The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 14 new columns ({'order_id', 'delivery_gps_lat', 'accept_gps_lng', 'delivery_gps_time', 'accept_gps_time', 'aoi_id', 'delivery_gps_lng', 'region_id', 'delivery_time', 'city', 'courier_id', 'accept_time', 'accept_gps_lat', 'aoi_type'}) and 2 missing columns ({'gps_time', 'postman_id'}). This happened while the csv dataset builder was generating data using hf://datasets/Cainiao-AI/LaDe/delivery/delivery_cq.csv (at revision be2cec02775cafc8d52230303f32134382bcc50b) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast order_id: int64 region_id: int64 city: string courier_id: int64 lng: double lat: double aoi_id: int64 aoi_type: int64 accept_time: string accept_gps_time: string accept_gps_lng: double accept_gps_lat: double delivery_time: string delivery_gps_time: string delivery_gps_lng: double delivery_gps_lat: double ds: int64 -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2289 to {'ds': Value(dtype='int64', id=None), 'postman_id': Value(dtype='string', id=None), 'gps_time': Value(dtype='string', id=None), 'lat': Value(dtype='float64', id=None), 'lng': Value(dtype='float64', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 14 new columns ({'order_id', 'delivery_gps_lat', 'accept_gps_lng', 'delivery_gps_time', 'accept_gps_time', 'aoi_id', 'delivery_gps_lng', 'region_id', 'delivery_time', 'city', 'courier_id', 'accept_time', 'accept_gps_lat', 'aoi_type'}) and 2 missing columns ({'gps_time', 'postman_id'}). This happened while the csv dataset builder was generating data using hf://datasets/Cainiao-AI/LaDe/delivery/delivery_cq.csv (at revision be2cec02775cafc8d52230303f32134382bcc50b) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ds
int64 | postman_id
string | gps_time
string | lat
float64 | lng
float64 |
---|---|---|---|---|
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 00:01:04 | 2,375,270.941943 | 1,566,730.575129 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 00:16:04 | 2,375,280.057346 | 1,566,754.00207 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 00:31:04 | 2,375,272.989632 | 1,566,753.28294 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 00:46:04 | 2,375,285.822973 | 1,566,752.845665 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 01:01:04 | 2,375,268.153828 | 1,566,752.803221 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 01:16:04 | 2,375,287.123177 | 1,566,730.848048 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 01:31:04 | 2,375,276.522651 | 1,566,742.993203 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 01:46:04 | 2,375,296.796619 | 1,566,755.212527 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 02:01:04 | 2,375,284.891912 | 1,566,738.800399 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 02:16:04 | 2,375,292.516728 | 1,566,728.5205 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 02:31:04 | 2,375,297.352988 | 1,566,734.85147 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 02:46:04 | 2,375,296.796029 | 1,566,747.722905 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 02:50:33 | 2,375,294.935199 | 1,566,736.015915 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 03:01:04 | 2,375,297.911034 | 1,566,735.789011 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 03:16:04 | 2,375,294.378167 | 1,566,747.951149 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 03:31:04 | 2,375,289.72657 | 1,566,724.768997 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 03:46:04 | 2,375,277.266449 | 1,566,740.888536 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 04:01:04 | 2,375,275.221616 | 1,566,754.458555 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 04:16:04 | 2,375,274.105136 | 1,566,747.668405 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 04:31:04 | 2,375,292.519069 | 1,566,758.244913 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 04:46:04 | 2,375,285.636226 | 1,566,743.249144 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 05:01:04 | 2,375,309.628877 | 1,566,741.200306 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 05:16:04 | 2,375,291.402645 | 1,566,752.156914 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 05:30:41 | 2,375,312.603217 | 1,566,722.015329 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 05:45:43 | 2,375,290.98939 | 1,566,758.779775 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 06:01:04 | 2,375,300.330334 | 1,566,753.816708 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 06:16:04 | 2,375,283.590879 | 1,566,750.265747 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 06:31:04 | 2,375,293.261503 | 1,566,738.8205 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 06:46:04 | 2,375,286.380688 | 1,566,749.570296 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:01:04 | 2,375,283.776796 | 1,566,749.329991 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:02:04 | 2,375,283.776796 | 1,566,749.329991 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:03:04 | 2,375,283.776796 | 1,566,749.329991 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:04:04 | 2,375,283.776796 | 1,566,749.329991 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:05:04 | 2,375,273.361264 | 1,566,748.836871 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:06:04 | 2,375,273.361264 | 1,566,748.836871 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:07:04 | 2,375,273.361264 | 1,566,748.836871 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:08:06 | 2,375,291.964488 | 1,566,801.309 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:09:06 | 2,375,305.169265 | 1,566,794.085112 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:10:06 | 2,375,305.169265 | 1,566,794.085112 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:11:06 | 2,375,309.818591 | 1,566,788.479041 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:12:06 | 2,375,299.217412 | 1,566,792.198408 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:13:06 | 2,375,299.217541 | 1,566,793.836769 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:14:06 | 2,375,299.217541 | 1,566,793.836769 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:15:06 | 2,375,294.753724 | 1,566,793.357947 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:16:06 | 2,375,322.652813 | 1,566,799.510271 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:17:06 | 2,375,324.884903 | 1,566,802.090196 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:18:06 | 2,375,312.236894 | 1,566,793.868029 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:19:06 | 2,375,360.215414 | 1,566,704.809899 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:20:06 | 2,375,310.376176 | 1,566,783.565302 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:21:22 | 2,375,291.657306 | 1,566,788.967523 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:22:27 | 2,375,306.290646 | 1,566,863.133195 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:23:27 | 2,375,317.265499 | 1,566,880.947576 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:24:27 | 2,375,311.127511 | 1,566,877.187989 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:25:27 | 2,375,464.925765 | 1,566,681.421829 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:26:27 | 2,375,433.836967 | 1,566,318.576776 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:27:27 | 2,375,101.827852 | 1,566,124.696331 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:28:27 | 2,374,696.353109 | 1,565,995.935187 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:29:27 | 2,374,161.428638 | 1,565,869.675041 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:30:27 | 2,374,152.315222 | 1,565,871.525488 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:31:27 | 2,374,109.905061 | 1,565,817.362554 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:32:27 | 2,373,838.353818 | 1,565,729.421961 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:33:27 | 2,373,233.144937 | 1,565,471.976273 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:34:27 | 2,373,246.82695 | 1,564,445.854256 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:35:27 | 2,373,249.423237 | 1,564,351.322953 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:36:27 | 2,373,247.741286 | 1,564,247.188502 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:37:27 | 2,373,246.045852 | 1,563,970.833618 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:38:27 | 2,372,853.824447 | 1,563,919.393067 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:39:27 | 2,372,697.780875 | 1,563,689.964552 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:40:27 | 2,372,495.832469 | 1,563,757.607119 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:41:44 | 2,372,495.645656 | 1,563,732.724359 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:42:53 | 2,372,486.628777 | 1,563,678.402337 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:43:56 | 2,372,484.179749 | 1,563,631.800724 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:45:15 | 2,372,490.439598 | 1,563,756.425288 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:46:15 | 2,372,435.016014 | 1,563,648.668443 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:47:18 | 2,372,315.308081 | 1,563,672.226208 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:48:46 | 2,372,414.493747 | 1,563,613.412796 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:49:50 | 2,372,422.063473 | 1,563,611.728407 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:50:54 | 2,372,321.587143 | 1,563,689.602917 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:52:04 | 2,372,329.556976 | 1,563,668.155579 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:53:07 | 2,372,308.406273 | 1,563,687.806905 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:54:10 | 2,372,410.841554 | 1,563,645.807637 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:55:10 | 2,372,461.048534 | 1,563,630.942492 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:56:10 | 2,372,406.188671 | 1,563,594.554174 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:57:10 | 2,372,399.688255 | 1,563,535.510097 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:58:29 | 2,372,405.365549 | 1,563,544.353077 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 07:59:40 | 2,372,399.122142 | 1,563,591.496891 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:00:40 | 2,372,452.501742 | 1,563,723.349273 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:01:40 | 2,372,422.190303 | 1,563,715.795136 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:02:41 | 2,372,414.493747 | 1,563,729.215176 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:03:48 | 2,372,402.582562 | 1,563,717.42433 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:05:02 | 2,372,421.729514 | 1,563,696.509647 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:06:04 | 2,372,451.193612 | 1,563,640.982329 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:07:04 | 2,372,445.43554 | 1,563,725.20568 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:08:08 | 2,372,412.489997 | 1,563,711.248177 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:09:08 | 2,372,396.34867 | 1,563,715.739924 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:10:22 | 2,372,432.979251 | 1,563,760.74485 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:11:22 | 2,372,477.606766 | 1,563,733.93399 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:12:38 | 2,372,498.317324 | 1,563,731.882155 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:13:58 | 2,372,465.366755 | 1,563,678.823437 |
318 | 01890dd2fdc077b8deb7d8c120bf9c9f | 03-18 08:14:58 | 2,372,495.458524 | 1,563,731.633394 |
Dataset Download: https://huggingface.co/datasets/Cainiao-AI/LaDe/tree/main
Dataset Website: https://cainiaotechai.github.io/LaDe-website/
Code Link:https://github.com/wenhaomin/LaDe
Paper Link: https://arxiv.org/abs/2306.10675
1. About Dataset
LaDe is a publicly available last-mile delivery dataset with millions of packages from industry. It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2) Comprehensive information, it offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen. (3) Diversity: the dataset includes data from various scenarios, such as package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations.
