Social connections between people influence how they behave and where they go; however, such networks are rarely incorporated in agent-based models of disaster. To address this, we introduce a novel synthetic population method which specifically creates social relationships. This synthetic population is then used to instantiate a geographically explicit agent-based model for the New York megacity region which captures pre- and post- disaster behaviors. We demonstrate not only how social networks can be incorporated into models of disaster but also how such networks can impact decision making, opening up a variety of new application areas where network structures matter in urban settings.
{"title":"Integrating social networks into large-scale urban simulations for disaster responses","authors":"Na Jiang, Annetta Burger, A. Crooks, W. Kennedy","doi":"10.1145/3423335.3428168","DOIUrl":"https://doi.org/10.1145/3423335.3428168","url":null,"abstract":"Social connections between people influence how they behave and where they go; however, such networks are rarely incorporated in agent-based models of disaster. To address this, we introduce a novel synthetic population method which specifically creates social relationships. This synthetic population is then used to instantiate a geographically explicit agent-based model for the New York megacity region which captures pre- and post- disaster behaviors. We demonstrate not only how social networks can be incorporated into models of disaster but also how such networks can impact decision making, opening up a variety of new application areas where network structures matter in urban settings.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128706218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","authors":"","doi":"10.1145/3423335","DOIUrl":"https://doi.org/10.1145/3423335","url":null,"abstract":"","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The trajectory of traffic participants is an essential source for pattern mining and knowledge discovery in urban mobility. However, real-world trajectory data are often not publicly available due to privacy concerns or intellectual property constraints. Although some simulators or synthetic trajectory datasets have been proposed, many of them only consider the spatial-temporal aspects of the trajectory data, but ignore other contextual information that could impact trajectories. On one hand, trajectories are usually associated with and affected by user profiles (e.g., a person's daily routines and preferred modes of transportation). On the other hand, an individual's movements are also affected by environmental conditions and interactions with other traffic participants, particularly in urban scenarios (e.g., routing choices due to congestion or road conditions). Such contextual trajectories provide a more realistic representation of the mobility patterns of traffic participants. Due to the lack of such datasets or trace generators, this work presents ConTraSim (Contextual Trajectory Simulation), a novel approach for generating contextual trajectories based on the Simulation of Urban Mobility (SUMO) traffic simulator. More specifically, the proposed approach is designed to produce GPS traces annotated by contextual information that mimic the movements of multiple types of traffic participants in urban areas. As a case study, we also generate a sample dataset using the proposed method and compare it to real-world data to demonstrate how well the synthetic data reflects real-world data characteristics.
{"title":"Generating contextual trajectories from user profiles","authors":"Jian Yang, C. Poellabauer, Pramita Mitra, Abhishek Sharma, Cynthia Neubecker, Arpita Chand","doi":"10.1145/3423335.3428166","DOIUrl":"https://doi.org/10.1145/3423335.3428166","url":null,"abstract":"The trajectory of traffic participants is an essential source for pattern mining and knowledge discovery in urban mobility. However, real-world trajectory data are often not publicly available due to privacy concerns or intellectual property constraints. Although some simulators or synthetic trajectory datasets have been proposed, many of them only consider the spatial-temporal aspects of the trajectory data, but ignore other contextual information that could impact trajectories. On one hand, trajectories are usually associated with and affected by user profiles (e.g., a person's daily routines and preferred modes of transportation). On the other hand, an individual's movements are also affected by environmental conditions and interactions with other traffic participants, particularly in urban scenarios (e.g., routing choices due to congestion or road conditions). Such contextual trajectories provide a more realistic representation of the mobility patterns of traffic participants. Due to the lack of such datasets or trace generators, this work presents ConTraSim (Contextual Trajectory Simulation), a novel approach for generating contextual trajectories based on the Simulation of Urban Mobility (SUMO) traffic simulator. More specifically, the proposed approach is designed to produce GPS traces annotated by contextual information that mimic the movements of multiple types of traffic participants in urban areas. As a case study, we also generate a sample dataset using the proposed method and compare it to real-world data to demonstrate how well the synthetic data reflects real-world data characteristics.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moving region is an abstraction used to represent the spatio-temporal behavior of real-world phenomena in database systems. The most common approach to model moving regions uses geometries to represent their position and shape at different times (observations), and interpolation functions to generate the evolution of the geometries between observations. Several region interpolation methods have been proposed in the databases literature, but as there is no suitable method for all use cases, users must select the most adequate algorithm to represent each region by visual inspection. This can be infeasible when dealing with large datasets. This paper presents the first steps towards a system that suggests which methods (and configurations) can generate representations fitting the requirements of a particular application. It includes an abstract specification of user-defined rules on the spatio-temporal evolution of moving regions to assess the suitability of region interpolation functions, a discussion on optimization strategies for efficient implementation of the rules and illustrative examples using real-world data to show how to use this approach to select the best methods to represent a spatio-temporal phenomena.
