Impacts of Travel Demand Information Diffusion on 

Reducing Empty Vehicle Miles Traveled by 

Ridesourcing Vehicles

Ria Kontou, Ph.D.

UNC Chapel Hill

Joint work with Venu Garikapati (NREL),

Yi Hou (NREL), and Chao Wang (UC Riverside) 

Shared Mobility, Ridehailing, and Emerging Transportation Trends - Session 1453

2019 Transportation Research Board Annual Meeting NREL/PR-5400-73061
• Transportation Network Companies (TNCs) are rapidly gaining market 

share. 

• Available in several cities in North America and are prevalent 

transportation mode alternatives in large metropolitan areas.

In 2017, Uber had 375.5 million rides in North America (1)

• TNCs are redefining the way people travel, but are also causing new 

transportation and energy problems that require immediate attention.

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Background
TNC Are Experiencing Exponential Growth 

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No. of Uber drivers making at least 1 trip/month

Source: Uber
TNCs are Increasing Mobility – But at What Cost? 

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TNC growth has added 976 million miles of driving to city 

streets, citywide, since 2013.

-Schaller Consulting Report
Research Motivation: Reducing Empty TNC Mileage

TNC services → empty vehicle miles

Example Cases: San Francisco (SFCTA, 2018) – INRIX data

47% of the increase in vehicle miles travelled from 2010 to 2016

Denver (Henao and Marshall, 2018) – Collected by driver data

41% empty miles share for a single Uber/Lyft driver in Denver 

Austin (Komanduri et al., 2018) – RideAustin TX data

37% empty miles of total vehicle miles traveled (VMT)

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Empty VMT
Empty VMT Scenarios

• Once the passenger is picked up and dropped off at their destination, the TNC 

driver (assuming they are still in service) can do one of four things:

• Park in a close-by location and wait for the next trip request

• Accept another request and travel to pick up the next passenger

• Cruise around until they find another trip to serve

• Travel to a known demand pocket such as the airport or the central 

business district (or a suggested high-demand location) while waiting to 

be assigned to a customer

Department Name 6
Future Travel Demand Information: Impact on Empty VMT

• What if ride-hailing drivers received information on future demand?

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– Hypothesis: 

• Reduction of cruising without passengers & empty mileage

• Energy & environmental savings

– Assumption: 

• Information on high future demand within next ββ minutes incentivizes drivers 

to wait in place for next ride

– Method: Machine learning applications to forecast demand

– Constraints: Cap drivers waiting time ββ to 5−20 minutes between rides

– Ride assignment: Next closest ride within ZIP code of recent rider drop-off
Travel info diffusion effects on vehicle empty mileage case studies

Trip-level analysis using 1 week data from:

• RideAustin (Austin, TX, USA)

o 28,586 trips, 16,930 drivers

• DiDi Chuxing (Chengdu, Sichuan, China)

o 1,048,575 trips, 216,927 drivers

Application – Data Sources

8

Austin

Descriptive Chengdu 

Statistics 

Trip 

Distance 

(mi) 

Estimated 

Deadheading 

Distance (mi) 

RideAustin 

Sample

Mean 4.27 3.91

Median 2.81 2.31

Std. Dev. 4.12 3.12 

DiDi 

Chuxing 

Sample

Mean 1.96 1.52

Median 1.72 0.84

Std. Dev. 1.29 1.66

Shared Mobility, Ridehailing, and Emerging Transportation Trends - Session 1453

2019 Transportation Research Board Annual Meeting

Brief Data 

Overview
TNC Demand Forecasting

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Ride-hailing trip demand prediction results for Austin TX, 

RideAustin 1 week data – Details in Hou et al. (2019)

Long Short-Term Memory (LSTM) network for demand prediction

Recurrent neural network architecture learning time series data with long time 

spans and high dimensions

• 1-hour ahead prediction for RideAustin, 10-minute ahead prediction for DiDi

• Performance check based on RMSE and MAE

Austin Chengdu 

Ride-hailing trip demand prediction results for Chengdu, 

DiDi 1 week data – Details in Chao and Hou (2019)
Drivers Waiting in Place – Heuristic Algorithm

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Which drivers are more likely to wait in place after receiving future 

travel demand info? 

Probability that a driver waits for a trip jj to 

be generated at zzddii of their last trip 

destination ii is binomially distributed with 

maximum probability of success equal to a 

threshold aa

XXiiii = aa �

PPzzddii

max PPTT

• rriiii random ∈ [0,1]

• Driver of a trip where XXiiii > rriiii waits in place for the next rider pickup

• XXiiii ≤ rriiii trip ii ineligible for the following trip-matching process

PPTTzzddii : predicted trips at zone (from ML application)

max PPTT : the max. number of trips predicted the following hour tt
Assignment - Heuristic

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Determine next trip origin to assign driver who is waiting in place

Conditions

• Next trip candidates adhere to 

temporal and spatial constraints

• Candidate origin should be within 

the same ZIP code or grid cell as 

the previous trip’s destination

• Driver is willing to wait in place for 

the next trip to arrive for less than 

a specific time threshold ββ

• Trip’s origin kk meets constraints 

and minimizes deadheading 

distance, then it occurs next

Note: 

Drop-off and pick-up times are not flexible
Empty Mileage Reductions & Energy/Cost Savings

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Application outputs: Average Trip-Level Savings RideAustin DiDi 

VMT: 55% – 59% empty 

VMT reduction/trip

Energy: between 0.035 –

0.05 gallon per trip 

Cost: drivers save 11 – 13 

US cents/trip 

VMT: 67.7% – 78% empty 

VMT reduction/trip

Energy: drivers save 0.08 –

0.10 gallons/trip 

Cost: cost savings 23 – 26 

cents/trip
Sensitivity Results – Waiting Time Parameter Impact

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Application outputs: Distribution of Trip-Level Savings

• Waiting time ↑ then 

deadheading VMT ↑

• Distribution uncovers avg. 

trip-level deadheading 

savings vary -0.1 – 6 mi

• Distribution uncovers 

avg. trip-level 

deadheading savings 

vary -0.08 – 2.7 mi
Sensitivity Results – Max Drivers Waiting Parameter Impact

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RideAustin DiDi

Scenario: Driver is willing to wait up to a threshold of ββ equal to 20 and 10 minutes 

for the RideAustin and DiDi data, respectively

Application: Varying threshold aa, where aa denotes the maximum percent of drivers 

that will be waiting based on the information received

Results: 20% and 25% threshold results in max savings for the regions examined 

respectively, but heavily dependent on data 
Conclusions & Future Research

• Travel demand information diffusion can 

help:

– Curb empty vehicle miles [up to 78% per trip]

– Reduce drivers operational costs, wear & tear, 

energy consumption 

– May require provisions for designated curb space 

for ride-hailing vehicles

• Extend application of the algorithm to 

different datasets

• Explore variability in driver behavior and 

impact on passenger wait time

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Thank you! 

Questions?

ekontou@email.unc.edu