Longitudinal jerk and celeration as measures of safety in bus rapid transit drivers in Tehran

Khorram, Bahram; Wåhlberg, Anders af; Kashani, Ali Tavakoli

doi:10.1080/1463922x.2020.1719228

Cited by 16 publications

(16 citation statements)

References 58 publications

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“…The conclusions from the present paper are that, again, tests have shown support for the DCBT, although with some slight deviations. This is similar to other papers [16,24,30,36]. This means that the theory is superior to other theories of driver behaviour, not only in its testability, but in its support from tests.…”

Section: Discussionsupporting

confidence: 89%

“…This method of computing a risk index for a driver can be compared to that of events, where the number of happenings during a given journey is negatively related to the level of the threshold used. At very low thresholds (for example, <2 m/s 2 ), this kind of calculation will become very similar to celeration [16]. In standard practice, however, the event-based method and DCBT are very different, although this has not always been understood (e.g., [13]).…”

Section: Introductionmentioning

confidence: 97%

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Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience

Wåhlberg

2018

Safety

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Driver celeration behaviour theory (DCBT) assumes that risk for a driver of causing a road crash is linearly related to speed change in any given moment and that the speed change variable (celeration) captures all risk (all vehicle control movements can be measured as acceleration). When sampling driver behaviour, the celeration variable is calculated as the average of all absolute values of acceleration when the vehicle is moving. DCBT predicts that no other variable can be a stronger predictor of (the same set of) traffic accident involvements than celeration, given equal reliability of the predictors. Also, other predictors, regardless of which ones, should associate with celeration in ways that are similar to how they correlate with accidents. Predictions were tested in a sample of bus drivers, against variables with reliabilities close to 1 (age, sex, experience, ethnic origin), which are not necessarily optimal predictors for testing but were the only predictors available. The results were largely as predicted from theory. The principles for testing the kind of predictions made from celeration theory were discussed, outlining the importance of a larger number of variables, preferably with repeated measurements.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 97%

Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience

Wåhlberg

2018

Safety

Self Cite

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“…Based on the gathered views, the study classified the drivers to moderate and aggressive groups on the basis of their acceleration rates at curves. In today's world, new measures continuously undergo development to assess driving risk from naturalistic driving with the help of new technologies like GPS [75].…”

Section: 1) Speedmentioning

confidence: 99%

“…In the context of the current study, there are different aspects of challenges included. For example, low GPS signals or app malfunction are among the most significant challenges in data collection because these issues may result in missing a few seconds of speed data [75]. Sites and the nature of roads, such as the mountainous roads, affect the GPS signals [113].…”

Section: ) Concern On Data Collection Managementmentioning

confidence: 99%

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

et al. 2021

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Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.

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“…Ride comfort is an important parameter in amusement rides [1,4,5], sea-keeping [9,10,122,126] and traditional land-based vehicles [12,25,45,95,[98][99][100][101][102][103][104][105][106]108,109,[111][112][113]115,[118][119][120][121][123][124][125][127][128][129][130][131]. There are still discussions ongoing regarding the significance of jerk regarding ride comfort for vehicles, and jerk is probably a better measure for driving comfort than acceleration, as pointed out by van Santen [128] and confirmed, e.g., in the study by Grant and Haycock [107].…”

Section: Jerk In Criteria For Discomfortmentioning

confidence: 99%

Jerk within the Context of Science and Engineering—A Systematic Review

et al. 2020

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Rapid changes in forces and the resulting changes in acceleration, jerk and higher order derivatives can have undesired consequences beyond the effect of the forces themselves. Jerk can cause injuries in humans and racing animals and induce fatigue cracks in metals and other materials, which may ultimately lead to structure failures. This is a reason that it is used within standards for limits states. Examples of standards which use jerk include amusement rides and lifts. Despite its use in standards and many science and engineering applications, jerk is rarely discussed in university science and engineering textbooks and it remains a relatively unfamiliar concept even in engineering. This paper presents a literature review of the jerk and higher derivatives of displacement, from terminology and historical background to standards, measurements and current applications.

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Longitudinal jerk and celeration as measures of safety in bus rapid transit drivers in Tehran

Cited by 16 publications

References 58 publications

Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience

Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

Jerk within the Context of Science and Engineering—A Systematic Review

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