The Mobility Energy Productivity (MEP) Metric

Yi Hou, Venu Garikapati, Stan Young, Ambarish Nag, and Tom Grushka

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.

MOTIVATION

• What is mobility?

• How do you quantify mobility?

• No ‘open’ and practical method to quantify mobility

• Existing transportation performance metrics measure utilization or efficiency of

road network

• Can we increase energy use if we connect people better?

• Productivity = mobility benefits/costs

Mobility: The quality of a network or system to connect people to goods, 

services, and employment that define a high quality of life.

The Energy Efficient Mobility Systems (EEMS) Program will identify and 

support technologies and innovations that encourage a maximum-mobility, 

minimum-energy future.

A metric that is 

easy to scale 

spatially, as 

different contexts 

might require 

computation at 

different scales

DATA SPECTRUM DRIVING THE METRIC

Travel Time and Isochrone 

• Third-party isochrone application program interfaces (e.g., HERE)

• GPS trajectory data (TomTom and INRIX)

• Travel demand models

Land Use Data

• Metropolitan planning organizations

Energy Efficiency Measures

• Transportation Energy Data Book

• Other energy intensity studies

Travel Demand Data

• National Household Travel

Survey (NHTS)

Cost Measures

• Capital and operational costs

• Value of time

BASIC DATA ELEMENTS OF THE MEP METRIC

• Quantify the number of opportunities

that people can reach within a certain

travel-time threshold via different

transportation modes

• The opportunities measure is

weighted by the energy efficiency, 

affordability (cost), and time efficiency 

of different transportation modes

• Average the energy-weighted

mobility values across all activities by 

frequency of trip purpose

• The opportunities measure is further 

weighted by the travel cost of each

mode

ISOCHRONE GENERATION

TNC ISOCHRONES

• 10 minutes of driving versus 10 minute TNC ride

• TNC waiting time is 3 minutes

Austin Columbus Washington, D.C.

Mode Energy Intensity

(kwh/passenger-mile)

Capital and Operational Cost

(dollar/passenger-mile)

Driving 0.90 0.48

Transit 0.65 0.85

Bike 0 0

Walk 0 0

TNC 1.8 1.54

Paratransit 4.13 2.25

MODAL WEIGHTS FOR ENERGY 

AND OPERATING COSTS

References:

• Federal Transit Administration, “National Transit Summary and Trends,” 2015

• Oak Ridge National Laboratory, “Transportation Energy Data Book,” 2018

• American Automobile Association, “Your Driving Costs,” 2018

• ALG, “The Road to 2030,” 2016

GEOGRAPHIC AREA FOR ANALYSIS

• Metropolitan area of Columbus, Ohio

• Divided into 1,830 1 km by 1 km pixels

• Mobility is evaluated at the centroid of each cell block

• 1 km chosen for balance between homogeneity of data and complexity of 

calculations

• Actual scale can be traffic analysis zone (TAZ) à block group à address, driven by 

data availability and analysis needs

• The MEP metric computed for each pixel is aggregated to the city level using

population or employment density-weighted summation

MEP MAPS BY MODE – COLUMBUS ILLUSTRATIVE SCENARIO ANALYSIS

Caveats:

• In the scenario analyses presented above, assumptions were made to show firstorder-effects improvements in MEP scores due to technological advancements or 

transportation investments 

• Coupling the MEP calculations with agent-based travel microsimulation models

can fully capture such effects—not only primary impacts such as energy intensity,

but also impacts of adoption rates, induced (or reduced) congestion, and other

secondary and tertiary impacts that can arise from these scenarios

MEP FOR VARIOUS CITIES

Austin, Texas: 233

Washington, D.C.: 427

Denver, Colorado: 216

pgRouting

PostGIS

Driving

Transit, Biking, and Walking Combined

NEXT STEPS

• Incorporate mode-activity realism into the computation of the metric (to address 

unrealistic scenarios like walking 40 minutes to access a grocery store)

• Obtain relevant data from publicly available sources such as the National Household

Travel Survey

• Integrate with regional travel demand models to assess alternative future scenarios

• Calculate MEP for the 50 largest metropolitan areas in the United States

PROPERTIES OF THE MEP METRIC

• Reflects efficiency of accessing a variety of goods, services, and employment

• Can be applied to any mode (car, walk, bike, transportation network company [TNC], 

etc.) and across modes

• Determined by 

- Travel time and travel time reliability to destinations

- Energy and monetary cost of travel

• Spatially scalable (applied to a home, district, city, or employer)

• Can compare

- Two locations (Chicago vs. Topeka, Kansas)

- Two planning strategies (roadway extension vs. transit expansion)

- Two technologies (electric vehicle penetration vs. automated vehicle penetration)

• Can be disaggregated by geography, mode, trip type, and population sub-group

MEP MAPS BY ACTIVITY – COLUMBUS

Jobs

Shopping/Errands

Transportation Research Board Annual Meeting 

(TRB Paper 19-03650)

January 13–17, 2019

NREL/PO-5400-72946

WEIGHTING MECHANISM FOR THE METRIC

MEP METRIC

The MEP metric is calculated by weighting the cumulative opportunities using a 

negative exponential function applied on the modal weighting factor.

Baseline

Population-Density-Weighted 

Metric: 162

Increase in Vehicle 

Efficiency

If MPG of cars is 

increased by 200% 

(MPG of cars increased 

from 25 in the baseline 

to 75 in the scenario)

Population-Density-Weighted 

Metric: 204 

(26% increase from baseline)

Vehicle Automation 

Travel-time deterrence 

decreased by 70% (10 

minutes in baseline 

equals 3 minutes in 

scenario analysis)

Population-Density-Weighted 

Metric: 246 

(52% increase from baseline)

Addition of 

TNC Mode

Population-Density-Weighted 

Metric with TNC: 198 

(22% increase)