2. Download
LaDe is composed of two subdatasets: i) LaDe-D, which comes from the package delivery scenario. ii) LaDe-P, which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.
LaDe can be used for research purposes. Before you download the dataset, please read these terms. And Code link. Then put the data into "./data/raw/".
The structure of "./data/raw/" should be like:
* ./data/raw/
* delivery
* delivery_sh.csv
* ...
* pickup
* pickup_sh.csv
* ...
* road-network
* roads.csv
* data_with_trajectory_20s
* courier_detailed_trajectory_20s.pkl.xz
road-network/roads.csv records the road network of the five cities.
data_with_trajectory_20s/* records the trajectory of courier.
import pandas as pd
>>> import pandas as pd
>>> df = pd.read_pickle("courier_detailed_trajectory_20s.pkl.xz")
>>> df.head(3)
ds postman_id gps_time lat lng
0 321 106f5ac22cfd1574b196d16fed62f90d 03-21 07:31:58 3.953700e+06 3.053400e+06
1 321 106f5ac22cfd1574b196d16fed62f90d 03-21 07:32:18 3.953700e+06 3.053398e+06
2 321 106f5ac22cfd1574b196d16fed62f90d 03-21 07:32:41 3.953700e+06 3.053398e+06
Each sub-dataset (delivery, pickup) contains 5 CSV files, with each representing the data from a specific city, the detail of each city can be find in the following table.
City | Description |
---|---|
Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
Jilin | A middle-size city in China, with a small number of orders each day. |
Yantai | A small city in China, with a small number of orders every day. |
3. Description
Below is the detailed field of each sub-dataset.
3.1 LaDe-P
Data field | Description | Unit/format |
---|---|---|
Package information | ||
package_id | Unique identifier of each package | Id |
time_window_start | Start of the required time window | Time |
time_window_end | End of the required time window | Time |
Stop information | ||
lng/lat | Coordinates of each stop | Float |
city | City | String |
region_id | Id of the Region | String |
aoi_id | Id of the AOI (Area of Interest) | Id |
aoi_type | Type of the AOI | Categorical |
Courier Information | ||
courier_id | Id of the courier | Id |
Task-event Information | ||
accept_time | The time when the courier accepts the task | Time |
accept_gps_time | The time of the GPS point closest to accept time | Time |
accept_gps_lng/lat | Coordinates when the courier accepts the task | Float |
pickup_time | The time when the courier picks up the task | Time |
pickup_gps_time | The time of the GPS point closest to pickup_time | Time |
pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float |
Context information | ||
ds | The date of the package pickup | Date |
3.2 LaDe-D
Data field | Description | Unit/format |
---|---|---|
Package information | ||
package_id | Unique identifier of each package | Id |
Stop information | ||
lng/lat | Coordinates of each stop | Float |
city | City | String |
region_id | Id of the region | Id |
aoi_id | Id of the AOI | Id |
aoi_type | Type of the AOI | Categorical |
Courier Information | ||
courier_id | Id of the courier | Id |
Task-event Information | ||
accept_time | The time when the courier accepts the task | Time |
accept_gps_time | The time of the GPS point whose time is the closest to accept time | Time |
accept_gps_lng/accept_gps_lat | Coordinates when the courier accepts the task | Float |
delivery_time | The time when the courier finishes delivering the task | Time |
delivery_gps_time | The time of the GPS point whose time is the closest to the delivery time | Time |
delivery_gps_lng/delivery_gps_lat | Coordinates when the courier finishes the task | Float |
Context information | ||
ds | The date of the package delivery | Date |
4. Leaderboard
Blow shows the performance of different methods in Shanghai.
4.1 Route Prediction
Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
Method | HR@3 | KRC | LSD | ED |
---|---|---|---|---|
TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
4.2 Estimated Time of Arrival Prediction
Method | MAE | RMSE | ACC@30 |
---|---|---|---|
LightGBM | 30.99 | 35.04 | 0.59 |
SPEED | 23.75 | 27.86 | 0.73 |
KNN | 36.00 | 31.89 | 0.58 |
MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 |
FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
4.3 Spatio-temporal Graph Forecasting
Method | MAE | RMSE |
---|---|---|
HA | 4.63 | 9.91 |
DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
5. Citation
If you find this helpful, please cite our paper:
@misc{wu2023lade,
title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry},
author={Lixia Wu and Haomin Wen and Haoyuan Hu and Xiaowei Mao and Yutong Xia and Ergang Shan and Jianbin Zhen and Junhong Lou and Yuxuan Liang and Liuqing Yang and Roger Zimmermann and Youfang Lin and Huaiyu Wan},
year={2023},
eprint={2306.10675},
archivePrefix={arXiv},
primaryClass={cs.DB}
}
- Downloads last month
- 525