2020
DOI: 10.1109/access.2020.2972079
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A Fuzzy-Theory-Based Social Force Model for Studying the Impact of Alighting Area Width on Alighting and Boarding Behaviors

Abstract: Alighting and boarding efficiency (A&Be) of passengers plays an important role in the formulation of subway timetable. This paper studies the impact of alighting area width on A&Be based on the improved social force model. This improved model adopts the fuzzy logic theory considering factors of train dwell time and passengers ahead to determine the desired speeds of passengers instead of setting fixed values. The t-test method verifies the validity of the model. The passenger movement dynamics characterized by… Show more

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Cited by 5 publications
(3 citation statements)
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References 36 publications
(40 reference statements)
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“…The classical centroid defuzzification method 48 is adopted as shown in Equation (12), which converts the fuzzy output probability p into the accurate value of the probability of passengers selecting each stair/escalator/elevator p * :…”
Section: Modelmentioning
confidence: 99%
“…The classical centroid defuzzification method 48 is adopted as shown in Equation (12), which converts the fuzzy output probability p into the accurate value of the probability of passengers selecting each stair/escalator/elevator p * :…”
Section: Modelmentioning
confidence: 99%
“…Li et al use social force to proposal region [6] and Sumon et al add SFM into CNN and LSTM for violent crowd flow detection [7]. SFM analyses the particle trajectory and can be applied for multi-pedestrian interaction [47], separating crowd behaviour [48], alighting and boarding behaviours [49], and evacuation assistant [50]. Qi Wang et al learn pixel-wise features with spatial CNN for crowd under-standing [51].…”
Section: Social Force Modelmentioning
confidence: 99%
“…add SFM into CNN and LSTM for violent crowd flow detection [7]. SFM analyses the particle trajectory and can be applied for multi‐pedestrian interaction [47], separating crowd behaviour [48], alighting and boarding behaviours [49], and evacuation assistant [50]. Qi Wang et al.…”
Section: Related Workmentioning
confidence: 99%