falta de atenção dos motoristas, inabilidades de observação do entorno, excesso de velocidade, não obediência às regras de trânsito, fadiga e sonolência (HASSAN et al, 2014).…”

Section: Justificativaunclassified

“…Os dados de vídeo foram utilizados para determinar a velocidade e as distâncias dos veículos envolvidos nas ultrapassagens observadas. Os resultados foram utilizados no desenvolvimento de um modelo modificado de distância de visibilidade de ultrapassagem (DVU) que foi incorporado ao Green Book da AASHTO 2011(HARWOOD et al, 2008) Hassan et al (2014). exploraram os fatores que afetam a velocidade de um veículo de ultrapassagem em seção de rodovia na Malásia.…”

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Manobra de ultrapassagem em pista simples bidirecional com simulador de direção

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Acidentes do tipo colisão frontal representaram 3,2% do total de acidentes e em relação às mortes registradas, a colisão frontal foi responsável por 31% das mortes: 25 mil mortes, 17 mil feridos graves e 25 mil feridos leves. Estes dados mostram que a ultrapassagem em rodovias de pista simples é uma das manobras mais complexas e arriscadas das rodovias brasileiras. Devido a estes valores, as atividades de pesquisa e análise de dados da acidentalidade rodoviária, juntamente com a influência de diversos fatores -infraestrutura rodoviária, componente veicular e comportamento do condutor -constituem uma parte essencial do projeto geométrico da rodovia e da segurança viária. O processo de ultrapassagem envolve manobras de mudança de faixa, aceleração, desaceleração, ações e estimativa da velocidade relativa dos veículos ultrapassados e o comportamento do condutor.O principal objetivo desta tese foi analisar as manobras de ultrapassagem em rodovias de pesquisa simples bidirecional. Para isso, foram formuladas três hipóteses e um método para confrontar os parâmetros que compõem o modelo de distância de visibilidade de ultrapassagem (AASHTO e DNIT), através de um experimento fatorial de medidas repetidas que contou com oitenta voluntários, que dirigiram num trecho virtual de rodovia no simulador de direção. O experimento possibilitou investigar as fases da manobra de ultrapassagem; caracterizar o perfil do condutor a partir do nível de risco mensurado nas ultrapassagens realizadas; o grau de confiança -realismo -do simulador de direção de base fixa para as pesquisas sobre fatores humanos em manobra de ultrapassagem e mensurar as distâncias e tempos das fases da manobra de ultrapassagem em ambiente simulado e compará-las ao modelo de distância de visibilidade normatizado.Palavras-chave: Simulador de direção. Manobras de ultrapassagem. Fatores humanos.Colisão frontal.

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Factors Affecting Overtaking Behaviour On Single Carriageway Road: Case Study at Jalan Kluang-Kulai

Cited by 18 publications

References 6 publications

Drivers’ adaptive travel behaviors towards green transportation development: a critical review

Drivers’ adaptive travel behaviors towards green transportation development: a critical review

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

Manobra de ultrapassagem em pista simples bidirecional com simulador de direção

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