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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Phases of scaling and cross-correlation behavior in traffic
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Phases of scaling and cross-correlation behavior in traffic

机译：流量中缩放和互相关行为的阶段

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While many microscopic models of traffic flow describe transitions between different traffic phases, such transitions are difficult to quantify in measured traffic data. Here we study long-term traffic recordings consisting of ≈2900 days of flow, density, and velocity time series with minute resolution from a Spanish motorway. We calculate fluctuations, cross-correlations, and long-term persistence properties of these quantities in the flow-density diagram. This leads to a data-driven definition of (local) traffic states based on the dynamical properties of the data, which differ from those given in standard guidelines. We find that detrending techniques must be used for persistence analysis because of non-stationary daily and weekly traffic flow patterns. We compare our results for the measured data with analysis results for a microscopic traffic model, finding good agreement in most quantities. However, the simulations cannot easily reproduce the congested traffic states observed in the data. We show how fluctuations and cross-correlations in traffic data may be used for prediction, i.e., as indications of increasing or decreasing velocities.
机译：尽管交通流的许多微观模型描述了不同交通阶段之间的转换，但是这种转换很难在测得的交通数据中量化。在这里，我们研究了来自西班牙高速公路的长期交通记录，包括约2900天的流量，密度和速度时间序列，具有微小的分辨率。我们在流量密度图中计算这些量的波动，互相关和长期持久性。这导致基于数据的动态属性的数据驱动的（本地）交通状态定义，这与标准指南中给出的不同。我们发现由于非固定的每日和每周流量流模式，必须使用去趋势技术进行持久性分析。我们将实测数据的结果与微观交通模型的分析结果进行比较，发现大多数情况下都具有良好的一致性。但是，模拟无法轻松地重现数据中观察到的拥塞流量状态。我们展示了交通数据中的波动和互相关如何可用于预测，即作为速度增加或减少的指示。

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