{"title":"Towards the automatic selection of moving regions representation methods","authors":"R. L. C. Costa, Enrico S. Miranda, José Moreira","doi":"10.1145/3423335.3428170","DOIUrl":"https://doi.org/10.1145/3423335.3428170","url":null,"abstract":"Moving region is an abstraction used to represent the spatio-temporal behavior of real-world phenomena in database systems. The most common approach to model moving regions uses geometries to represent their position and shape at different times (observations), and interpolation functions to generate the evolution of the geometries between observations. Several region interpolation methods have been proposed in the databases literature, but as there is no suitable method for all use cases, users must select the most adequate algorithm to represent each region by visual inspection. This can be infeasible when dealing with large datasets. This paper presents the first steps towards a system that suggests which methods (and configurations) can generate representations fitting the requirements of a particular application. It includes an abstract specification of user-defined rules on the spatio-temporal evolution of moving regions to assess the suitability of region interpolation functions, a discussion on optimization strategies for efficient implementation of the rules and illustrative examples using real-world data to show how to use this approach to select the best methods to represent a spatio-temporal phenomena.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127876805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Used for route choice modeling by the transportation research community, recursive logit is a form of inverse reinforcement learning, the field of learning an agent's objective by observing it's behavior. By solving a large-scale system of linear equations it allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of IRL models applied to real world travel trajectories and look at some of the challenges with recursive logit for modeling bicycle route choice in the city center area of Amsterdam.
{"title":"A review of methods to model route choice behavior of bicyclists: inverse reinforcement learning in spatial context and recursive logit","authors":"Thomas Koch, E. Dugundji","doi":"10.1145/3423335.3428165","DOIUrl":"https://doi.org/10.1145/3423335.3428165","url":null,"abstract":"Used for route choice modeling by the transportation research community, recursive logit is a form of inverse reinforcement learning, the field of learning an agent's objective by observing it's behavior. By solving a large-scale system of linear equations it allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of IRL models applied to real world travel trajectories and look at some of the challenges with recursive logit for modeling bicycle route choice in the city center area of Amsterdam.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128885631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban land markets exhibit complex emergent behaviors that have yet to be fully explained by the microeconomic decision-making which constitutes the market. The Agent-based Land MArket (ALMA) framework has been introduced to simulate a bilateral agent-based land market that produces a rent gradient. In this paper, we extend the ALMA framework by introducing two new parameters, heterogeneity, and stochasticity which allow us to explore how the rent gradient is affected by buyers with diverse preferences and a range of market compositions.
{"title":"An exploration of the effect of buyer preference and market composition on the rent gradient using the ALMA framework","authors":"Alexander Michels, Jeon-Young Kang, Shaowen Wang","doi":"10.1145/3423335.3428167","DOIUrl":"https://doi.org/10.1145/3423335.3428167","url":null,"abstract":"Urban land markets exhibit complex emergent behaviors that have yet to be fully explained by the microeconomic decision-making which constitutes the market. The Agent-based Land MArket (ALMA) framework has been introduced to simulate a bilateral agent-based land market that produces a rent gradient. In this paper, we extend the ALMA framework by introducing two new parameters, heterogeneity, and stochasticity which allow us to explore how the rent gradient is affected by buyers with diverse preferences and a range of market compositions.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129577935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban street space is often contested with competition and coordination between multiple road users and transport modes. Understanding how pedestrians decide to enter the carriageway can help inform how to plan and manage street space and is increasingly relevant as autonomous vehicles mature. In this paper, we present a description of pedestrian road crossing behaviour using a novel sequential sampling modelling approach implemented in a spatial agent-based model. The model explicitly represents the gradual process of deliberation between discrete road crossing choice alternatives resulting in a choice of crossing location. The model reproduces some characteristic pedestrian road crossing behaviours such as trade offs between vehicle exposure and journey time, non-compliant crossing (jaywalking), and dependence of crossing choice on the proximity of crossing alternatives.
{"title":"A sequential sampling model of pedestrian road crossing choice","authors":"Obi Thompson Sargoni, E. Manley","doi":"10.1145/3423335.3428163","DOIUrl":"https://doi.org/10.1145/3423335.3428163","url":null,"abstract":"Urban street space is often contested with competition and coordination between multiple road users and transport modes. Understanding how pedestrians decide to enter the carriageway can help inform how to plan and manage street space and is increasingly relevant as autonomous vehicles mature. In this paper, we present a description of pedestrian road crossing behaviour using a novel sequential sampling modelling approach implemented in a spatial agent-based model. The model explicitly represents the gradual process of deliberation between discrete road crossing choice alternatives resulting in a choice of crossing location. The model reproduces some characteristic pedestrian road crossing behaviours such as trade offs between vehicle exposure and journey time, non-compliant crossing (jaywalking), and dependence of crossing choice on the proximity of crossing alternatives.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128820517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since 2011, Syria has experienced episodes of large-scale conflict and violence. Syrian Civil War has exposed the global society to increasingly flow of refugees who are forcibly displaced from their homes due to persecution, conflict, violence, or human rights violations. The inconsistent responses from humanitarian organisations and governments indicate lack of robust framework to assess and evaluate policy responses. In this article, we examine the effect of border control policies via a data-driven agent-based model of refugee flows from Syria. We implement policy scenarios and investigate the consequences in the flow pattern in terms of measuring the size of flows and the potential destinations. We demonstrate that the spatial configuration of borders intended to be closed directly influence the consequences of the corresponding closure policy.
