Awesome
Traffic-UAGCRNTF
Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
Traffic Prediction models - UAGCRN and UAGCTransformer
CIKM 2023 - "Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis" paper
Dataset
You can download from Download
Traffic Dataset
-
california-vds.json: https://pems.dot.ca.gov/
-
metr-la, pems-bay: DCRNN, https://github.com/liyaguang/DCRNN
-
pemsd7: STGCN, https://github.com/VeritasYin/STGCN_IJCAI-18
Traffic Dataset Description
| DATASET | N | Speed (miles/hour) | Datasize | PLACE | DURATION | INTERVAL |
|---|---|---|---|---|---|---|
| METR_LA | 207 | 54 ± 20 | 34,249 | Los Angeles, USA | Mar. 1, 2012 - Jun. 27, 2012 | 5min |
| PEMS_BAY | 325 | 62 ± 10 | 52,093 | San Francisco Bay Area, USA | Jan. 1, 2017 - Jun. 30, 2017 | 5min |
| PEMSD7 | 228 | 59 ± 13 | 12,652 | Los Angeles, USA | May. 1, 2012 - Jun.30, 2012 | 5min |
National Household Survey
Citation
To recognize the valuable role of National Household Travel Survey (NHTS) data in the transportation research process and to facilitate repeatability of the research, users of NHTS data are asked to formally acknowledge the data source. Where possible, this acknowledgement should take place in the form of a formal citation, such as when writing a research report, planning document, on-line article, and other publications. The citation can be formatted as follows: U.S. Department of Transportation, Federal Highway Administration, 2017 National Household Travel Survey. URL: http://nhts.ornl.gov.
Download page
- https://nhts.ornl.gov/downloads
- survey data csv link: https://nhts.ornl.gov/assets/2016/download/csv.zip
Sensor Location Correction
Corrected sensor location with osm path files are found in
- METR-LA: dataset/corrected-metr-la-sensorid-osm-path-uv.csv
- PEMS-BAY: dataset/corrected-pemsbay-sensorid-osm-path-uv.csv
- PEMSD7: dataset/corrected-pemsd7-sensorid-osm-path-uv.csv
METR-LA
PEMS-BAY
PEMSD7
Code
This installation command worked with our code execution:
conda deactivate
conda env remove -n CIKM
conda create -n CIKM -y
conda activate CIKM
conda install python==3.10 -y
pip install tensorflow-gpu==2.10.0
pip install tqdm
pip install tables
pip install scipy==1.10.1
pip install pandas==1.5.3
pip install numpy==1.23.5
python train.py --model_name=MyUAGCRN --dataset=metr-la --Q=12 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
python train.py --model_name=MyUAGCRN --dataset=pems-bay --Q=12 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
python train.py --model_name=MyUAGCRN --dataset=pemsd7 --Q=9 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
python train.py --model_name=MyUAGCTransformer --dataset=metr-la --Q=12 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
python train.py --model_name=MyUAGCTransformer --dataset=pems-bay --Q=12 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
python train.py --model_name=MyUAGCTransformer --dataset=pemsd7 --Q=9 --activity_embedding --sensor_embedding --graph_type=cooccur_dist
Performance Comparison
Ablation Study
