Recent achievements in deep learning (DL) have shown its potential for
predicting traffic flows. Such predictions are beneficial for understanding the
situation and making decisions in traffic control. However, most
state-of-the-art DL models are considered "black boxes" with little to no
transparency for end users with respect to the underlying mechanisms. Some
previous work tried to "open the black boxes" and increase the interpretability
of how predictions are generated. However, it still remains challenging to
handle complex models on large-scale spatio-temporal data and discover salient
spatial and temporal patterns that significantly influence traffic flows. To
overcome the challenges, we present TrafPS, a visual analytics approach for
interpreting traffic prediction outcomes to support decision-making in traffic
management and urban planning. The measurements, region SHAP and trajectory
SHAP, are proposed to quantify the impact of flow patterns on urban traffic at
different levels. Based on the task requirement from the domain experts, we
employ an interactive visual interface for multi-aspect exploration and
analysis of significant flow patterns. Two real-world case studies demonstrate
the effectiveness of TrafPS in identifying key routes and decision-making
support for urban planning.

利用 TrafPS 可视化分析方法，通过测量、区域 SHAP 和轨迹 SHAP，来量化不同层次上流量模式对城市交通的影响，为交通管理和城市规划决策提供支持。

TrafPS：一种基于 Shapley 的交通解释可视分析方法

TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic

To develop the most appropriate control strategy and monitor, maintain, and
evaluate the traffic performance of the freeway weaving areas, state and local
Departments of Transportation need to have access to traffic flows at each pair
of on-ramp and off-ramp. However, ramp flows are not always readily available
to transportation agencies and little effort has been made to estimate these
missing flows in locations where no physical sensors are installed. To bridge
this research gap, a data-driven framework is proposed that can accurately
estimate the missing ramp flows by solely using data collected from loop
detectors on freeway mainlines. The proposed framework employs a transfer
learning model. The transfer learning model relaxes the assumption that the
underlying data distributions of the source and target domains must be the
same. Therefore, the proposed framework can guarantee high-accuracy estimation
of on-ramp and off-ramp flows on freeways with different traffic patterns,
distributions, and characteristics. Based on the experimental results, the flow
estimation mean absolute errors range between 23.90 veh/h to 40.85 veh/h for
on-ramps, and 31.58 veh/h to 45.31 veh/h for off-ramps; the flow estimation
root mean square errors range between 34.55 veh/h to 57.77 veh/h for on-ramps,
and 41.75 veh/h to 58.80 veh/h for off-ramps. Further, the comparison analysis
shows that the proposed framework outperforms other conventional machine
learning models. The estimated ramp flows based on the proposed method can help
transportation agencies to enhance the operations of their ramp control
strategies for locations where physical sensors are not installed.

通过使用循环检测器收集的数据，提出了一种数据驱动的框架，可以准确估计高速公路主线上缺失的匝道流量，该框架利用了迁移学习模型，可以保证在具有不同交通模式、分布和特征的高速公路上准确估计入口和出口匝道的流量，从而优于传统机器学习模型。

基于数据驱动的迁移学习框架的出入站流量估计

On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven  Transfer Learning Framework

There is extensive literature on perceiving road structures by fusing various
sensor inputs such as lidar point clouds and camera images using deep neural
nets. Leveraging the latest advance of neural architects (such as transformers)
and bird-eye-view (BEV) representation, the road cognition accuracy keeps
improving. However, how to cognize the ``road'' for automated vehicles where
there is no well-defined ``roads'' remains an open problem. For example, how to
find paths inside intersections without HD maps is hard since there is neither
an explicit definition for ``roads'' nor explicit features such as lane
markings. The idea of this paper comes from a proverb: it becomes a way when
people walk on it. Although there are no ``roads'' from sensor readings, there
are ``roads'' from tracks of other vehicles. In this paper, we propose FlowMap,
a path generation framework for automated vehicles based on traffic flows.
FlowMap is built by extending our previous work RoadMap, a light-weight
semantic map, with an additional traffic flow layer. A path generation
algorithm on traffic flow fields (TFFs) is proposed to generate human-like
paths. The proposed framework is validated using real-world driving data and is
amenable to generating paths for super complicated intersections without using
HD maps.

本文提出了一种基于交通流的路径生成框架 FlowMap，该框架可以为自动驾驶车辆生成人类模拟路径，并且可以在不使用高精度地图的情况下应对超级复杂的十字路口。

FlowMap: 交通流量下无人驾驶车辆的路径生成

FlowMap: Path Generation for Automated Vehicles in Open Space Using  Traffic Flow

This paper focuses on devising graph signal processing tools for the
treatment of data defined on the edges of a graph. We first show that
conventional tools from graph signal processing may not be suitable for the
analysis of such signals. More specifically, we discuss how the underlying
notion of a `smooth signal' inherited from (the typically considered variants
of) the graph Laplacian are not suitable when dealing with edge signals that
encode a notion of flow. To overcome this limitation we introduce a class of
filters based on the Edge-Laplacian, a special case of the Hodge-Laplacian for
simplicial complexes of order one. We demonstrate how this Edge-Laplacian leads
to low-pass filters that enforce (approximate) flow-conservation in the
processed signals. Moreover, we show how these new filters can be combined with
more classical Laplacian-based processing methods on the line-graph. Finally,
we illustrate the developed tools by denoising synthetic traffic flows on the
London street network.

本文介绍了一种基于 Edge-Laplacian 的图信号处理工具，能够有效处理编码流动概念的边缘信号，提出的新滤波器能够实现 (近似) 流量守恒和降噪，举例说明了在伦敦街道网络上处理合成交通流的具体应用。