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

Singh, Mohit Kumar; Rao, K. Ramachandra

doi:10.1177/00375497221078936

Cited by 5 publications

(5 citation statements)

References 50 publications

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“…Finally, the two-dimensional lattice Boltzmann model can be introduced to modeling multi-lane heterogeneous traffic, especially involving autonomous vehicles. 40…”

Section: Discussionmentioning

confidence: 99%

Bus bunching simulation based on the lattice Boltzmann model of traffic flow

Wang

Liu

2022

SIMULATION

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Bus bunching is a typical phenomenon observed in daily public transit operations and that significantly reduces the reliability and attractiveness of transit service. Although various control strategies, such as vehicle holding, speed guidance, and signal priority, have been proposed to eliminate or alleviate bus bunching, the impact of road traffic flow on bus bunching has not been well studied. To bridge this gap, this work proposes a new mesoscopic simulation model by incorporating the lattice Boltzmann model to simulate bus bunching considering the impacts of road traffic flow. Simulation results show that by utilizing the new simulation model, different phase diagrams can be observed. Under different conditions of traffic flow-density evolution, i.e., no oscillation, single oscillation, global oscillation, and irregular oscillation, the resulting bus trajectories are significantly different. In addition, an investigation of a real-world bus line in Beijing with global positioning system (GPS)-based vehicle location data shows that some results of the model are qualitatively consistent with the actual characteristics of the bus route. It shows that the proposed new simulation model has great potential for enhancing our understanding of the impacts of road traffic flow on bus bunching.

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“…Finally, the two-dimensional lattice Boltzmann model can be introduced to modeling multi-lane heterogeneous traffic, especially involving autonomous vehicles. 40…”

Section: Discussionmentioning

confidence: 99%

Bus bunching simulation based on the lattice Boltzmann model of traffic flow

Wang

Liu

2022

SIMULATION

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“…17 The rules of passenger movement in the cellular automata model [27][28][29] are relatively simple, and the model has the advantages of fast operation speed and strong real-time performance. Therefore, this study intends to use the cellular automata model 30,31 to describe the microscopic movement of passengers at the subway station.…”

Section: Introductionmentioning

confidence: 99%

Stair/escalator/elevator selection behavior of passengers in subway stations based on the fuzzy logic theory

et al. 2023

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The behavior of stair/escalator/elevator selection of passengers at the subway platform could directly affect the travel efficiency and even the service level of the station. This paper proposes a stair/escalator/elevator selection model of passengers at the subway platform, where the cellular automata model is used to simulate passenger movement, and the fuzzy logic theory is used to describe selection behavior. The distance to the stair/escalator/elevator and the density of passengers in front of the facility are selected as the main influence factors. The rationality and effectiveness of the selection model are verified by comparing the field data with simulation results of passengers choosing different stairs/escalators/elevators. The relationship between passenger quantity, passenger speed, escalator quantity, escalator location, and the traffic rate is analyzed based on simulation results, which can provide a theoretical reference for the facility layout optimization at the platform floor of the subway station.

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“…5 Cellular automata (CA) models can be used to simulate traffic performances. Singh and Ramachandra Rao 6 used the CA model to evaluate open and closed boundary simulations in heterogeneous non-lane-based traffic and included some features like seepage of small-sized vehicles in traffic.…”

Section: Introductionmentioning

confidence: 99%

Simulation of luggage-laden passengers’ behavior in the evacuation process based on a floor field CA model case study: Tehran metro-rail transfer corridor

et al. 2022

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Passengers’ safety against unexpected incidents such as rail stations’ fire accidents is essential in the safety field. The presence of luggage with passengers occupies extra space, diminishes passenger’s velocity in high densities, and consequently increases the evacuation time. Therefore, studying the mixture of luggage-laden passengers with non-luggage-laden passengers during the emergency evacuation time of a rail station is vital. In this paper, a simulation of a metro-rail transfer station using an extended cellular automata (CA) model is used to illustrate the importance of this consideration. In this model, luggage-laden passengers and non-luggage-laden passengers are defined as two-cell and one-cell groups, respectively. Specific parameters for luggage-laden passengers in minimum wall prevention and velocity are used. Also, the volume of each passenger group is extracted from the Wi-Fi scanners during the busiest time of the normal station operational hours due to metro and railway schedules. The simulation is carried out using the Python programming language. Fourteen scenarios that vary in their impact on the three classifications of station infrastructure, station equipment, and management’s approach are presented. The analysis indicates that approximately 28% of passengers, or 236 passengers, will not be evacuated in the time period predicted by the simulation if the luggage is not considered. Interestingly, resizing retail stores in the corridor reduced emergency evacuation times by 6.3%, the equivalent of removing them. Failures in the two escalators affect an 8% and 9.4% increase in emergency evacuation time and cause 28 and 46 more passengers to be trapped, respectively. Although the construction of the second railway entrance corridor has been suspended, results indicate that it will save 67 passengers and reduce evacuation time by 9.5%.

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

Cited by 5 publications

References 50 publications

Bus bunching simulation based on the lattice Boltzmann model of traffic flow

Bus bunching simulation based on the lattice Boltzmann model of traffic flow

Stair/escalator/elevator selection behavior of passengers in subway stations based on the fuzzy logic theory

Simulation of luggage-laden passengers’ behavior in the evacuation process based on a floor field CA model case study: Tehran metro-rail transfer corridor

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