Yu Wang , Thomas O. Hancock , Yacan Wang , Charisma Choudhury
{"title":"结合被动显示偏好数据和陈述偏好调查数据对住宅搬迁行为进行建模","authors":"Yu Wang , Thomas O. Hancock , Yacan Wang , Charisma Choudhury","doi":"10.1016/j.tranpol.2025.103789","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding how various factors shape residential relocation is crucial for effective infrastructure planning and policy. Yet, existing revealed preference (RP) datasets often lack essential demographic or dwelling details, while stated preference (SP) surveys are prone to hypothetical bias and behavioural incongruence. To fill in this gap, this study presents a residential relocation choice model that combines residential location data derived from passively generated public transport smart cards of 82,720,872 users and SP data from 971 respondents (8739 observations) in Beijing, China. Both types of data were generated or collected in the backdrop of the COVID-19 pandemic, which led to higher-than-usual residential relocations in Beijing. The integrated approach, which accounts for the scale difference between the two datasets, reveals a strong preference for city-centre locations. But higher infection risks increase the likelihood of moving away from crowded areas, whereas flexible work-from-home policies lower the inclination to relocate to the centre. These findings quantify how different pandemic-related factors alter traditional relocation drivers. The results can guide policymakers in designing more resilient housing and transport policies, especially under future disruptions like pandemics. Moreover, the data-fusion framework offers a replicable strategy for researchers and planners seeking to capture both real-world behaviours and hypothetical scenarios in residential location studies.</div></div>","PeriodicalId":48378,"journal":{"name":"Transport Policy","volume":"173 ","pages":"Article 103789"},"PeriodicalIF":6.3000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling residential relocation behaviour combining passive revealed preference data and stated preference survey data\",\"authors\":\"Yu Wang , Thomas O. Hancock , Yacan Wang , Charisma Choudhury\",\"doi\":\"10.1016/j.tranpol.2025.103789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding how various factors shape residential relocation is crucial for effective infrastructure planning and policy. Yet, existing revealed preference (RP) datasets often lack essential demographic or dwelling details, while stated preference (SP) surveys are prone to hypothetical bias and behavioural incongruence. To fill in this gap, this study presents a residential relocation choice model that combines residential location data derived from passively generated public transport smart cards of 82,720,872 users and SP data from 971 respondents (8739 observations) in Beijing, China. Both types of data were generated or collected in the backdrop of the COVID-19 pandemic, which led to higher-than-usual residential relocations in Beijing. The integrated approach, which accounts for the scale difference between the two datasets, reveals a strong preference for city-centre locations. But higher infection risks increase the likelihood of moving away from crowded areas, whereas flexible work-from-home policies lower the inclination to relocate to the centre. These findings quantify how different pandemic-related factors alter traditional relocation drivers. The results can guide policymakers in designing more resilient housing and transport policies, especially under future disruptions like pandemics. Moreover, the data-fusion framework offers a replicable strategy for researchers and planners seeking to capture both real-world behaviours and hypothetical scenarios in residential location studies.</div></div>\",\"PeriodicalId\":48378,\"journal\":{\"name\":\"Transport Policy\",\"volume\":\"173 \",\"pages\":\"Article 103789\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transport Policy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967070X25003324\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transport Policy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967070X25003324","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Modelling residential relocation behaviour combining passive revealed preference data and stated preference survey data
Understanding how various factors shape residential relocation is crucial for effective infrastructure planning and policy. Yet, existing revealed preference (RP) datasets often lack essential demographic or dwelling details, while stated preference (SP) surveys are prone to hypothetical bias and behavioural incongruence. To fill in this gap, this study presents a residential relocation choice model that combines residential location data derived from passively generated public transport smart cards of 82,720,872 users and SP data from 971 respondents (8739 observations) in Beijing, China. Both types of data were generated or collected in the backdrop of the COVID-19 pandemic, which led to higher-than-usual residential relocations in Beijing. The integrated approach, which accounts for the scale difference between the two datasets, reveals a strong preference for city-centre locations. But higher infection risks increase the likelihood of moving away from crowded areas, whereas flexible work-from-home policies lower the inclination to relocate to the centre. These findings quantify how different pandemic-related factors alter traditional relocation drivers. The results can guide policymakers in designing more resilient housing and transport policies, especially under future disruptions like pandemics. Moreover, the data-fusion framework offers a replicable strategy for researchers and planners seeking to capture both real-world behaviours and hypothetical scenarios in residential location studies.
期刊介绍:
Transport Policy is an international journal aimed at bridging the gap between theory and practice in transport. Its subject areas reflect the concerns of policymakers in government, industry, voluntary organisations and the public at large, providing independent, original and rigorous analysis to understand how policy decisions have been taken, monitor their effects, and suggest how they may be improved. The journal treats the transport sector comprehensively, and in the context of other sectors including energy, housing, industry and planning. All modes are covered: land, sea and air; road and rail; public and private; motorised and non-motorised; passenger and freight.