Seoul City taxi request forecast

---

[ Objective ]

• We want predict the future taxi-demand within 30-min of all locations.

[ Summary ]

• TGNet(temporal guided network) applied Seoul City & NYC taxi datasets.
• It outperforms previous models.

[ Intro ]

• On-demand taxi request service is very convenient in city life.
• Taxi request data shows variety pattern depends on the time and location

Seoul Ride Hailing Requests:
(left) the ride hailing requests in residential and commercial area,
(middle) different patterns in residential area on holiday and
(right) average requests in each day of week

[ Data Processing ]

• To capture the Spatial / Temporal information in the same time
• We use Unet based the fully convolutional network
• To do this, we split the Seoul city to 50x50 grid (approx. 800m x 700m ) and the data aggregated in 30 mins.
• We used the Requests data on the spot and drop-off data on the spot.
• Two data type have different patterns, below shows the results.

Aggregated request data to grid cell, which has in 30 minutes.
(left) Requests data in 08/01/2018 Mon AM 08:00
(right) Drop-off data in same day and same time  

• We also apply the same model to NYC datasets : http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml

• Compare to previous work, NYC-Taxi open dataset, which contains taxi trip records of NYC in 2015, from 01/01/2015 to 03/01/2015, is used. (Yao, H.; Tang, X.; Wei, H.; Zheng, G.; Yu, Y.; and Li, Z. 2018a. Modeling spatial-temporal dynamics for traffic pre- diction. arXiv preprint arXiv:1803.01254.)[pdf]

• The first 40 days data is considered training purpose, and the remaining 20 days are tested.

• Preprocessed NYC datasets are available now [down]

[ Results ]

• We achieve the performance enhancement previous works on the NYC datasets.

Method	MAPE	RMSE
Historical AVG	23.18%	43.82
ARIMA	22.21%	36.53
LinUOTD	19.91%	28.48
XGBoost	19.35%	26.07
ConvLSTM	20.50%	28.13
ST-ResNet	21.13%	26.23
DMVST-Net	17.36%	25.71
STDN	16.30%	24.10
TGNet	15.62%	23.45

• We also applied the same model to Seoul City datasets.

Method	MAPE	RMSE
ARIMA	23.05%	12.53
XGBoost	19.68%	8.08
Baseline	18.87%	7.06
Baseline + Drop-off	18.62%	6.73
Baseline + Temporal Information	17.97%	6.68
TGNet	17.72	6.32

[ Contribution ]

• Our contribution is introduced temporal data directly to model output, then increase the performance.
• Another one is using drop-off data. In some locations, drop-off data may represent the future demand.
• We located those spot that held festival, concert and events irregularly.

- We select 4 Area which has stadium or complex hall
- That place held the event irregularly such as Concert, Festival, Convention
  - (1) Gocheok Skydome [37.498555, 126.867300]
  - (2) Jangchung Sports Complex [37.558160, 127.006782]
  - (3) Seoul worldcup stadium - (Sangam) [37.568130, 126.897210]
  - (4) Seoul olympic stadium - (Jamsil) [37.515686, 127.072793]

Full Event list [link]

[ Experiment ]

• UNet based network (CNN kernel size 3x3)
• using skip-connection
• Average pooling
• ADAM optimization
• 20% validation set out of training, early stopping applied when the val_loss is not changed