Cellular automata models for signalised and unsignalised intersections with special attention to mixed traffic flow: a review

Singh, Mohit Kumar; Rao, K. Ramachandra

doi:10.1049/iet-its.2020.0062

Cited by 17 publications

(10 citation statements)

References 77 publications

(117 reference statements)

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“…The knowledge map and the diverse perspectives and disciplines contribute to the elaboration of view in this regard. It lays the foundation for creating a dynamic research field across these disciplines [14].…”

Section: Search Methodsmentioning

confidence: 99%

“…Prior studies explored driver behavior as well, but the differences of the current study from prior studies were highlighted in this paper to reflect the significant contributions of the current study in the field. To be more specific, prior studies focused on simulation models that are available for signalized and unsignalized intersections using cellular automata (CA) models, and driver behavior was studied as a foregone conclusion [14]. Another systematic review focused on the issue of driver distraction in the context of advanced Driver Assistance Systems (DAS) [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Search Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…As the boundaries may affect the simulation results, 3 this paper gives an overview on the choice of open and closed boundaries for simulation, specifically for non-homogeneous, non-lane-based traffic conditions. This study also includes the realistic, feasible, and known but often ignored features such as seepage 12,13 and zone of influence. 13 This study considers fewer modes (bus, cars, two wheelers, motorized three wheelers) with multiple lanes at isolated signalized junction and at the mid-block.…”

Section: Introductionmentioning

confidence: 99%

“…This study also includes the realistic, feasible, and known but often ignored features such as seepage 12,13 and zone of influence. 13 This study considers fewer modes (bus, cars, two wheelers, motorized three wheelers) with multiple lanes at isolated signalized junction and at the mid-block. This study would be useful to decide the suitable boundary conditions for simulation of different facilities.…”

Section: Introductionmentioning

confidence: 99%

Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic

Singh

Rao

2022

SIMULATION

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Cellular automata (CA) simulation models developed for traffic are either closed or open boundary type. The selection and difference of boundaries has been studied extensively for ideal and single-lane homogeneous traffic conditions. However, the effect of these on multi-lane-heterogeneous traffic still needs attention because most of the traffic observed in many parts of the world is not single-lane homogeneous traffic. It is evident from multiple studies that open and closed boundaries affect the simulation results. Moreover, these require different inputs for simulation. This study attempts to evaluate the difference in the results of open and closed boundary simulations in heterogeneous non-lane-based traffic. The methodology discussed in this study is relatable to the field conditions. The present study includes some of the common but often ignored features in the model such as seepage of small-sized vehicles. Furthermore, this study also includes some of the previously unnoticed features while modeling the non-lane-based traffic at intersections. The modeling of open boundaries simulation can be better and easy in most of the situations compared to the closed boundaries. Closed boundary simulation results for flow-density curve show a smooth trend, whereas open boundary simulation results are scattered as observed in the field. This study further concludes that the size of the vehicle does not change the fundamental diagrams except when other characteristics such as seepage, lane change, and different maximum speeds for different modes are considered. The study used field observed influence zone of intersections to decide the dimension of intersection in the simulation model.

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“…Так как система автомобилей в транспортном потоке и дорожная инфраструктура как раз являются частью сложной и разнородной системы, клеточные автоматы остаются одним из популярных инструментов при моделировании транспортных потоков (см., например, [2][3][4][5][6][7][8][9][10][11][12]).…”

Section: Introductionunclassified

Алгоритмы Поведения Водителей На Нерегулируемых Перекрестках С Приоритетом И При Объезде Препятствий

Chechina¹

2021

Матем. моделирование

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Представлен набор алгоритмов «вежливый водитель» для двумерной микроскопической модели движения автотранспорта на основе теории клеточных автоматов. Рассмотрены случаи перестроения при объезде препятствия на многополосной дороге и въезд с второстепенной дороги на главную; для каждого случая представлена блок-схема алгоритма «вежливый водитель». Обсуждаются особенности программной реализации алгоритмов в составе комплекса программ для моделирования транспортных потоков. Проведены тестовые расчеты для апробации алгоритмов. Расчеты показывают, что наличие в модели «вежливых» водителей позволяет снизить время ожидания перестроения или въезда для автомобилей без приоритета, что соответствует реальной ситуации. Представленные результаты подтверждают, что созданные алгоритмы и программные модули позволяют адекватно моделировать различные ситуации, возникающие при движении автотранспорта и обусловленные «человеческим фактором».

show abstract

Cellular automata models for signalised and unsignalised intersections with special attention to mixed traffic flow: a review

Cited by 17 publications

References 77 publications

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

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

Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic

Алгоритмы Поведения Водителей На Нерегулируемых Перекрестках С Приоритетом И При Объезде Препятствий

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