Abstract The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.

中文翻译：

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城市网络日常始发地流量与交通量的相关性分析

摘要 城市网络上的出行模式可以由两个主要变量表示：起点-终点流量（OD流量），定义为给定时间段内两个地点之间的出行次数，以及交通量，定义为车辆数量在给定的时间间隔内穿过街道。过去对交通分配动态和 OD 估计的研究表明，旅行者的决定每天都在变化，他们最近的决定可能会影响他们当前的旅行决定。基于这些假设，本研究分析了由大量交通传感器生成的一组日常交通量和 OD 流量的自相关性，确定了城市网络中不同位置和 OD 对的数据相关结构. 为此，采用巴西福塔莱萨交通监控系统2017年数据集数据处理方法，主要包括以下几个步骤：设备故障数据清洗、典型月和非典型月流量定义、每日流量周期，选择合适的设备来获取 OD 流量，以及检测时间序列中的异常值。交通概况和每日交通时段是通过应用聚类技术定义的。自相关分析是在通过应用回归分析控制数据中的季节性影响后进行的。本研究有助于了解由于城市活动的空间分布和网络的空间中心性，出行模式的动态如何随空间变化。对 2017 年全年 144 组交通量的分析表明，在拥堵的中心区域，有多种路线可供选择，交通量的自相关性应该更高。看来，对于大型拥塞网络，存在许多不确定因素（如事故、多变天气、多路径等），部分用户对网络性能的了解并不完整，必须依靠经验和习惯决定他们的路线，尤其是在网络的更集中的位置。区域间OD流样本序列的序列相关分析表明，城市中心区商业和服务相关活动较多，似乎影响OD流的动态，