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Tfty jinyan218 commited on
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Duplicate from Cainiao-AI/LaDe-P

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Co-authored-by: LJH <jinyan218@users.noreply.huggingface.co>

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  1. .gitattributes +55 -0
  2. README.md +185 -0
  3. data/pickup_cq-00000-of-00001-a172031e5392f9d3.parquet +3 -0
  4. data/pickup_hz-00000-of-00001-2641abebfe50648a.parquet +3 -0
  5. data/pickup_jl-00000-of-00001-9b430a56a935f284.parquet +3 -0
  6. data/pickup_sh-00000-of-00001-79fabe8088e723a2.parquet +3 -0
  7. data/pickup_yt-00000-of-00001-6d21a4dccd28ee03.parquet +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - Spatial-Temporal
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+ - Graph
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+ - Logistic
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+ size_categories:
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+ - 10M<n<100M
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+ dataset_info:
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+ features:
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+ - name: order_id
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+ dtype: int64
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+ - name: region_id
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+ dtype: int64
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+ - name: city
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+ dtype: string
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+ - name: courier_id
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+ dtype: int64
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+ - name: accept_time
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+ dtype: string
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+ - name: time_window_start
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+ dtype: string
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+ - name: time_window_end
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+ dtype: string
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+ - name: lng
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+ dtype: float64
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+ - name: lat
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+ dtype: float64
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+ - name: aoi_id
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+ dtype: int64
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+ - name: aoi_type
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+ dtype: int64
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+ - name: pickup_time
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+ dtype: string
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+ - name: pickup_gps_time
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+ dtype: string
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+ - name: pickup_gps_lng
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+ dtype: float64
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+ - name: pickup_gps_lat
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+ dtype: float64
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+ - name: accept_gps_time
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+ dtype: string
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+ - name: accept_gps_lng
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+ dtype: float64
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+ - name: accept_gps_lat
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+ dtype: float64
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+ - name: ds
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+ dtype: int64
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+ splits:
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+ - name: pickup_jl
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+ num_bytes: 54225579
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+ num_examples: 261801
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+ - name: pickup_cq
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+ num_bytes: 243174931
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+ num_examples: 1172703
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+ - name: pickup_yt
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+ num_bytes: 237146694
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+ num_examples: 1146781
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+ - name: pickup_sh
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+ num_bytes: 293399390
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+ num_examples: 1424406
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+ - name: pickup_hz
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+ num_bytes: 436103754
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+ num_examples: 2130456
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+ download_size: 443251368
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+ dataset_size: 1264050348
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+ ---
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+ # 1. About Dataset
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+ **LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry.
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+ It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation.
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+ (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.
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+ (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.
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+
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+ If you use this dataset for your research, please cite this paper: {xxx}
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+
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+ # 2. Download
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+ [LaDe](https://huggingface.co/datasets/Cainiao-AI/LaDe) is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
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+ ii) [LaDe-P](https://huggingface.co/datasets/Cainiao-AI/LaDe-P), which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.
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+
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+ LaDe-P is the second subdataset from [LaDe](https://huggingface.co/datasets/Cainiao-AI/LaDe)
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+ LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](https://github.com/wenhaomin/LaDe). Then put the data into "./data/raw/".
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+ The structure of "./data/raw/" should be like:
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+ ```
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+ * ./data/raw/
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+ * pickup
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+ * pickup_sh.csv
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+ * ...
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+ ```
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+
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+ LaDe-P contains files, with each representing the data from a specific city, the detail of each city can be find in the following table.
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+
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+
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+ | City | Description |
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+ |------------|----------------------------------------------------------------------------------------------|
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+ | Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
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+ | Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
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+ | Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
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+ | Jilin | A middle-size city in China, with a small number of orders each day. |
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+ | Yantai | A small city in China, with a small number of orders every day. |
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+
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+
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+ # 3. Description
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+ Below is the detailed field of each LaDe-P.
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+
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+ | Data field | Description | Unit/format |
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+ |----------------------------|----------------------------------------------|--------------|
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+ | **Package information** | | |
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+ | package_id | Unique identifier of each package | Id |
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+ | time_window_start | Start of the required time window | Time |
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+ | time_window_end | End of the required time window | Time |
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+ | **Stop information** | | |
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+ | lng/lat | Coordinates of each stop | Float |
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+ | city | City | String |
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+ | region_id | Id of the Region | String |
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+ | aoi_id | Id of the AOI (Area of Interest) | Id |
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+ | aoi_type | Type of the AOI | Categorical |
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+ | **Courier Information** | | |
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+ | courier_id | Id of the courier | Id |
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+ | **Task-event Information** | | |
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+ | accept_time | The time when the courier accepts the task | Time |
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+ | accept_gps_time | The time of the GPS point closest to accept time | Time |
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+ | accept_gps_lng/lat | Coordinates when the courier accepts the task | Float |
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+ | pickup_time | The time when the courier picks up the task | Time |
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+ | pickup_gps_time | The time of the GPS point closest to pickup_time | Time |
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+ | pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float |
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+ | **Context information** | | |
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+ | ds | The date of the package pickup | Date |
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+
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+
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+ # 4. Leaderboard
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+ Blow shows the performance of different methods in Shanghai.
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+ ## 4.1 Route Prediction
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+
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+ Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
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+
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+ | Method | HR@3 | KRC | LSD | ED |
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+ |--------------|--------------|--------------|-------------|-------------|
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+ | TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
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+ | DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
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+ | OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
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+ | LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
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+ | FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
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+ | DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
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+ | Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
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+
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+
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+ ## 4.2 Estimated Time of Arrival Prediction
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+
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+ | Method | MAE | RMSE | ACC@30 |
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+ | ------ |--------------|--------------|-------------|
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+ | LightGBM | 30.99 | 35.04 | 0.59 |
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+ | SPEED | 23.75 | 27.86 | 0.73 |
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+ | KNN | 36.00 | 31.89 | 0.58 |
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+ | MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 |
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+ | FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
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+
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+
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+ ## 4.3 Spatio-temporal Graph Forecasting
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+
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+
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+ | Method | MAE | RMSE |
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+ |-------|-------------|-------------|
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+ | HA | 4.63 | 9.91 |
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+ | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
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+ | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
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+ | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
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+ | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
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+ | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
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+ | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
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+ | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
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+
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+
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+
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+ # 5. Citation
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+ To cite this repository:
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+
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+ ```shell
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+ @software{pytorchgithub,
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+ author = {xx},
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+ title = {xx},
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+ url = {xx},
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+ version = {0.6.x},
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+ year = {2021},
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+ }
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+ ```
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