scholarly journals An Initial Agent-Based Model for E-scooter Accommodation into Existing Urban Streetscapes

2021 ◽  
Author(s):  
Hannah Gumble ◽  
Sarah Wise

New forms of mobility reshape the transportation landscape, changing movement for both their users and others in the environment. The transition period during which novel forms of travel are being explored can be a challenging time while the use of spaces must be renegotiated. E-scooters, which have recently been more widely introduced to the UK, are experiencing such a moment as riders, planners, and other users of the streetscape are determining what role this technology will play in communities. The data gaps surrounding e-scooters can make this an especially difficult question for planners because of the cost of gathering relevant observational data, much of which is held under private company ownership. In light of this, this work presents an agent-based model developed to examine the integration of e-scooters into existing streetscapes. Agent-based models explore phenomena through focusing on individual behaviour and rules which in turn gives rise to emergent large scale patterns. These patterns can be dissected and interrogated with a variety of tools, allowing us to tease out individual as well as group experiences of different scenarios. An agent-based approach allows us to capture the individual behaviours of e-scooter users and those of cyclists, drivers of variously sized vehicles, pedestrians, and others present in the environment. By focusing on the interactions of these various street users, we can explore how different approaches to e-scooter integration may fare relative to varying street configurations. Their decision frameworks are informed by observational studies of e-scooter users in order to augment the available data. We discuss the current state of understanding e-scooter behaviour and the potential modelling applications, present an initial behavioural framework of e-scooter decision making and inter-modal interactions, and highlight some preliminary results examining the differences between e-scooters operating on roads versus shared segregated cycle lanes. The work concludes with a case study comparing two modelled scenarios, one including a segregated cycle lane and one without. Drawing upon metrics such as the route segmentation/ cut-off rate and average travel comfort, we can more precisely explore how new forms of mobility will influence different kinds of street users in order to better understand the trade-offs associated with different paths forward.

2020 ◽  
Author(s):  
Junjiang Li ◽  
Philippe J. Giabbanelli

AbstractThere is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identify which interventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 (Covasim) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.


2019 ◽  
pp. 1-20
Author(s):  
Ermanno Catullo ◽  
Federico Giri ◽  
Mauro Gallegati

The paper presents an agent-based model reproducing a stylized credit network that evolves endogenously through the individual choices of firms and banks. We introduce in this framework a financial stability authority in order to test the effects of different prudential policy measures designed to improve the resilience of the economic system. Simulations show that a combination of micro- and macroprudential policies reduces systemic risk but at the cost of increasing banks’ capital volatility. Moreover, the agent-based methodology allows us to implement an alternative meso regulatory framework that takes into consideration the connections between firms and banks. This policy targets only the more connected banks, increasing their capital requirement in order to reduce the diffusion of local shocks. Our results support the idea that the mesoprudential policy is able to reduce systemic risk without affecting the stability of banks’ capital structure.


2003 ◽  
Vol 06 (03) ◽  
pp. 331-347 ◽  
Author(s):  
YUTAKA I. LEON SUEMATSU ◽  
KEIKI TAKADAMA ◽  
NORBERTO E. NAWA ◽  
KATSUNORI SHIMOHARA ◽  
OSAMU KATAI

Agent-based models (ABMs) have been attracting the attention of researchers in the social sciences, becoming a prominent paradigm in the study of complex social systems. Although a great number of models have been proposed for studying a variety of social phenomena, no general agent design methodology is available. Moreover, it is difficult to validate the accuracy of these models. For this reason, we believe that some guidelines for ABMs design must be devised; therefore, this paper is a first attempt to analyze the levels of ABMs, identify and classify several aspects that should be considered when designing ABMs. Through our analysis, the following implications have been found: (1) there are two levels in designing ABMs: the individual level, related to the design of the agents' internal structure, and the collective level, which concerns the design of the agent society or macro-dynamics of the model; and (2) the mechanisms of these levels strongly affect the outcomes of the models.


