Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm

Huang, Ling; Wong, S. C.; Zhang, Mengping; Shu, Chi‐Wang; Lam, William H. K.

doi:10.1016/j.trb.2008.06.003

Cited by 263 publications

(170 citation statements)

References 36 publications

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“…The two articles review existing studies and discuss various issues that are encountered in the modeling of pedestrian behavior including: (a) pedestrian micro-simulation methods; (b) pedestrian speeds at crossings and the associated behavior such as gap-acceptance behavior and compliance rates; and (c) pedestrian speeds on pavements. It is also noteworthy that apart from the empirical studies reviewed in Ishaque and Noland (2008) and Papadimitriou et al (2009), there is attention in the recent literature paid to the methodological research on pedestrian behavior, such as pedestrian walking behavior based on discrete choice (Antonini et al, 2006;Robin et al, 2009), pedestrian flow modeling (Huang et al, 2009), pedestrians route choice and activity scheduling via dynamic programming (Hoogendoorn and Bovy, 2004a, b), etc.…”

Section: Introductionmentioning

confidence: 99%

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

2013

Transportation Research Part B: Methodological

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For the purposes of both traffic-light control and the design of roadway layouts, it is important to understand pedestrian street-crossing behavior because it is not only crucial for improving pedestrian safety but also helps to optimize vehicle flow. This paper explores the mechanism of pedestrian street crossings during the red-man phase of traffic light signals and proposes a model for pedestrians' waiting times at signalized intersections. We start from a simplified scenario for a particular pedestrian under specific traffic conditions. Then we take into account the interaction between vehicles and pedestrians via statistical unconditioning. We show that this in general leads to a U-shaped distribution of the pedestrians' intended waiting time. This U-shaped distribution characterizes the nature of pedestrian street-crossing behavior, showing that in general there are a large proportion of pedestrians who cross the street immediately after arriving at the crossing point, and a large proportion of pedestrians who are willing to wait for the entire red-man phase. The U-shaped distribution is shown to reduce to a J-shaped or Lshaped distribution for certain traffic scenarios. The proposed statistical model was applied to analyze real field data.

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Section: Introductionmentioning

confidence: 99%

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

2013

Transportation Research Part B: Methodological

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“…Equation (8) contains the potential energy; because the nonlinear binary quadratic Eiknoal equation is to be solved, the general FDM cannot be applied. Therefore, in this case, the Godunov fast sweeping method is applied to solve the iterative equation [41]. Furthermore, in order to satisfy the stability of the partial difference equation, the increment ∆t should not be overly large.…”

Section: Methodsmentioning

confidence: 99%

Cluster Risk of Walking Scenarios Based on Macroscopic Flow Model and Crowding Force Analysis

Zhou

Feng

et al. 2018

Sustainability

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Abstract:In recent years, accidents always happen in confined space such as metro stations because of congestion. Various researchers investigated the patterns of dense crowd behaviors in different scenarios via simulations or experiments and proposed methods for avoiding accidents. In this study, a classic continuum macroscopic model was applied to simulate the crowded pedestrian flow in typical scenarios such as at bottlenecks or with an obstacle. The Lax-Wendroff finite difference scheme and artificial viscosity filtering method were used to discretize the model to identify high-density risk areas. Furthermore, we introduced a contact crowding force test of the interactions among pedestrians at bottlenecks. Results revealed that in the most dangerous area, the individual on the corner position bears the maximum pressure in such scenarios is 90.2 N, and there is an approximate exponential relationship between crowding force and density indicated by our data. The results and findings presented in this paper can facilitate more reasonable and precise simulation models by utilizing crowding force and crowd density and ensure the safety of pedestrians in high-density scenarios.

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“…Fluid dynamic models are the most recognized macroscopic models [52,53], which use the analogy of fluid dynamics to describe the crowd flow in the form of partial differential equations [54]. Fluid-dynamic models and social force models are continuous in space and time, compared to cellular automata models and lattice gas models, where space and time are discrete [22].…”

Section: Evacuation Models and Crowd Dynamicsmentioning

confidence: 99%

Integrating Decentralized Indoor Evacuation with Information Depositories in the Field

Zhao

Winter

Tomko

2017

IJGI

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Abstract:The lonelier evacuees find themselves, the riskier become their wayfinding decisions. This research supports single evacuees in a dynamically changing environment with risk-aware guidance. It deploys the concept of decentralized evacuation, where evacuees are guided by smartphones acquiring environmental knowledge and risk information via exploration and knowledge sharing by peer-to-peer communication. Peer-to-peer communication, however, relies on the chance that people come into communication range with each other. This chance can be low. To bridge between people being not at the same time at the same places, this paper suggests information depositories at strategic locations to improve information sharing. Information depositories collect the knowledge acquired by the smartphones of evacuees passing by, maintain this information, and convey it to other passing-by evacuees. Multi-agent simulation implementing these depositories in an indoor environment shows that integrating depositories improves evacuation performance: It enhances the risk awareness and consequently increases the chance that people survive and reduces their evacuation time. For evacuating dynamic events, deploying depositories at staircases has been shown more effective than deploying them in corridors.

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Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm

Cited by 263 publications

References 36 publications

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

Cluster Risk of Walking Scenarios Based on Macroscopic Flow Model and Crowding Force Analysis

Integrating Decentralized Indoor Evacuation with Information Depositories in the Field

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