Traffic flow prediction provides essential future views in the intelligent
transportation system. Explainable predictions offer valuable insights into the
factors influencing traffic patterns, which help urban planners, traffic
engineers, and policymakers make informed decisions about infrastructure
development, traffic management strategies, and public transportation planning.
Despite their widespread popularity and commendable accuracy, prediction
methods grounded in deep learning frequently disappoint in terms of
transparency and interpretability. Recently, the availability of large-scale
spatio-temporal data and the development of large language models (LLMs) have
opened up new opportunities for urban traffic prediction. With the popularity
of LLMs, people witnessed the potential reasoning and generating ability of
foundation models in various tasks. Considering text as input and output, LLMs
have advantages in generating more intuitive and interpretable predictions.
Hence, this work introduces TP-LLM, an explainable foundation-model-based
method for traffic prediction, aiming at more direct and reasonable
forecasting. TP-LLM presents a framework to unify multi-modality factors as
language-based inputs, TP-LLM avoids complex spatial-temporal data programming
and outperforms state-of-art baselines merely under fine-tuning foundation
models. Also, TP-LLM can generate input-dependency explanations for more
confident prediction and can be easily generalized to different city dynamics
for zero-shot prediction with a similar framework. These findings demonstrate
the potential of LLMs for explainable traffic prediction.

这项研究介绍了一种基于 TP-LLM 的可解释交通预测方法，通过将多模态因素作为语言输入统一起来，避免了复杂的时空数据编程，证明了大语言模型在交通预测方面的潜力。

可解释的大语言模型交通流预测

Explainable Traffic Flow Prediction with Large Language Models

Trees are convenient models for obtaining explainable predictions on
relatively small datasets. Although there are many proposals for the end-to-end
construction of such trees in supervised learning, learning a tree end-to-end
for clustering without labels remains an open challenge. As most works focus on
interpreting with trees the result of another clustering algorithm, we present
here a novel end-to-end trained unsupervised binary tree for clustering: Kauri.
This method performs a greedy maximisation of the kernel KMeans objective
without requiring the definition of centroids. We compare this model on
multiple datasets with recent unsupervised trees and show that Kauri performs
identically when using a linear kernel. For other kernels, Kauri often
outperforms the concatenation of kernel KMeans and a CART decision tree.

使用贪婪最大化核 KMeans 目标，无需定义质心的 Kauri 方法，是一种用于集群的新型端到端训练的无监督二叉树，它在使用线性核时表现相同，对于其他核，Kauri 通常优于核 KMeans 和 CART 决策树的串联。

基于核 KMeans 聚类的端到端无监督决策树分割

Kernel KMeans clustering splits for end-to-end unsupervised decision  trees

Deploying reliable deep learning techniques in interdisciplinary applications
needs learned models to output accurate and ({even more importantly})
explainable predictions. Existing approaches typically explicate network
outputs in a post-hoc fashion, under an implicit assumption that faithful
explanations come from accurate predictions/classifications. We have an
opposite claim that explanations boost (or even determine) classification. That
is, end-to-end learning of explanation factors to augment discriminative
representation extraction could be a more intuitive strategy to inversely
assure fine-grained explainability, e.g., in those neuroimaging and
neuroscience studies with high-dimensional data containing noisy, redundant,
and task-irrelevant information. In this paper, we propose such an explainable
geometric deep network dubbed.

该文提出了一种名为 “可解释的几何深度网络” 的端到端学习方法，用于在高维数据中实现精细的可解释性，例如神经成像和神经科学研究。该方法采用学习解释性因素来增强鉴别性表示提取，以实现可解释的预测和分类结果。