Paper: LaDe: The First Comprehensive Last-mile Express Dataset from Industry (ICLR-2024)

Reviewer comment (Presentation):
Can the font of axis labels on images/graphs in figures 3 and 4 be enlarged?

Author response:
Thanks for the comment. We will add the related works and revise the figure in the revision.

Reviewer comment (Evaluation):
While the dataset's findings may be valuable for last-mile delivery research, it's crucial for the authors to critically evaluate its broader utility and acknowledge any limitations. The authors can try to include several points regarding broader applicability and limitations:  
* Geographic Diversity: Last-mile delivery challenges can vary significantly across regions, influenced by factors like urban layout, road infrastructure, population density, and geographical features. It's essential to explore how well the dataset's findings can be extrapolated to cities with different characteristics, such as those in other countries or even within China.  
* Cultural Factors: The cultural norms and consumer behavior in China might differ from those in other countries. How do these cultural factors affect the dataset's applicability in regions with distinct consumer preferences or expectations? This could include considerations related to delivery timeframes, delivery modes, and consumer attitudes toward delivery services.  
* Environmental Factors: Environmental conditions, including weather, air quality, and traffic congestion, play a significant role in last-mile delivery operations. The authors should analyze whether the dataset's findings are sensitive to these environmental factors and whether the methods proposed can adapt to varying conditions in different regions.  
* Regulatory and Infrastructure Variances: Different countries and regions may have unique regulatory frameworks and transportation infrastructure. How might these variations impact the implementation and effectiveness of the last-mile delivery strategies derived from the dataset?  
* Socio-Economic Disparities: Socio-economic disparities can affect last-mile delivery in various ways. It's important to assess whether the dataset captures these disparities and how they may differ in other regions. Are the strategies proposed in the study suitable for addressing disparities in different socio-economic contexts?  

Including the above points will enhance the practical relevance and impact of the research for a wider audience and encourage further discussion on adapting and extending the findings to different international settings.

Author response:
Thanks for the valuable suggestions. We will improve the dataset by considering the given points.

Reviewer comment (Novelty):
Although the proposed dataset can support the research in several research topics, it can only support a narrow research area in AI. Thus, its impacts may be limited.

Author response:
Q1:Thanks for the comments. In addition to primary tasks, the dataset can provide substantial support for a wide range of other tasks in different research fields. Firstly, LaDe can be used for representation learning, which involves many topics that can be broadly classified by the representation object: i) POI (Point of Interest) representation learning , i.e., to learn the representation of the pickup/delivery location. ii) trajectory representation learning, i.e., to learn the representation of courier trajectory; ii) AOI representation learning. Secondly, LaDe can be utilized to verify algorithms for optimization problems, such as the vehicle route problem, delivery scheduling problem. Moreover, benefiting from its large data volume and detailed information, LaDe shows great potential to support the development of foundation models in geo-related domains.

Reviewer comment (Experiments):
Besides the logistics, supply chain management, and spatio-temporal data mining, are there any other deep learning related research areas that can use this dataset for experimental evaluation?

Author response:
Q1:Thanks for the comments. In addition to primary tasks, the dataset can provide substantial support for a wide range of other tasks in different research fields. Firstly, LaDe can be used for representation learning, which involves many topics that can be broadly classified by the representation object: i) POI (Point of Interest) representation learning , i.e., to learn the representation of the pickup/delivery location. ii) trajectory representation learning, i.e., to learn the representation of courier trajectory; ii) AOI representation learning. Secondly, LaDe can be utilized to verify algorithms for optimization problems, such as the vehicle route problem, delivery scheduling problem. Moreover, benefiting from its large data volume and detailed information, LaDe shows great potential to support the development of foundation models in geo-related domains.

Reviewer comment (Evaluation):
This dataset is collected in several cities in China. Will the models developed on this dataset be suitable to the applications in other countries?

Author response:
Q2: Thanks for the comments. We acknowledge that LaDe may be limited to one country. However,  it is quite difficult for a single company to collect data from different countries. To address this challenge, we record data from different cities with different characteristics so that models developed in this dataset can generalize well in different scenarios (such as in different countries). Many famous datasets only contain data from one country, such as the NYC taxi data (https://github.com/toddwschneider/nyc-taxi-data) and data mentioned in related work.  Our work contributes the first pick-up and delivery dataset from the industry,  which should not be neglected mainly because it is from one country.

Reviewer comment (Novelty):
The data fields of the dataset proposed in the article appear relatively straightforward and, in essence, do not significantly distinguish themselves from the data fields found in other existing Pick-up & Delivery datasets.

Author response:
Thanks for the comments. We'd like to argue that our compared with previous works,  we are the first publicly available last-mile dataset containing both package pick-up and delivery data.

Reviewer comment (Experiments):
The dataset outlined in this research paper reveals certain limitations with respect to its incorporation of road network structure, a pivotal aspect for applications pertaining to pick-up and delivery services. Predominantly relying on GPS locations (x-y coordinates), while undoubtedly valuable, might not comprehensively address the intricacies inherent in real-world logistics operations. The absence of road network information in the dataset does raise valid concerns about its suitability for applications necessitating an in-depth understanding of spatial constraints and the optimization of routing, particularly in the last-mile express domain. To augment the dataset's applicability in such scenarios, it may prove advantageous to contemplate the inclusion of coarse-grained road network data. While acknowledging the privacy concerns associated with road network information, providing a more generalized form of road network data could still facilitate researchers in achieving a more comprehensive and accurate representation of the real-world scenarios under examination.

Author response:
Thanks for the valuable suggestion. We will add the road network to the dataset in the revision.

Reviewer comment (Experiments):
Throughout the article, the authors seek to benchmark several aspects of applications, each of which boasts a significant body of existing works. Yet, the chosen methods in the article appear to fall short of encompassing the full spectrum of relevant works and categories within these benchmarked areas. In light of this, it is imperative that the authors offer more extensive elucidation regarding their decision to select these specific methods as baselines when there are numerous alternatives at their disposal. Providing a comprehensive explanation will not only enhance the transparency and academic rigor of their research but also enable the reader to grasp the underlying reasoning behind these choices.

Author response:
Thanks for the comments. For the route and time prediction task, we argue that we have implemented all related baselines. And for the STG prediction task, there are indeed numerous models proposed in recent years, and it is hard to include all of them considering the limited page of the paper. Therefore, we implemented several basic models (i.e., HA, DCRNN, STGCN), the most popular models (i.e., ASTGCN, MTGNN, AGCRN), as well as the most recent SOTA models (i.e., STGNCDE, GMSDR) as the baselines.