{"title":"Spatial analysis of border closure intervention scheme in conflict-induced displacement","authors":"Zahra Jafari, Toby P Davies, Shane D. Johnson","doi":"10.1145/3423335.3428162","DOIUrl":"https://doi.org/10.1145/3423335.3428162","url":null,"abstract":"Since 2011, Syria has experienced episodes of large-scale conflict and violence. Syrian Civil War has exposed the global society to increasingly flow of refugees who are forcibly displaced from their homes due to persecution, conflict, violence, or human rights violations. The inconsistent responses from humanitarian organisations and governments indicate lack of robust framework to assess and evaluate policy responses. In this article, we examine the effect of border control policies via a data-driven agent-based model of refugee flows from Syria. We implement policy scenarios and investigate the consequences in the flow pattern in terms of measuring the size of flows and the potential destinations. We demonstrate that the spatial configuration of borders intended to be closed directly influence the consequences of the corresponding closure policy.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Grajdura, Sachraa G. Borjigin, Debbie A. Niemeier
Fast-moving wildfires pose difficult modeling challenges, including accounting for heterogeneity in individual evacuee behavior and capturing a complex changing system across time dimensions. Here, we employ a NetLogo agent-based model that enables the development of behavioral models for nearest shelter evacuations using origin information. We use GIS shape files (i.e., road network, building blocks etc.) and the spatiotemporal wildfire dynamics (wind speed, direction and possibility of spread) to support our analysis. Our framework is capable of generating various wildfire scenarios that capture the overall evacuation processes. We can use the simulations to demonstrate the feasibility of agent-based models and to compare them under different fire evacuation scenarios.
{"title":"Agent-based wildfire evacuation with spatial simulation: a case study","authors":"S. Grajdura, Sachraa G. Borjigin, Debbie A. Niemeier","doi":"10.1145/3423335.3428169","DOIUrl":"https://doi.org/10.1145/3423335.3428169","url":null,"abstract":"Fast-moving wildfires pose difficult modeling challenges, including accounting for heterogeneity in individual evacuee behavior and capturing a complex changing system across time dimensions. Here, we employ a NetLogo agent-based model that enables the development of behavioral models for nearest shelter evacuations using origin information. We use GIS shape files (i.e., road network, building blocks etc.) and the spatiotemporal wildfire dynamics (wind speed, direction and possibility of spread) to support our analysis. Our framework is capable of generating various wildfire scenarios that capture the overall evacuation processes. We can use the simulations to demonstrate the feasibility of agent-based models and to compare them under different fire evacuation scenarios.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128529081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information, ideas, and diseases, or more generally, contagions, spread over time and space through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a detailed network structure and individual diffusion pathways are obtained. Studying such diffusion networks provides valuable insights to understand important actors in carrying and spreading contagions and to help predict occurrences of new infections. Most prior research focuses on modeling diffusion process only in the temporal dimension. The advent of rich social, media and geo-tagged data now allows us to study and model this diffusion process in both temporal and spatial dimensions than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall spatiotemporal process is difficult to trace. This propagation is continuous over time and space, where individual transmissions occur at different rates via complex and latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion simulation (STAND) is developed based on the survival model in this research. Both time and geographic distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes denote geographic locations at a large scale.
{"title":"STAND: a spatio-temporal algorithm for network diffusion simulation","authors":"Fangcao Xu, B. Desmarais, D. Peuquet","doi":"10.1145/3423335.3428164","DOIUrl":"https://doi.org/10.1145/3423335.3428164","url":null,"abstract":"Information, ideas, and diseases, or more generally, contagions, spread over time and space through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a detailed network structure and individual diffusion pathways are obtained. Studying such diffusion networks provides valuable insights to understand important actors in carrying and spreading contagions and to help predict occurrences of new infections. Most prior research focuses on modeling diffusion process only in the temporal dimension. The advent of rich social, media and geo-tagged data now allows us to study and model this diffusion process in both temporal and spatial dimensions than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall spatiotemporal process is difficult to trace. This propagation is continuous over time and space, where individual transmissions occur at different rates via complex and latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion simulation (STAND) is developed based on the survival model in this research. Both time and geographic distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes denote geographic locations at a large scale.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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