Sensitivity Testing with the Oregon Statewide Integrated Model

Weidner, Tara; Knudson, Becky; Picado, Rosella; Hunt, John Douglas

doi:10.3141/2133-12

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…These models investigate the interaction of involved parameters at a micro scale (e.g. TLUMIP (Weidner et al, 2009), UrbanSim (Waddell et al, 2003), ILUMASS (Wagner & Wegener, 2007)), or a macro scale (e.g. LTM (Pijanowski et al, 2002)), or at multiple scales (e.g.…”

Section: Existing Land-use Models and Modeling Approachesmentioning

confidence: 99%

Developing an Automated Method for the Application of Lidar in Iumat Land-Use Model: Analysis of Land-Use Changes Using Building-Form Parameterization, Gis, and Artificial Neural Networks

Farzinmoghadam¹,

Mostafavi²,

Infield³

et al. 2019

Journal of Green Building

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Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. The study of land-use change has many implications for infrastructure design, resource allocation, and urban metabolism simulation. While most urban models focus on horizontal growth patterns, few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes. In this paper, Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change. This work outlines an automated method for generating building-form variables from Light Detection and Ranging (LIDAR) data by using Density-Based Spatial Clustering and normal equations. This paper presents a Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict urban growth patterns within the IUMAT framework (Integrated Urban Metabolism Analysis Tool), which is an analytical platform for quantifying the overall sustainability in the urbanscape. The town of Amherst in Western Massachusetts (for the period of 1971–2005) is used as a case study for testing the model. By isolating the weights of each explanatory variable in models, this study highlights the influence of building geometry on future development scenarios.

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Section: Existing Land-use Models and Modeling Approachesmentioning

confidence: 99%

Developing an Automated Method for the Application of Lidar in Iumat Land-Use Model: Analysis of Land-Use Changes Using Building-Form Parameterization, Gis, and Artificial Neural Networks

Farzinmoghadam¹,

Mostafavi²,

Infield³

et al. 2019

Journal of Green Building

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“…Recognizing the fact that travel is an activity derived demand, researchers have been actively seeking to understand the behavioral choices related to daily activities, such as activity generation and scheduling, destination choice, and intra-household task assignment (Habib, 2007). Because the activity-based modeling approach is considered more theoretically rigorous, it is gradually favored in travel demand modeling not only in the stand-alone models like MATSIM (The Multi-agent Transport Simulation) (Balmer et al, 2008), but also in a number of integrated land use and transportation models including ILUTE (The Integrated Land Use, Transportation, Environment) (Salvini and Miller, 2005), ILUMASS (Integrated Land-Use Modelling and Transportation System Simulation) (Wagner and Wegener, 2007), TLUMIP (Transportation and Land Use Model Integration Project) (Weidner et al, 2009), and SimMobility (Adnan et al, 2016; Zhu, 2018). However, one key challenge related to the advancement of activity-based research is to better understand how people decide and arrange their daily activities under a variety of circumstances, and to investigate how to model these behaviors appropriately.…”

Section: Introductionmentioning

confidence: 99%

Inference of activity patterns from urban sensing data using conditional random fields

Zhu

2021

Environment and Planning B: Urban Analytics and City Science

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The study of human activity–travel patterns for urban planning has evolved a long way in theories, methodologies, and applications. However, the scarcity of data has become a major barrier for the advancement of research in the field. Recently, the proliferation of urban sensing and location-based devices generates voluminous streams of spatio-temporal registered information. In this study, we propose an approach using the linear-chain Conditional Random Fields (CRFs) model to learn the spatio-temporal correspondences of different types of activities and the inter-dependencies among sequential activities from training dataset such as the household travel or time use surveys, and to infer the hidden activity types associated with urban sensing data. The performance of the CRFs model is compared against the Random Forest (RF) model, which has been used in a number of existing studies. The results show that the linear-chain CRFs models generally outperform the RF counterparts with respect to classification accuracy of activity types, in particular for those travelers having more outdoor daily activities. The proposed methodology is demonstrated by reconstructing the activity landscape of the surrounding area of a major Mass Rail Transit station in Singapore using the transit smart card transaction data. The inferred activities from the transit smart card data are expected to complement the ground surveys and improve our understanding of the interactions of different components of activities/travels as well as the relationship between urban space and human activities.

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“…There is an urgent need for improved models that address the interdependencies between land use and transportation at state and regional levels, and considerable new work is underway to develop such models (See, for example, ULTRANS ITS UC Davis and HBA Specto Incorporated, 2011;Waddell et al, 2010;Weidner et al, 2009). These models and planning practices to integrate land use and transportation, however, require the integration of massive amounts of land use and socio-economic data that is messy and incomplete.…”

Section: Introductionmentioning

confidence: 99%

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Wang

Kim²

2014

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There is an urgent need for improved models that address the interdependencies between land use and transportation, and considerable new work is underway to develop such models in Oregon and elsewhere. These models and planning practices to integrate land use into the process, however, require the integration of massive amounts of land use data that is messy and incomplete. There have been considerable advances in the treatment of such data problems in other domains, drawing on data mining and machine-learning techniques to address issues in various domains. To date, however, little systematic effort has applied these technological advances to the problem domain of land use and transportation data. Experience suggests that as much as 70% of the total effort in developing integrated land use and transportation models is directly or indirectly associated with data development, integration and cleaning, yet there is remarkably little systematic research focused on the development of reusable methods and tools to support this problem domain.In coordination with an ongoing project of similar theme funded by University of California Transportation Center (UCTC), this project will focus on bringing interdisciplinary methods to develop land use datasets for integrated land use and transportation planning and modeling, with special attention on preserving temporal dimension of the data and monitoring data quality through indicators. Utilizing statistics and machinelearning techniques, we will develop reusable tools that will create a harmonized and coherent land use database from various public and private sources. These tools will be made available to the public and can be used by cities, counties, metropolitan planning agencies, state agencies, universities or anyone else needing to develop a usable database for use in integrated planning and modeling. All of the resulting data, with the exception of proprietary or confidential input data where there is no viable alternative, would be public and reusable for planning and research.17.

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Sensitivity Testing with the Oregon Statewide Integrated Model

Cited by 5 publications

References 5 publications

Developing an Automated Method for the Application of Lidar in Iumat Land-Use Model: Analysis of Land-Use Changes Using Building-Form Parameterization, Gis, and Artificial Neural Networks

Developing an Automated Method for the Application of Lidar in Iumat Land-Use Model: Analysis of Land-Use Changes Using Building-Form Parameterization, Gis, and Artificial Neural Networks

Inference of activity patterns from urban sensing data using conditional random fields

Continuous Data Integration for Land Use and Transportation Planning and Modeling

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