Leveraging machine learning to predict residential location choice: A comparative analysis
Residential location choice is a pivotal factor shaping urban landscapes, influencing economic activity, social demographics, and transportation networks. Understanding these decisions is essential for effective urban planning. While discrete choice models have been used to predict residential locat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003007 |
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| author | Vahid Noferesti Hamid Mirzahossein |
| author_facet | Vahid Noferesti Hamid Mirzahossein |
| author_sort | Vahid Noferesti |
| collection | DOAJ |
| description | Residential location choice is a pivotal factor shaping urban landscapes, influencing economic activity, social demographics, and transportation networks. Understanding these decisions is essential for effective urban planning. While discrete choice models have been used to predict residential locations, their limitations in handling complex datasets have hindered their accuracy. Machine learning has shown potential to outperform discrete choice methods in transportation-related decisions like travel mode. However, the traditional application of these methods to residential location choice faces challenges due to the large number of choice alternatives and the limitations of sample size and data availability. This often leads to biased predictions, favoring popular choices over less common ones. This study introduces a novel approach that overcomes these limitations, making machine learning more effective for residential location choice. By applying this method and different machine learning models, the study provides a detailed comparison of their performance in predicting residential choices. A comparative analysis of various machine learning algorithms reveals that XGBoost and gradient boosting models significantly outperform traditional methods, achieving a 42 % accuracy rate in predicting residential location choices on the 33 % validation data of household travel survey data from MWCOG. These findings offer valuable insights for policymakers and urban planners to optimize land use, transportation, and urban development strategies. |
| format | Article |
| id | doaj-art-599ad11cd6664ab193b450037122f382 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-599ad11cd6664ab193b450037122f3822025-02-08T05:00:57ZengElsevierResults in Engineering2590-12302025-03-0125104214Leveraging machine learning to predict residential location choice: A comparative analysisVahid Noferesti0Hamid Mirzahossein1Department of Civil - Transportation Planning, Imam Khomeini International University (IKIU), Qazvin, IranCorresponding author.; Department of Civil - Transportation Planning, Imam Khomeini International University (IKIU), Qazvin, IranResidential location choice is a pivotal factor shaping urban landscapes, influencing economic activity, social demographics, and transportation networks. Understanding these decisions is essential for effective urban planning. While discrete choice models have been used to predict residential locations, their limitations in handling complex datasets have hindered their accuracy. Machine learning has shown potential to outperform discrete choice methods in transportation-related decisions like travel mode. However, the traditional application of these methods to residential location choice faces challenges due to the large number of choice alternatives and the limitations of sample size and data availability. This often leads to biased predictions, favoring popular choices over less common ones. This study introduces a novel approach that overcomes these limitations, making machine learning more effective for residential location choice. By applying this method and different machine learning models, the study provides a detailed comparison of their performance in predicting residential choices. A comparative analysis of various machine learning algorithms reveals that XGBoost and gradient boosting models significantly outperform traditional methods, achieving a 42 % accuracy rate in predicting residential location choices on the 33 % validation data of household travel survey data from MWCOG. These findings offer valuable insights for policymakers and urban planners to optimize land use, transportation, and urban development strategies.http://www.sciencedirect.com/science/article/pii/S2590123025003007Residential location choiceMachine learningRandom forestExtra treeXGBoostGradient boosting |
| spellingShingle | Vahid Noferesti Hamid Mirzahossein Leveraging machine learning to predict residential location choice: A comparative analysis Results in Engineering Residential location choice Machine learning Random forest Extra tree XGBoost Gradient boosting |
| title | Leveraging machine learning to predict residential location choice: A comparative analysis |
| title_full | Leveraging machine learning to predict residential location choice: A comparative analysis |
| title_fullStr | Leveraging machine learning to predict residential location choice: A comparative analysis |
| title_full_unstemmed | Leveraging machine learning to predict residential location choice: A comparative analysis |
| title_short | Leveraging machine learning to predict residential location choice: A comparative analysis |
| title_sort | leveraging machine learning to predict residential location choice a comparative analysis |
| topic | Residential location choice Machine learning Random forest Extra tree XGBoost Gradient boosting |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003007 |
| work_keys_str_mv | AT vahidnoferesti leveragingmachinelearningtopredictresidentiallocationchoiceacomparativeanalysis AT hamidmirzahossein leveragingmachinelearningtopredictresidentiallocationchoiceacomparativeanalysis |