Guided crowd evacuation: approaches and challenges

Zhou, Min; Dong, Hairong; Ioannou, Pétros; Zhao, Yanbo; Wang, Fei‐Yue

doi:10.1109/jas.2019.1911672

Cited by 79 publications

(44 citation statements)

References 114 publications

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“…In formula (12), θ is the randomness of the description model; q O D is the passenger flow between OD pairs; f O D k is the distribution flow of the k-path between OD pairs; and V O D k is the cumulative prospect value of the k-path between OD pairs. is paper uses the MSA algorithm, and the algorithm steps are as follows:…”

Section: Construction Of the Multipath Probability Allocationmentioning

confidence: 99%

“…However, it is difficult to predict the selection behavior of urban rail transit passengers under emergency conditions, and it is difficult to conduct in-depth research with traditional models and methods. Research on rail transit passenger flow under emergencies mostly focuses on the prediction, propagation, evacuation, etc., of emergent passenger flow [6][7][8][9][10][11][12][13]. Current research mostly analyzes the impact of sudden passenger flow on urban rail operation from the perspective of train operation and passenger transportation organization, whereas relatively few studies have been conducted on the prediction and early warning of the temporal and spatial distributions of passenger flow in rail transit emergencies.…”

Section: Introductionmentioning

confidence: 99%

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CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Zhou

Dong

2020

Journal of Advanced Transportation

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Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures.

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Section: Construction Of the Multipath Probability Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Zhou

Dong

2020

Journal of Advanced Transportation

Self Cite

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“…The growth of ridership in an urban metro is regarded as a good sign for the mitigation of traffic congestion [6]. However, large passenger flows especially those unexpected will pose great pressure to the operation of the urban metro [7, 8]. Anomalous large passenger flows may also increase the possibility of train malfunctions [9] and threaten the safety of passengers.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model for predicting anomalous large passenger flow in urban metros

Zheng

Ling

Wang

et al. 2020

IET intell. transp. syst

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Machine learning models have been widely adopted for passenger flow prediction in urban metros; however, the authors find machine learning models may underperform under anomalous large passenger flow conditions. In this study, they develop a prediction framework that combines the advantage of complex network models in capturing the collective behaviour of passengers and the advantage of online learning algorithms in characterising rapid changes in real-time data. The proposed method considerably improves the accuracy of passenger flow prediction under anomalous conditions. This study can also serve as an exploration of interdisciplinary methods for transportation research.

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“…e application of the proposed method can reduce the amount of traffic information that needs to be collected significantly at the expense of a slight loss in prediction accuracy. Here, we introduce some related research [22][23][24]. An approach was proposed to exploit the spatial-temporal causality among travel speeds of road segments by a time-lagged correlation coefficient function and utilize the local stationarity of correlation coefficient to estimate the travel speeds of road segments to handle the problem of missing travel speed values of vehicles on some road segments, due to the coarseness of vehicular crowdsensing data [22].…”

Section: Introductionmentioning

confidence: 99%

Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

Zhou

et al. 2020

Journal of Advanced Transportation

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Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

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Guided crowd evacuation: approaches and challenges

Cited by 79 publications

References 114 publications

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Hybrid model for predicting anomalous large passenger flow in urban metros

Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

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