Change in long-distance travel: uncovering travel activity trends, inequalities, and dynamics over the life course

长途旅行和航空旅行在运输量和温室气体排放方面的相关性越来越大，可以说这要求对旅行需求进行管理。该项目正在进行的第一阶段提供了国际移民、社交网络的空间延伸和早期旅行社会化如何促进土地退化和土地退化的证据。然而，在我们对LDT如何随时间变化的理解中仍然存在一些差距。为了解决这个问题，我们计划该项目的第二阶段，将追求四个研究目标：i）跟踪旅行活动水平的变化和参与LDT的社会经济不平等模式; ii）确定个人生命历程中航空旅行变化的决定因素; iii）记录LDT，航空旅行及其个人水平决定因素之间的关联模式随时间的变化; iv）利用现有的最新数据，提高我们对航空旅行的空间决定因素的理解。从方法上讲，该项目将包括对项目第一阶段获得资金以来发布的具有国家代表性的数据集进行二次定量分析。英国独特的面板数据将使我们能够在个人生命历程的两个点（2013年和2019年）调查航空旅行行为。德国（2002年、2008年、2017年）的重复横截面数据将使我们能够记录LDT行为的水平、不平等和决定因素如何随时间变化，并提供关于受访者居住地点的独特、精细的地理信息（2017年）。数据分析技术将包括二元趋势分析、多元回归和不平等指标的估计。最后，我们将强调我们的研究结果对未来LDT趋势，旅游需求和排放建模以及当代可持续交通和气候政策辩论的影响。

Long-distance travel (LDT) and air travel are increasingly relevant in terms of transport volume and GHG emissions, which arguably calls for travel demand management. The ongoing first phase of the project has provided evidence of on how international migration, spatial stretching of social networks and early socialisation to travel contribute to the growth and ‘institutionalisation’ of LDT. However, there remain several gaps in our understanding of how LDT changes over time. To address this, we plan a second phase of the project that will pursue four research objectives: i) to track changes in levels of travel activity and patterns of socioeconomic inequality in participation to LDT; ii) to identify the determinants of changes in air travel over individual life courses; iii) to document changes in patterns of association between LDT, air travel and their individual-level determinants over time; iv) to improve our understanding of the spatial determinants of air travel, with the most recent data available. Methodologically, the project will consist of secondary quantitative analysis of nationally representative datasets that were released since the first phase of the project was funded. Unique panel data for the UK will allow us to investigate air travel behaviour at two points in individual life courses (2013 and 2019). Repeated cross-sectional data for Germany (2002, 2008, 2017) will allow us to document how the levels, inequality, and determinants of LDT behaviour changed over time, as well as providing unique, fine-grained geographical information on respondents’ residential location (for 2017). Data analysis techniques will include bivariate trend analysis, multivariate regression and estimation of inequality metrics. We will conclude by highlighting the implications of our findings for future LDT trends, for travel demand- and emission-modelling, and for contemporary debates on sustainable transport and climate policy.