适者生存:车队数据科学如何使用生存分析来估计8级卡车替换率

该分析是与车队数据科学团队的Will Gagne-Maynard和Nicholas Janetos共同撰写的.

Class 8 truck orders are one of the most widely watched data points in the trucking industry. Freight market folklore views them as a kind of canary-in-the-coalmine: When the market is hot, they foreshadow an imminent downshift.

This makes sense. After accounting for delivery lags, 卡车订单增加,应该意味着该行业正在投资于新产能——如果新产能太多,或者需求意外疲软,这些新产能可能变成产能过剩. By most accounts, this is one reason for the freight industry’s notorious boom-and-bust cycle.

The reality is, truck orders reflect not only business’ plans for the future, but also the legacy of decisions made long ago. Not all truck orders are new investments; some are replacements for older, worn-out vehicles. 我们的分析表明,卡车行业的商用车替换率可能大大高于传统的假设.

Microdata > Macrodata

Unfortunately, 新卡车交付时,驾驶室上不会有标签,表明它是否要取代一辆最近退役的车辆——这是我们必须估计的东西. Fortunately, 这正是车队数据科学团队的数据科学家和经济学家们所接受的培训要回答的问题.

例如,对8级卡车替换率的大多数估计(至少是我们在公开可获得的来源中遇到的估计)依赖于行业轶事的启发法, a uniform seven- or eight-year replacement cycle or a long-term moving average of truck orders. From a data perspective, the reliance on high-level summary statistics is not ideal. 更细粒度的数据——统计学家称之为“微数据”——允许更灵活的建模,并产生更可靠的估计(而且处理起来更有趣).

Ideally, 管理数据将使我们能够跟踪每辆卡车从“出生”(制造)到“死亡”(报废)的一段时间,即所谓的“纵向队列分析”。. To our knowledge, such detailed records do not exist. 另一种接近的方法是在给定的时间点观察每辆现役卡车的“年龄”, 根据我们对8级卡车年产量的了解,将这种分布与预期的年龄分布进行比较——这种方法被称为“生存分析”,在生物统计学中被广泛应用, ecology and demography.

我们很快意识到,通过将详细的运营和安全数据(Convoy用来评估卡车公司的安全)与公开可用的车辆信息相结合,我们可以创建一个原始数据集,使我们能够进行生存分析, 为卡车行业的8级替换率这个长期存在的问题提供了一个独特而详细的答案.

From this insight, pulling the data together was straightforward.

  • First, 我们对最近在联邦汽车运输安全管理局(FMCSA)和州交通部门注册的活跃的商业运输公司进行了快照. 绝大多数卡车运输公司被要求保持联邦(有时是州)注册, including the number of vehicles they operate. An important note is that the registrations are for trucking companies, not vehicles. 
  • Next, we identified the vehicles registered to those carriers. 对于那些最近有安全检查记录的货运公司来说,车辆标识是可用的. We recognize that there is the potential for sampling bias at this stage; however, as safety inspections are conducted at random, we believe the potential for bias is minimal.
  • We then merged on the build year for each truck in the sample. This information is publicly available from the U.S. Department of Transportation’s National Highway Traffic Safety Administration.

From this data set, 我们能够创建一份截至2021年3月的8级卡车的年龄和“出生年份”快照. Comparing this distribution with cumulative commercial vehicle production data published by FTR,我们能够估算出8级卡车的“预期寿命”范围和年替换率.

Unexpected Results

Our analysis suggests that the average lifespan of a Class 8 truck is eight years, precisely in line with conventional wisdom. Importantly, however, the age distribution is not normal: The median Class 8 truck is only six years old. A handful of older trucks drive up the national average, but the “typical” truck is substantially younger.

这些结果隐含的替换率表明,卡车行业每年需要看到8级卡车订单在369辆之间,700 and 374,400 (midpoint of 372,200, or 31,000 per month) in order to just keep the current level of truck capacity constant. This is somewhat higher than other estimates that we’ve seen.

As of June 2021 Class 8 truck orders are running at an annual pace of 431,000 — above both replacement rate estimates, but less dramatically so in the case of ours. (Moreover, due to elevated delivery lags associated with the global semiconductor shortage, 从接到新的卡车订单到货运市场开始看到净运力增加的效果之间,有一个比通常情况下更长的延迟.)

我们分析的第二个含义是,卡车生产赤字比其他替代率估计所显示的要大. 在货运市场疲软期间——比如2018年末至2020年初的市场状况——卡车产量低于更换需求并不罕见,这导致了活跃车辆的净下降. 单纯地看活跃的卡车数量可能会低估这一现象的真实规模,因为一些小型承运人可能会试图在不景气时期延长其车辆的寿命,以期推迟大规模的资本支出.

我们对该行业卡车更新率的估计表明,过去两年累积的资本投资赤字总计为39个,000 trucks as of June 2021, compared to a surplus of 164,000 via more conventional estimates of the replacement rate.

Takeaways

8级卡车订单是关注货运市场发展的重要指标. 理解卡车订单中哪些部分代表了更新,哪些部分代表了净增长,对于准确解读这个行业指南至关重要. Get the number too high and you’ll underestimate the extent of capacity growth and downside risk to the freight market; get the number too low and you set the stage for contract failure, which exacerbates market inefficiencies contributing to waste and higher prices.

In the absence of the ideal-world data we all wish we had, we must make decisions with the messy real-world data we actually have. But real-time exigencies are no excuse for napkin math. Naive estimates drive suboptimal decisions, and the predominance of suboptimal decision making creates more problems downstream. As an industry, we can and must be more rigorous.

View our economic commentary disclaimer here.

Aaron Terrazas
Aaron Terrazas是Convoy公司的经济研究总监,他在那里研究和评论货运市场以及货运揭示了更广泛的经济. 在信誉手机赌城下注车队之前,亚伦是Zillow的高级经济学家和经济研究总监. Before that, he was an Economist at the U.S. Treasury Department’s Office of Economic Policy in Washington, D.C. He was educated at Georgetown University and Johns Hopkins University. Aaron has been a runner since age 13 and is a sucker for all endurance sports.
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