A new approach to develop large-scale land-use models using publicly available data

Bibliographic Data

ID21247528
AuthorsAli R Samani (University of Tennessee at Knoxville, corresponding author), Sabyasachee Mishra (0000-0002-7198-3548, University of Tennessee at Knoxville), David Jung-Hwi Lee (0000-0001-8644-6545, Tennessee Department of Transportation), Mihalis Golias (0000-0001-9402-2996, University of Tennessee at Knoxville), Mihalis M Golias (University of Memphis), Jerry Everett (0000-0002-4192-8062, University of Tennessee at Knoxville)
Year2022
Volume49
Issue1
Pages169-187
Publication date2022-01-01
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/2399808321999399
OpenAlexW3136544003
LanguageEN
Citations received2
References cited14

Developing a land-use model for large-scale cases is a topic that has received less attention in the literature, while it is crucial for transportation engineers and urban planners to analyze the effect of various policies in multi-jurisdiction metropolitan areas and to some extent on a statewide scale. While gravity-based models are too simplistic, microsimulation models require extensive data and massive computation. This paper presents a land-use model that can be applied to large-scale geographies using publicly available data and be able to forecast demographic and socioeconomic attributes with reasonable accuracy and acceptable computational time. The proposed model incorporates Putman’s Integrated Transportation–Land-Use Package (ITLUP) and Kockelman’s Gravity-based Land-Use Model (G-LUM) fundamentals with enhanced formulation of newly added variables and structural changes. Considering the nonconvex and nonlinear nature of the proposed model, we utilize an enhanced genetic algorithm for base year calibration. Further, we assess the accuracy of the model with backcasting validation. We utilize the state of Tennessee as the case study area and utilized all open-source data available to the model application. The model results show reasonably accurate estimates of households by size, employment by industry, and land utilization by condition. As applicable, the model outperforms G-LUM by accuracy ( R 2 and Percentage of Good Prediction ( PGP)) and error measures (Mean Absolute Percentage Error ( MAPE)). The proposed land-use model has the potential to be applied for medium to large-scale geographies with reasonable accuracy in predicting socioeconomic, demographic, and land condition estimates by using publicly available data

Backcasting · Civil engineering · Data mining · Econometrics · Geography · Gravity model of trade · Land use · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility

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