Estimating intercity heavy truck mobility flows using the deep gravity framework
Estimating intercity heavy truck mobility flows using the deep gravity framework
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DOI：
10.1016/j.tre.2023.103320
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Yitao Yang;Bin Jia;Xiao-Yong Yan;Yan Chen;Dongdong Song;Danyue Zhi;Yiyun Wang;Ziyou Gao
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文献类型：
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作者：
Yitao Yang;Bin Jia;Xiao-Yong Yan;Yan Chen;Dongdong Song;Danyue Zhi;Yiyun Wang;Ziyou Gao
城际重卡移动流量的准确估算对城市规划、交通管理和物流运营具有重要意义。与城际运输系统相关的大数据的不可访问性和卡车运输活动的异质性给可靠估计带来了挑战。最近，人工智能（AI）的进步为这个问题提供了一个潜在的解决方案。然而，以往的研究大多集中在区域间乘客流动性的估计上。利用深度学习技术对城际重型卡车移动流量进行评估的深入研究仍然很少。为了填补这一空白，我们构建了一个基于深度重力框架的深度神经网络，这是一种先进的人类流动性预测模型。我们收集了与重型卡车运动、货运地点、道路网络和土地使用相关的广泛数据来训练模型，并通过与传统重力模型进行比较来验证其高性能。此外，我们使用可解释的人工智能技术来解释城市特征如何影响城际重型卡车运动的确定，其结果可以为物流运营、企业和城市规划提供有价值的政策建议。
Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban planning, transportation management and logistics operations. The inaccessibility of big data related to intercity transport systems and the heterogeneity of trucking activities pose challenges for the reliable estimation. Recently, the advance of Artificial Intelligence (AI) provides a potential solution to this problem. However, most previous studies focused on the estimation of inter-regional passenger mobility. In-depth studies of estimating intercity heavy truck mobility flows by using deep learning techniques are still scarce. To fill in the gaps, we construct a deep neural network based on the Deep Gravity framework, an advanced predictive model for human mobility. We collect a wide range of data related to heavy truck movements, freight locations, road networks and land uses to train the model, and validate its high performance by comparing to traditional gravity model. Furthermore, we use an explainable AI technique to interpret how the city features contribute to the determination of intercity heavy truck movements, and the results can provide valuable policy implications for logistics operations, businesses and urban planning.