A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events

Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is...

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Main Authors: Bao Guo, Hu Yang, Fan Zhang, Pu Wang
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/7096153
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author Bao Guo
Hu Yang
Fan Zhang
Pu Wang
author_facet Bao Guo
Hu Yang
Fan Zhang
Pu Wang
author_sort Bao Guo
collection DOAJ
description Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n-gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.
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institution Kabale University
issn 2042-3195
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Advanced Transportation
spelling doaj-art-5229c5f5657c468a83796a36010b32882025-02-03T01:06:50ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7096153A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding EventsBao Guo0Hu Yang1Fan Zhang2Pu Wang3School of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringShenzhen Institutes of Advanced TechnologySchool of Traffic and Transportation EngineeringPredicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n-gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.http://dx.doi.org/10.1155/2022/7096153
spellingShingle Bao Guo
Hu Yang
Fan Zhang
Pu Wang
A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
Journal of Advanced Transportation
title A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
title_full A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
title_fullStr A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
title_full_unstemmed A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
title_short A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events
title_sort hierarchical passenger mobility prediction model applicable to large crowding events
url http://dx.doi.org/10.1155/2022/7096153
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