Representative truck activity patterns from anonymous mobile sensor data
Representative truck activity patterns from anonymous mobile sensor data
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DOI：
10.1016/j.ijtst.2022.05.002
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发表时间：
2022-05
影响因子：
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通讯作者：
T. Akter;S. Hernandez
中科院分区：
文献类型：
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作者：
T. Akter;S. Hernandez
有了新的大数据来源，越来越有可能实际实施先进的货运预测模型，包括基于活动的模型和卡车旅游模型。这些模型通过捕捉对运输政策和基础设施变化敏感的货运行为，改进了传统的基于出行的方法。在这种情况下使用大数据的一个长期挑战是，能否从描述人口复杂行为的匿名数据中概括一组具有代表性的行为，作为模型校准和验证的基础。为了应对这一挑战，我们提出了一种从被动收集的卡车全球定位系统(GPS)数据中提取独特且具有代表性的货运活动模式的两阶段方法。第一阶段涉及一种基于启发式的方法，从大量的GPS ping信号中得出一组停车和行程特征。第二阶段采用数据挖掘和机器学习技术，从定义的特征集中识别常见的货运活动模式。由此产生的活动模式概况，定义为活动链及其在时间和空间上的轨迹，使我们能够保持GPS数据集中卡车的匿名性，同时提供高分辨率的旅行概况--这是公共机构和私人数据提供商之间达成大多数数据共享协议的必要条件。这些活动模式是校准和验证高级货运预测模型所需的关键数据，也是目前缺失的数据。随着更先进的预测模型反映观察到的货运行为，我们将能够更准确地评估更广泛的政策和基础设施情景。
With new sources of big data, it is increasingly possible to practically implement advanced freight forecasting models including activity-based and truck touring models. Such models improve upon traditional trip-based approaches by capturing freight behaviors sensitive to transportation policy and infrastructure changes. A persistent challenge with the use of big data in this context is the ability to generalize a set of representative behaviors to serve as the basis for model calibration and validation from anonymized data depicting the complex behaviors of the population. To address this challenge, we present a two-stage methodology to extract unique and representative freight activity patterns from passively collected truck Global Positioning System (GPS) data. The first stage involved a heuristic-based approach to derive a set of stop and trip characteristics from large-streams of GPS pings. The second stage employed data mining and machine learning techniques to discern common freight activity patterns from the set of defined features. The resulting activity pattern profiles, defined as chains of activities and their trajectories over time and space, allow us to maintain the anonymity of the trucks included in the GPS dataset while providing high-resolution travel profiles- a necessary condition for most data sharing agreements between public agencies and private data providers. These activity patterns serve as the critical, and currently missing, data needed to calibrate and validate advanced freight forecasting models. With more advanced forecasting models reflective of observed freight behaviors, we will be able to evaluate a wider spectrum of policy and infrastructure scenarios more accurately.