How does a static measure influence passengers’ boarding behaviors and bus dwell time? Simulated evidence from Nanjing bus stations

Ji, Yanjie; Gao, Liangpeng; Chen, Dandan; Ma, Xinwei; Zhang, Ruochen

doi:10.1016/j.tra.2018.02.003

Cited by 18 publications

(13 citation statements)

References 29 publications

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“…Based on all of the calculations above, the updated velocity of the auto-agent i is expressed by Equation (14).…”

Section: Classic Social Force Modelmentioning

confidence: 99%

“…According to Mehdi et al (2011), the desired velocity direction of an individual is the tangential direction of the path taking into account the existence of other individuals and obstacles, and the desired velocity of an individual does not exceed a certain fixed value [45]. Ji et al (2018) inserted a direction rule into the social force model and combined the model with a multi-agent concept. They built a simulation for the alighting and boarding process of buses [14].…”

Section: Introductionmentioning

confidence: 99%

“…Ji et al (2018) inserted a direction rule into the social force model and combined the model with a multi-agent concept. They built a simulation for the alighting and boarding process of buses [14].…”

Section: Introductionmentioning

confidence: 99%

“…After that, many other scholars also confirmed this in their studies [5,6,8] and found many other environmental factors that influence the alighting and boarding process. The type as well as the layout of the vehicle and the platform all have an effect on the alighting and boarding time [9][10][11][12][13][14]. In addition, the door width and the number of doors can also influence the alighting and boarding process time [5,10,[15][16][17][18][19][20].…”

mentioning

confidence: 99%

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Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China

Gao

et al. 2019

Sustainability

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Since the long dwell time and chaotic crowds make metro trips inefficient and dissatisfying, the importance of optimizing alighting and boarding processes has become more prominent. This paper focuses on the adjustment of passenger organizing modes. Using field data from the metro station in Nanjing, China, a micro-simulation model of alighting and boarding processes based on an improved social force paradigm was built to simulate the movement of passengers under different passenger organizing modes. Unit flow rate, delay, and social force work (SFW) jointly reflect the efficiency and, especially, the physical energy consumption of passengers under each mode. It was found that when passengers alighted and boarded by different doors, efficiency reached its optimal level which was 76.92% higher than the status quo of Nanjing, and the physical energy consumption was reduced by 16.30%. Both the findings and the model can provide support for passenger organizing in metro stations, and the concept of SFW can be applied to other scenes simulated by the social force model, such as evacuations of large-scale activities, to evaluate the physical energy consumption of people.

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“…Based on all of the calculations above, the updated velocity of the auto-agent i is expressed by Equation (14).…”

Section: Classic Social Force Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China

Gao

et al. 2019

Sustainability

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“…Wu et al [38] modeled bus bunching and holding control with vehicle overtaking and distributed passenger boarding behavior. Ji et al [39] provided a simulation model based on the social force paradigm and incorporated five different forces that drive individual agents' boarding and alighting.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

et al. 2018

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This paper proposes an equilibrium bus boarding model to investigate optimal pricing and service for peak-period bus commuting inefficiency of boarding queuing congestion. Commuters are assumed to choose their optimal time-of-use decision from home or the workplace to the bus. We found that: (1) when the earliest commuter boards the bus as soon as the bus arrives at the bus station, the dynamic boarding queuing congestion toll that eliminates the boarding queuing congestion creates social optimal equilibrium and the optimal bus departure interval during the peak period; (2) the optimal bus departure interval during the peak period is the time that the preceding bus riders spend on boarding, which means the relationship between service frequency and ridership does not conform to the square root principle: the optimal bus frequency is proportional to the square root of the number of commuters.

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Effect of Land Use Pattern on Bus Blockage Duration at Curb Side Bus Stops

Padinjarapat,

Othayoth,

Krishna Rao

et al. 2023

Lecture Notes in Civil Engineering

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How does a static measure influence passengers’ boarding behaviors and bus dwell time? Simulated evidence from Nanjing bus stations

Cited by 18 publications

References 29 publications

Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China

Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

Effect of Land Use Pattern on Bus Blockage Duration at Curb Side Bus Stops

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