This paper presents a modeling approach to infer scheduling and routing
patterns from digital freight transport activity data for different freight
markets. We provide a complete modeling framework including a new
discrete-continuous decision tree approach for extracting rules from the
freight transport data. We apply these models to collected tour data for the
Netherlands to understand departure time patterns and tour strategies, also
allowing us to evaluate the effectiveness of the proposed algorithm. We find
that spatial and temporal characteristics are important to capture the types of
tours and time-of-day patterns of freight activities. Also, the empirical
evidence indicates that carriers in most of the transport markets are sensitive
to the level of congestion. Many of them adjust the type of tour, departure
time, and the number of stops per tour when facing a congested zone. The
results can be used by practitioners to get more grip on transport markets and
develop freight and traffic management measures.

从数字货运活动数据中推断调度和路径模式的建模方法，应用于荷兰的巡回数据以了解发车时间模式和巡回策略，并评估所提出算法的有效性。研究发现空间和时间特征对捕捉巡回类型和货运活动的时间模式很重要，同时实证证据表明运输市场的多数承运商对拥堵程度敏感，在面对拥堵区时调整巡回类型、发车时间和巡回次数。该研究结果可以被从业人员用于更好地理解交通市场并制定货运和交通管理措施。

货运运输的时空特征：基于数据驱动的探索性分析

Spatial and Temporal Characteristics of Freight Tours: A Data-Driven  Exploratory Analysis

Tables stored in databases and tables which are present in web pages and
articles account for a large part of semi-structured data that is available on
the internet. It then becomes pertinent to develop a modeling approach with
large language models (LLMs) that can be used to solve diverse table tasks such
as semantic parsing, question answering as well as classification problems.
Traditionally, there existed separate models specialized for each task
individually. It raises the question of how far can we go to build a unified
model that works well on some table tasks without significant degradation on
others. To that end, we attempt at creating a shared modeling approach in the
pretraining stage with encoder-decoder style LLMs that can cater to diverse
tasks. We evaluate our approach that continually pretrains and finetunes
different model families of T5 with data from tables and surrounding context,
on these downstream tasks at different model scales. Through multiple ablation
studies, we observe that our pretraining with self-supervised objectives can
significantly boost the performance of the models on these tasks. As an example
of one improvement, we observe that the instruction finetuned public models
which come specialized on text question answering (QA) and have been trained on
table data still have room for improvement when it comes to table specific QA.
Our work is the first attempt at studying the advantages of a unified approach
to table specific pretraining when scaled from 770M to 11B sequence to sequence
models while also comparing the instruction finetuned variants of the models.

通过使用大型语言模型，我们尝试创建一个共享的建模方法，在预训练阶段使用编码器 - 解码器风格的大型语言模型，以适用于各种表格任务，并观察到自我监督目标的预训练可以显著提升模型在这些任务上的性能.