2017 ◽  
Vol 132 ◽  
pp. 91-103 ◽  
Author(s):  
Christian Grovermann ◽  
Pepijn Schreinemachers ◽  
Suthathip Riwthong ◽  
Thomas Berger

2021 ◽  
Author(s):  
Maria Coto-Sarmiento ◽  
Simon Carrignon

The goal of this study is to analyse the transmission of technical skills among potters within the Roman Empire. Specifically, our case study has been focused on the production processes based on Baetica province (currently Andalusia) from 1st to 3rd century AD. Variability of material culture allows observing different production patterns that can explain how social learning evolves. Some differences can be detected in the making techniques processes through time and space that might explain different degrees of specialization. Unfortunately, it is extremely difficult to identify some evidence of social learning strategies in the archaeological record. In Archaeology, this process has been analysed by the study of the production of handmade pottery. In our case, we want to know if the modes of transmission could be similar with a more standardized production as Roman Age. We propose here an Agent-Based Model to compare different cultural processes of learning transmission. Archaeological evidence will be used to design the model. In this model, we implement a simple mechanism of pottery production with different social learning processes under different scenarios. In particular, the aim of this study is to quantify which one of those processes explain better the copying mechanisms among potters revealed in our dataset. We believe that the model presented here can provide a strong baseline for the exploration of transmission processes related to large-scale production.


PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246788
Author(s):  
Simon J. Lloyd ◽  
Zaid Chalabi

Undernutrition is a major contributor to the global-burden of disease, and global-level health impact models suggest that climate change-mediated reductions in food quantity and quality will negatively affect it. These models, however, capture just some of the processes that will shape future nutrition. We adopt an alternative standpoint, developing an agent-based model in which producer-consumer smallholders practice different ‘styles of farming’ in the global food system. The model represents a hypothetical rural community in which ‘orphan’ (subsistence) farmers may develop by adopting an ‘entrepreneurial’ style (highly market-dependent) or by maintaining a ‘peasant’ style (agroecology). We take a first look at the question: how might patterns of farming styles—under various style preference, climate, policy, and price transmission scenarios—impact on hunger and health-supporting conditions (incomes, work, inequality, ‘real land productivity’) in rural areas? imulations without climate change or agricultural policy found that style preference patterns influence production, food price, and incomes, and there were trade-offs between them. For instance, entrepreneurial-oriented futures had the highest production and lowest prices but were simultaneously those in which farms tended towards crisis. Simulations with climate change and agricultural policy found that peasant-orientated agroecology futures had the highest production, prices equal to or lower than those under entrepreneurial-oriented futures, and better supported rural health. There were, however, contradictory effects on nutrition, with benefits and harms for different groups. Collectively the findings suggest that when attempting to understand how climate change may impact on future nutrition and health, patterns of farming styles—along with the fates of the households that practice them—matter. These issues, including the potential role of peasant farming, have been neglected in previous global-level climate-nutrition modelling but go to the heart of current debates on the future of farming: thus, they should be given more prominence in future work.


SIMULATION ◽  
2020 ◽  
Vol 96 (8) ◽  
pp. 655-678 ◽  
Author(s):  
Imran Mahmood ◽  
Quair-tul-ain ◽  
Hasan Arshad Nasir ◽  
Fahad Javed ◽  
José A Aguado

Analyzing demand behavior of end consumers is pivotal in long term energy planning. Various models exist for simulating household load profiles to cater different purposes. A macroscopic viewpoint necessitates modeling of a large-scale population at an aggregate level, whereas a microscopic perspective requires measuring loads at a granular level, pertinent to the individual devices of a household. Both aspects have lucrative benefits, instigating the need to combine them into a modeling framework which allows model scalability and flexibility, and to analyze domestic electricity consumption at different resolutions. In this applied research, we propose a multi-resolution agent-based modeling and simulation (ABMS) framework for estimating domestic electricity consumption. Our proposed framework simulates per minute electricity consumption by combining large neighborhoods, the behavior of household individuals, their interactions with the electrical appliances, their sociological habits and the effects of exogenous conditions such as weather and seasons. In comparison with the existing energy models, our framework uniquely provides a hierarchical, multi-scale, multi-resolution implementation using a multi-layer architecture. This allows the modelers flexibility in order to model large-scale neighborhoods at one end, without any loss of expressiveness in modeling microscopic details of individuals’ activities at house level, and energy consumption at the appliance level, at the other end. The validity of our framework is demonstrated using a case study of 264 houses. A validated ABMS framework will support: (a) Effective energy planning; (b) Estimation of the future energy demand; (c) and the analysis of the complex dynamic behavior of the consumers.


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