A Deep-Learning Approach for Transportation 

Network Companies Trip-Demand Prediction 

Considering Spatial-Temporal Features

Yi Hou, Venu Garikapati, Joshua Sperling, Alejandro Henao, and Stan Young

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy 

Vehicle Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, 

irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

STUDY QUESTIONS

Unique questions explored in this study:

• What are the most relevant variables (e.g., time of day, weather, and zip code)

affecting transportation network company (TNC) trip demand?

• How can TNC trip demand be predicted in small and mid-sized cities?

Transportation Research Board Annual Meeting 

(TRB Paper 19-03468)

January 13–17, 2019

NREL/PO-5400-72955

RIDEAUSTIN DATASET

By the numbers:

• Sample duration—6 months

• Period—7/24/2016 to 2/6/2017

• Total trips—820,816

• Average trip duration—12 minutes

• Average trip distance—8 kilometers

• Average hourly pickups—173/hour

DAILY TRIP DEMAND

Daily Trip Demand Average Trip Distance

TRIP DEMAND BETWEEN ORIGINS 

AND DESTINATIONS 

DEMAND VARIATION BY HOUR

DEMAND VARIATION BY DAY OF WEEK

DEMAND VARIATION WITH PRECIPITATION

DEMAND VARIATION WITH TEMPERATURE

TRIP-DEMAND FORECASTING

Objective:

• Predict the future pick-up counts in the next hour for seven zip code zones in Austin,

Texas

Model: 

• Deep learning—long short-term memory

• Model input

- Past six hours of pick-up counts in each zip code zone

- Day of week, hour of day, weather, holiday or not

• Model training—data from 7/24/2016 to 1/6/2017

• Model testing—data from 1/7/2017 to 2/6/2017

ZIP CODE ZONES FOR TNC PREDICTION

LONG SHORT-TERM MEMORY

A special recurrent-neural-network architecture designed to learn time-series data with 

long time spans and high dimensions

LONG SHORT-TERM MEMORY

PREDICTION VERSUS OBSERVATION

NEXT STEPS

• Apply the methodology to more zones with higher spatial granularity

• Compare with more advanced baseline models

• Build prediction models for special trip generators (e.g., airports)

Long short-term memory:

• Mean absolute error—7.2

• Root mean square error—12.3

Instantaneous trip demand: 

• Mean absolute error—9.5

• Root mean square error—22.5

Historical average:

• Mean absolute error—11.5

• Root mean square error—25.0

Trip Demand by Day of Week

Demand Variation with Precipitation

Hourly Average Pickups 

Hourly Average Pickups

Demand Variation with Temperature

Temperature

Precipitation

Total Pickups

Day of Week

Average Pickups

Hour

Trip Demand by Hour

Downtown, Zip Code: 78701 Zip Code: 78702

Zip Code: 78703 Zip Code: 78704

Zip Code: 78705 Airport, Zip Code: 78719

Zip Code: 78741

Downtown

78702

Airport

78741

78704

78703 78705

Austin