找出高速公路拥堵源头，帮交通管理精准干预。
NEXICA: Discovering Road Traffic Causality (Extended arXiv Version)
- 只关注速度突变事件，识别拥堵起始点
- 在洛杉矶数据上准确率超主流方法，速度快一倍以上
- 适合城市交通管理者和智能调度系统使用
道路拥堵是长期存在的问题。将资源集中于拥堵成因，可能更高效地缓解交通缓慢。我们提出NEXICA算法，用于发现高速路网中哪些路段的拥堵会引发其他路段的拥堵。该算法以道路速度时间序列作为输入。鉴于现有方法不足，我们提出三种创新：第一，仅关注时间序列中事件的有无，事件代表交通缓慢的开始时刻；第二，构建基于最大似然估计的概率模型，计算两地间自发与受控缓慢的概率；第三，训练二分类器识别因果关系对，训练数据来自事先可确定因果关系（正负）的路段对。我们在洛杉矶地区195个高速传感器连续六个月的速度数据上测试，结果表明，本方法在准确率和计算速度上均优于当前最优基线。
原文摘要 · Abstract (English)
Road traffic congestion is a persistent problem. Focusing resources on the causes of congestion is a potentially efficient strategy for reducing slowdowns. We present NEXICA, an algorithm to discover which parts of the highway system tend to cause slowdowns on other parts of the highway. We use time series of road speeds as inputs to our causal discovery algorithm. Finding other algorithms inadequate, we develop a new approach that is novel in three ways. First, it concentrates on just the presence or absence of events in the time series, where an event indicates the temporal beginning of a traffic slowdown. Second, we develop a probabilistic model using maximum likelihood estimation to compute the probabilities of spontaneous and caused slowdowns between two locations on the highway. Third, we train a binary classifier to identify pairs of cause/effect locations trained on pairs of road locations where we are reasonably certain a priori of their causal connections, both positive and negative. We test our approach on six months of road speed data from 195 different highway speed sensors in the Los Angeles area, showing that our approach is superior to state-of-the-art baselines in both accuracy and computation speed.
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