Multi-stage Dynamic Coordination Model for Large-Scale Crowd's Activities Based on Multi-agent

Li, Jin; Wu, Qinmin; Dao-li, Zhu; Hu, Yihong

doi:10.1109/csie.2009.67

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…Randomly generating the destination for each agent could contribute to balance the geographical distribution of agents and avoid the concentration of agents in certain attractions [20]. Therefore, the NWA at each attraction is relatively small, as shown in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

The Impact of Information Sharing on Congestion Using Agent-Based Simulation

Zheng¹,

Mitsugi²,

Ren³

2014

Int. j. simul. model.

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The number of tourists to recreation areas has increased dramatically, leading to a growing concern about the congestion phenomena in these areas. Since the information provision has been introduced to reduce the congestion, its potential benefits as well as its drawbacks have been discussed controversially. In this work, we address a basic recreation area scenario with different patterns of information sharing and study the impact of them using agent-based simulation, where the tourists are modelled as agents. Three evaluation indicators are proposed to evaluate the performance of the strategies and both of the positive and negative of information sharing patterns are described.

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Section: Simulation Resultsmentioning

confidence: 99%

The Impact of Information Sharing on Congestion Using Agent-Based Simulation

Zheng¹,

Mitsugi²,

Ren³

2014

Int. j. simul. model.

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“…Zheng et al [30] evaluated the performance of three coordination approaches based on a multiagent simulation and believed that both the crowding situation and distance between each pair of rides should be taken into account when coordinating tourists' itineraries. In addition to crowding situations and distance, some studies have considered tourists' preferences [31,32]. Ahmadi [26] coordinated tourists' routing sequences by optimizing a ride's capacity and then generating the desired transition pattern.…”

Section: Crowding Mitigationmentioning

confidence: 99%

“…Organizers of large-scale exhibitions use two methods of tourist coordination control: (1) designing an overall route for tourists based on their preferences and group coordination or (2) deciding the next visiting stop for tourists based on a preferential selection mechanism [31]. Due to the complexity and interactivity of tourist movements in a theme park, the latter coordination control methods have shown greater promise for crowding mitigation in theme parks [31]. Most existing coordination mechanisms organize tourists' visit sequences based on the current crowd situation.…”

Section: 2mentioning

confidence: 99%

“…Coordinating tourist visit sequences step by step (also known as the single step dynamic coordination approach) has shown greater promise for crowding mitigation in theme parks than alternatives, due to the complexity and interactivity of tourist movements in a theme park [31]. Most step-by-step approaches coordinate tourist visit sequences based on the current crowd situation, while the prospective coordination approach proposed in this paper is based on predictions of tourist distributions.…”

Section: Performance Evaluationmentioning

confidence: 99%

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How to Mitigate Theme Park Crowding? A Prospective Coordination Approach

Yuan

Zheng

2018

Mathematical Problems in Engineering

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Crowding is a key factor in tourists’ experience in theme parks, and mitigating crowding makes parks more competitive. This study examines how to effectively mitigate crowding in theme parks. First, a Markov-based method is developed to predict the spatial-temporal distribution of tourists in the park. Then, a prospective coordination approach based on the tourist distribution prediction is proposed. To evaluate the performance of this approach, an experiment is constructed using an agent-based simulation platform. The results indicate that the proposed method significantly outperforms existing methods. Furthermore, we conduct two experiments and, based on the results, offer several recommendations for crowd management.

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“…diversion is likely to cause Matthew Effect. So Li Jin and others allocated the tourists to different routes with Logit Model and avoid the Mathew Effect to a great extent [16]. For the advantages of Logit Model, it can perfectly reflect the crowd and random factors during the route selection so as to realize the even space-time distribution of tourists and ensure the reasonable configuration of resources.…”

Section: Logit Strategymentioning

confidence: 99%

Evaluation Model Construction and Simulation Research on Tourist Diversion Strategy in Ecotourism Scenery Sport

Lin¹,

Yuan²,

Zheng³

et al. 2013

Appl. Math. Inf. Sci.

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Due to unbalanced distribution of tourists during peak time, tourists are likely to highly flock in certain areas of scenery spots if imperfect organization and management. For this reason, there may be crowds or even trample accidents in addition to unrecoverable damages for the ecological environment in such areas. Therefore, the valid solution is to formulate scientific tourist diversion strategy, reasonably adjust the tour routes, and realize the balanced distribution of tourists over space and time. The prerequisite and base of the research is to establish scientific rational evaluation indicators for diversion strategy. At present, the common indicators include the Variance Model reflecting the balance of load rate for each spot in the scenic region and the Gini-Simpson Index (G-S Index) showing the dispersion degrees of tourists among the scenery spots. However, these indexes highlight the opinions of scenery region managers but ignore the tourism utility during the travel. Such indexes can be used to evaluate the diversion strategy only from a macro view but hardly facilitates shaping any scientific valid suggestions for plan improvement. So this thesis established the evaluation model of tourism utility function (TUF) based on the spot congestion degree and compared the simulation effects of three diversion strategies respectively with three evaluation models, including Variance, G-S Index and TUF. It is found from the comparison results that TUF can better the overall evaluation of the diversion strategies and reveal the relevant inherent problems so as to provide reasonable and suggestions for scientific management on the scenery spot.

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Multi-stage Dynamic Coordination Model for Large-Scale Crowd's Activities Based on Multi-agent

Cited by 4 publications

References 7 publications

The Impact of Information Sharing on Congestion Using Agent-Based Simulation

The Impact of Information Sharing on Congestion Using Agent-Based Simulation

How to Mitigate Theme Park Crowding? A Prospective Coordination Approach

Evaluation Model Construction and Simulation Research on Tourist Diversion Strategy in Ecotourism Scenery Sport

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