科学如何激励我们的ETA模型
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Have you ever driven alongside another vehicle for an extended period? You’ve likely experienced this peculiar phenomenon: despite sharing the same route and traffic signals, you inevitably encounter a red light while the other vehicle passes through seconds earlier. For a moment, you might think they’ll reach their destination first. However, as you continue, you’re surprised to find them waiting at another red light just a few blocks ahead. This dance continues for a while. The ‘notes’ of this ‘song’ are the micro-random events inherent in traffic: a flock of pigeons crossing the road, a cyclist approaching, a sudden lane change by the vehicle in front. Some factors are more deterministic: weather conditions, road closures, or construction delays, others less so.
您是否曾经与另一辆车并行驾驶很长一段时间？您可能经历过这种奇特的现象：尽管共享同一路线和交通信号，您却不可避免地在红灯前停下，而另一辆车却在几秒钟前通过。您可能会想，他们会先到达目的地。然而，当您继续前行时，您会惊讶地发现他们在前方几个街区的另一个红灯前等候。这种舞蹈持续了一段时间。这首“歌”的“音符”是交通中固有的微随机事件：一群鸽子穿过马路，一名骑自行车的人接近，前方车辆的突然变道。有些因素更具确定性：天气条件、道路封闭或施工延误，其他因素则不那么确定。
As a seasoned data scientist, your mission is to uncover hidden patterns within chaotic systems and translate them into mathematical insights. These insights inform the decisions, both big and small, of engineering and science organizations, and support its continual operational strategy. This blog translates a seemingly random traffic pattern into a comprehensible behavior, which we will use to build a statistical model for travel time.
作为一名经验丰富的数据科学家，您的使命是揭示混乱系统中的隐藏模式，并将其转化为数学洞察。这些洞察为工程和科学组织的决策提供信息，无论大小，并支持其持续的运营战略。这个博客将一个看似随机的交通模式转化为可理解的行为，我们将利用它来构建旅行时间的统计模型。
Let’s get back to the core question: how do seemingly chaotic patterns help us build models? One observation we repeatedly made is that the distance of a ride significantly influences our understanding of travel time uncertainty. The longer y...