The dynamic urban link travel speed estimation (DU-LSE) problem has been studied extensively with approaches ranging from model to data driven since it benefits multiple applications in transport mobility, especially in dense cities. However, with drawbacks such as heavy assumption in model-driven and not being capable for big city network in data-driven, there has not been a consensus on the most effective method. This study aims to develop a Sequential Three Step framework to solve the DU-LSE problem using only the passively collected taxi trip data. The framework makes use of two deep learning models namely Traffic Graph Convolution (TGCN) and its recurrent variant TGCN_lstm to capture both spatial and temporal correlation between road segments. The proposed framework has three advantages over similar approaches: (1) it uses only the affordable taxi data and overcomes the data’s incompleteness both in spatial (full GPS trajectory is not available) and temporal (incomplete historic time-series) domain, (2) it is specifically designed to preserve the directionality nature of traffic flow, and (3) it is capable for large networks. The model results and validations suggest the framework can achieve high enough accuracy and will provide valuable mobility data for cities especially those without traffic sensing infrastructure already in place.

动态城市链路行驶速度估计 (DU-LSE) 问题已通过从模型到数据驱动的各种方法进行了广泛研究，因为它有利于交通运输中的多种应用，尤其是在密集城市中。然而，由于模型驱动假设重，数据驱动不适合大城市网络等缺点，目前尚未就最有效的方法达成共识。本研究旨在开发一个顺序三步框架，仅使用被动收集的出租车行程数据来解决 DU-LSE 问题。该框架使用两种深度学习模型，即流量图卷积 (TGCN) 及其循环变体 TGCN _lstm捕获路段之间的空间和时间相关性。所提出的框架与类似方法相比具有三个优点：（1）它仅使用负担得起的出租车数据并克服了数据在空间（完整的 GPS 轨迹不可用）和时间（不完整的历史时间序列）域中的不完整性，（2）它是专门为保持流量的方向性而设计的，并且（3）它能够用于大型网络。模型结果和验证表明该框架可以达到足够高的准确性，并将为城市提供有价值的移动数据，尤其是那些没有交通传感基础设施的城市。