In vehicle trajectory prediction, physics models and data-driven models are
two predominant methodologies. However, each approach presents its own set of
challenges: physics models fall short in predictability, while data-driven
models lack interpretability. Addressing these identified shortcomings, this
paper proposes a novel framework, the Physics-Enhanced Residual Learning (PERL)
model. PERL integrates the strengths of physics-based and data-driven methods
for traffic state prediction. PERL contains a physics model and a residual
learning model. Its prediction is the sum of the physics model result and a
predicted residual as a correction to it. It preserves the interpretability
inherent to physics-based models and has reduced data requirements compared to
data-driven methods. Experiments were conducted using a real-world vehicle
trajectory dataset. We proposed a PERL model, with the Intelligent Driver Model
(IDM) as its physics car-following model and Long Short-Term Memory (LSTM) as
its residual learning model. We compare this PERL model with the physics
car-following model, data-driven model, and other physics-informed neural
network (PINN) models. The result reveals that PERL achieves better prediction
with a small dataset, compared to the physics model, data-driven model, and
PINN model. Second, the PERL model showed faster convergence during training,
offering comparable performance with fewer training samples than the
data-driven model and PINN model. Sensitivity analysis also proves comparable
performance of PERL using another residual learning model and a physics
car-following model.

该论文提出了一种名为 Physics-Enhanced Residual Learning（PERL）的新型框架，将基于物理的模型与数据驱动模型相结合，用于交通状态预测。PERL 模型综合了物理模型和残差学习模型，其预测结果是基于物理模型和预测的残差之和，保持了物理模型的可解释性并降低了对数据的要求。实验证明，PERL 模型在小规模数据集上取得了更好的预测效果，并且在训练过程中收敛更快，与数据驱动模型相比使用更少的训练样本达到了相似性能。敏感性分析也证明了使用其他残差学习模型和基于物理的车辆跟驰模型时 PERL 模型的可比较性能。

物理增强的残差学习（PERL）框架用于交通状态预测

A Physics Enhanced Residual Learning (PERL) Framework for Traffic State  Prediction

Accurate traffic forecasting at intersections governed by intelligent traffic
signals is critical for the advancement of an effective intelligent traffic
signal control system. However, due to the irregular traffic time series
produced by intelligent intersections, the traffic forecasting task becomes
much more intractable and imposes three major new challenges: 1) asynchronous
spatial dependency, 2) irregular temporal dependency among traffic data, and 3)
variable-length sequence to be predicted, which severely impede the performance
of current traffic forecasting methods. To this end, we propose an Asynchronous
Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic
states of the lanes entering intelligent intersections in a future time window.
Specifically, by linking lanes via a traffic diffusion graph, we first propose
an Asynchronous Graph Diffusion Network to model the asynchronous spatial
dependency between the time-misaligned traffic state measurements of lanes.
After that, to capture the temporal dependency within irregular traffic state
sequence, a learnable personalized time encoding is devised to embed the
continuous time for each lane. Then we propose a Transformable Time-aware
Convolution Network that learns meta-filters to derive time-aware convolution
filters with transformable filter sizes for efficient temporal convolution on
the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network
consisting of a state evolution unit and a semiautoregressive predictor is
designed to effectively and efficiently predict variable-length traffic state
sequences. Extensive experiments on two real-world datasets demonstrate the
effectiveness of ASeer in six metrics.

通过异步时空图卷积网络（ASeer）来预测智能路口进入车道在未来时间窗口内的交通状态，试验结果表明 ASeer 在六个指标上的效果显著。

基于异步时空图卷积网络的不规则交通时间序列预测

Irregular Traffic Time Series Forecasting Based on Asynchronous  Spatio-Temporal Graph Convolutional Network

Accurate and real-time traffic state prediction is of great practical
importance for urban traffic control and web mapping services (e.g. Google
Maps). With the support of massive data, deep learning methods have shown their
powerful capability in capturing the complex spatio-temporal patterns of road
networks. However, existing approaches use independent components to model
temporal and spatial dependencies and thus ignore the heterogeneous
characteristics of traffic flow that vary with time and space. In this paper,
we propose a novel dynamic graph convolution network with spatio-temporal
attention fusion. The method not only captures local spatio-temporal
information that changes over time, but also comprehensively models
long-distance and multi-scale spatio-temporal patterns based on the fusion
mechanism of temporal and spatial attention. This design idea can greatly
improve the spatio-temporal perception of the model. We conduct extensive
experiments in 4 real-world datasets to demonstrate that our model achieves
state-of-the-art performance compared to 22 baseline models.

本文提出了一种新颖的动态图卷积神经网络，采用时空关注融合的方法，全面地模拟了长距离和多尺度的时空模式，取得了 22 种基线模型之外的最先进表现。