A Cluster Analysis of Temporal Patterns of Travel Production in the Netherlands: Dominant within-day and day-to-day patterns and their association with Urbanization Levels

IF 2.1 4区 工程技术 Q3 TRANSPORTATION
Zahra Eftekhar, Adam Pel, Hans Van Lint
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引用次数: 0

Abstract

This paper explores temporal patterns in travel production using a full month of production data from traffic analysis zones (TAZ) in the (entire) Netherlands. The mentioned data is a processed aggregated derivative (due to privacy concerns) from GSM traces of a Dutch telecommunication company. This research thus also sheds light on whether such a processed data source is representative of both regular and non-regular patterns in travel production and how such data can be used for planning purposes. To this end, we construct normalized matrix (heatmap) representations of weekly hour-by-hour travel production patterns of over 1200 TAZs, which we cluster using K-means combined with deep convolutional neural networks (inception V3) to extract relevant features. A silhouette score shows that three dominant clusters of temporal patterns can be discerned (K=3). These three clusters have distinctly different within-day and day-to-day production patterns in terms of peak period intensity over different days of the week. Subsequently, a spatial analysis of these clusters reveals that the differences can be related to (easily observable) land-use features such as urbanization levels (i.e., Urban, Rural, and mixed-level). To substantiate this hypothesis and the usefulness of this clustering result, we apply an OVR-SMOTE-XGBoost ensemble classification model on the land-use features of the TAZs (i.e., to identify their cluster). The results of our clustering analysis show that given the land-use features, the overall production patterns are identifiable. Further analysis of the mixed-level areas shows a more complex relationship between temporal heterogeneity and spatial characteristics. Population density seems to impose additional uncertainty on the temporal patterns. All in all, feature selection and spatial and temporal discretization play essential roles in identifying the dominant trip production patterns. These findings are directly useful for data-driven estimation and prediction of demand time series. Furthermore, this study provides further insights into people's mobility, relevant for transportation analysis and policies.
荷兰旅行生产时间模式的聚类分析:日内和日间的主要模式及其与城市化水平的关系
本文利用荷兰(整个)交通分析区(TAZ)的全月生产数据,探讨了出行生产的时间模式。上述数据是荷兰一家电信公司 GSM 跟踪数据经过处理后的汇总衍生数据(出于隐私考虑)。因此,本研究还揭示了这样一个经过处理的数据源是否能代表常规和非常规的出行模式,以及如何将此类数据用于规划目的。为此,我们构建了 1200 多个 TAZ 的每周逐小时旅行生产模式的归一化矩阵(热图)表示法,并使用 K-means 结合深度卷积神经网络(inception V3)对其进行聚类,以提取相关特征。剪影评分显示,可以发现三个主要的时间模式聚类(K=3)。这三个群组在一周内不同日期的高峰期强度方面具有明显不同的日内和日间生产模式。随后,对这些群组的空间分析表明,这些差异与(易于观察到的)土地利用特征有关,如城市化水平(即城市、农村和混合水平)。为了证实这一假设和聚类结果的实用性,我们对 TAZ 的土地利用特征应用了 OVR-SMOTE-XGBoost 集合分类模型(即识别其聚类)。聚类分析结果表明,根据土地利用特征,总体生产模式是可以识别的。对混合水平区域的进一步分析表明,时间异质性与空间特征之间的关系更为复杂。人口密度似乎给时间模式带来了额外的不确定性。总之,特征选择和时空离散化在识别主要行程生产模式方面发挥着至关重要的作用。这些发现对数据驱动的需求时间序列估计和预测直接有用。此外,这项研究还为交通分析和政策提供了有关人们流动性的进一步见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
30 weeks
期刊介绍: The European Journal of Transport and Infrastructure Research (EJTIR) is a peer-reviewed scholarly journal, freely accessible through the internet. EJTIR aims to present the results of high-quality scientific research to a readership of academics, practitioners and policy-makers. It is our ambition to be the journal of choice in the field of transport and infrastructure both for readers and authors. To achieve this ambition, EJTIR distinguishes itself from other journals in its field, both through its scope and the way it is published.
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