Guest editorial for spatial agent-based models: current practices and future trends

Heppenstall, Alison; Crooks, Andrew

doi:10.1007/s10707-019-00349-y

Cited by 8 publications

(9 citation statements)

References 25 publications

(23 reference statements)

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“…Finding incentives to diffuse focused resources, the protocols to complete the first price auction, and local games for Nash equilibrium are among the common problems in theoretical aspect [37]. At present, extensive discussions, such as the use of big data, micro data, modeling, and adaptation of the behavior of agent models, with real environments and validation, are among the main challenges of spatial agent's researches [38]. Although, the use of sophisticated mathematical or probabilistic models simplifies spatial simulation, equally, it reduces the degree of credibility.…”

Section: Methodsmentioning

confidence: 99%

Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System

Eivazy

Malek

2019

IJGI

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Propagating crowdsourcing services via a wireless network can be an appropriate solution to using the potential of crowds in crisis management processes. The present study aimed to deploy crowdsourcing services properly to spatial urgent requests. Composing such atomic services can conquer sophisticated crisis management. In addition, the conducted propagated services guide people through crisis fields and allow managers to use crowd potential appropriately. The use of such services requires a suitable automated allocation method, along with a proper approach to arranging the sequence of propagating services. The solution uses a mathematical framework in the context of a GIS (Geospatial Information System) in order to construct an allocation approach. Solution elements are set out in a multi-agent environment structure, which simulate disaster field objects. Agents which are dynamically linked to objects in a crisis field, interact with each other in a competitive environment, and the results in forming crowdsourcing services are used to guide crowds in the crisis field via the crowdsourcing services. The present solution was implemented through a proper data schema in a powerful geodatabase, along with various users with specialized interfaces. Finally, a solution and crowdsourcing service was tested in the context of a GIS in the 2019 Aqala flood disaster in Iran and other complement scenarios. The allocating performance and operation of other system elements were acceptable and reduced indicators, such as rescuer fatigue and delay time. Crowdsourcing service was positioned well in the solution and provided good performance among the elements of the Geospatial Information System.

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Section: Methodsmentioning

confidence: 99%

Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System

Eivazy

Malek

2019

IJGI

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“…That heterogeneous pattern is typically governed by the underlying fabric, such as socioeconomic or demographic structure in urban phenomena. The integration of spatial effects in transportation planning and modeling remains to be an unbeaten track (Lopes, Brondino, & Silva, 2014), and a challenge to areas of advanced modeling, e.g., Spatial Agent-Based Models, in which contextualizing the spatial effect in the agents set of rules and relationships still remains a challenge (Heppenstall &Crooks, ISSN 2520-2979 Journal of Sustainable Development of Transport andLogistics, 6(2), 2021 ‹ 9 › 2019), especially in shared mobility and ride-sourcing context (Kelleny & Ishak, 2018). Even for conventional transportation models, ecological fallacy and spatial dependence were amongst the key methodological issues discussed (Wang, Quddus, Ryley, Enoch, & Davison, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring and visualizing spatial effects and patterns in ride-sourcing trip demand and characteristics

Kelleny

Ishak

2021

J. sustain. dev. transp. logist.

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The complex demand pattern of ride-sourcing remains to be a challenge to transportation modeling practitioners due to the infancy and the inherently dynamic nature of the ride-sourcing system. Spatial effects exploration and analysis protocols can provide informative insights on the underlying structure of demand and trip characteristics. Those protocols can be thought of as an opportunistic strategy to alleviate the complexity and help specifying the appropriate econometric models for the system. Spatial effects exploration is comparable to point pattern analysis, in which, signals from spatial entities, like census tracts, can be analyzed statistically to reveal whether a specific phenomenon respective signal distribution is a completely random process or if it follows some regular pattern. The results of such analysis help to explore the investigated phenomenon and conceptualize its causal forces. In this paper, we apply spatial pattern analysis edge methods integrated into a visual analytics framework to: (1) test the null hypothesis of system demand complete randomness; (2) further analyze and explain this demand in terms of the origindestination (OD) flow and trips characteristics, i.e., length and duration; and (3) develop a pattern profile of the demand and trip characteristics to provide potential directions to modeling and predictive analytics approaches. This framework helps explain the ride-sourcing system demand and trip characteristics in space and time to fill the gap in integrating the system in multimodal transportation frameworks. We use the ride-sourcing trip dataset released from the City of Chicago, USA, for the year 2019 to showcase the proposed methods and their novelty in capturing such effects as well as explaining the Article history:

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“…ABMs are now well-established in GIScience 17,[26][27][28] . They are also increasingly being accepted as a powerful tool that allows for the explicit representation of the processes underlying large-scale animal movement patterns [29][30][31][32][33] .…”

Section: Proposing Agents For Movement Modellingmentioning

confidence: 99%

A Vector-agent Approach to (Spatiotemporal) Movement Modelling and Reasoning

Rahimi¹,

Moore²,

Whigham³

2021

Preprint

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Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space-time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented in this article as an example. The movement of football players was chosen as it is appropriately constrained in possible space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players’ role-areas) were manipulated. In conclusion, ABMs can contribute to our understanding of movement and how causally-relevant evidence could be produced, through a proposed agent equipped with active causal knowledge.

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Guest editorial for spatial agent-based models: current practices and future trends

Cited by 8 publications

References 25 publications

Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System

Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System

Exploring and visualizing spatial effects and patterns in ride-sourcing trip demand and characteristics

A Vector-agent Approach to (Spatiotemporal) Movement Modelling and Reasoning

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