Peak–car is characterised by slower rates of growth, levelling off, or a reduction in car travel. Researchers have paid much attention to this topic recently. However, a consensus on possible explanations of the phenomenon remains elusive. Questions remain whether the drivers of travel demand are changing and projection methods need to be revised, or the peak–car is purely an external effect (e.g. fuel price, financial crisis) and car travel continues to increase when such effects are nullified. The study investigates these issues focusing on Brisbane using six versions of the South East Queensland Household Travel Survey data (1992, 2003, 2006, 2009, 2012, and 2018). Three different analytical methods were employed. First, four pooled regression models were estimated to examine the effects of time (external effects) on travel behaviour, controlling for different socio-demographics and built environment factors. Second, five multiple linear regression models were estimated using 2003, 2006, 2009, 2012, and 2018 datasets to identify behavioural differences between different groups in a specific point in time. Third, differences in coefficients as generated in the second stage were estimated to understand whether the behavioural patterns are changing. The findings suggest that car travel in Brisbane: a) reached a peak in 2003; b) experienced a decline throughout 2003–2012; and c) exhibited a levelling off throughout 2012–2018. The factors contributed to the peak–car phenomenon in Brisbane are both external (global financial crisis, policy interventions in public transport) and a significant change in the coefficients of some internal factors over the periods (millennials, household size, population density).

高峰期汽车的特点是增长速度减慢，趋于平稳或汽车旅行减少。研究人员最近对这个话题非常关注。但是，关于这种现象可能的解释的共识仍然难以捉摸。仍然存在问题，即旅行需求的驱动因素是否正在改变，是否需要修改预测方法，或者高峰汽车纯粹是外部影响（例如，燃油价格，金融危机），而当这种影响被消除时，汽车旅行继续增加。该研究使用六个版本的《昆士兰州东南部家庭旅行调查》数据（1992、2003、2006、2009、2012和2018年）对这些问题进行了调查，重点是布里斯班。采用了三种不同的分析方法。首先，我们估计了四个汇总的回归模型，以检验时间（外部影响）对旅行行为的影响，控制不同的社会人口统计学和建筑环境因素。其次，使用2003、2006、2009、2012和2018年的数据集估计了五个多元线性回归模型，以识别特定时间点不同群体之间的行为差​​异。第三，估算第二阶段产生的系数差异，以了解行为模式是否正在改变。研究结果表明，布里斯班的汽车旅行：a）在2003年达到高峰。b）2003-2012年期间下降；c）在2012-2018年期间一直保持稳定。造成布里斯班高峰车现象的因素既有外部因素（全球金融危机，公共交通方面的政策干预），又有一段时间内一些内部因素的系数发生重大变化（千禧一代，