With the arrival of the big data era, mobility profiling has become a viable
method of utilizing enormous amounts of mobility data to create an intelligent
transportation system. Mobility profiling can extract potential patterns in
urban traffic from mobility data and is critical for a variety of
traffic-related applications. However, due to the high level of complexity and
the huge amount of data, mobility profiling faces huge challenges. Digital Twin
(DT) technology paves the way for cost-effective and performance-optimised
management by digitally creating a virtual representation of the network to
simulate its behaviour. In order to capture the complex spatio-temporal
features in traffic scenario, we construct alignment diagrams to assist in
completing the spatio-temporal correlation representation and design dilated
alignment convolution network (DACN) to learn the fine-grained correlations,
i.e., spatio-temporal interactions. We propose a digital twin mobility
profiling (DTMP) framework to learn node profiles on a mobility network DT
model. Extensive experiments have been conducted upon three real-world
datasets. Experimental results demonstrate the effectiveness of DTMP.

基于大数据和数字孪生技术构建的数字孪生移动性建模框架在交通相关应用中展现了良好的性能和有效性。

数字孪生移动性编制：一种时空图学习方法

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning  Approach

This research presents a novel active detection model utilizing deep
reinforcement learning to accurately detect traffic objects in real-world
scenarios. The model employs a deep Q-network based on LSTM-CNN that identifies
and aligns target zones with specific categories of traffic objects through
implementing a top-down approach with efficient feature extraction of the
environment. The model integrates historical and current actions and
observations to make a comprehensive analysis. The design of the state space
and reward function takes into account the impact of time steps to enable the
model to complete the task in fewer steps. Tests conducted demonstrate the
model's proficiency, exhibiting exceptional precision and performance in
locating traffic signal lights and speed limit signs. The findings of this
study highlight the efficacy and potential of the deep reinforcement
learning-based active detection model in traffic-related applications,
underscoring its robust detection abilities and promising performance.

该研究提出了一种新颖的主动检测模型，利用深度强化学习来准确地检测实际场景中的交通物体。该模型通过实施自上而下的方法，利用基于 LSTM-CNN 的深度 Q 网络来识别和对齐具有特定交通物体类别的目标区域，并通过有效的环境特征提取。模型集成了历史和当前的动作和观察结果，进行综合分析。所设计的状态空间和奖励函数考虑了时间步长的影响，使得模型在较少的步骤中完成任务。进行的测试展示了该模型在定位交通信号灯和速限标志方面的卓越准确性和性能，突出了深度强化学习基于主动检测模型在交通相关应用中的功效和潜力，强调了其强大的检测能力和有 promising perfo rmance。