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Modeling and Simulation of 

Automated Mobility Districts

Venu Garikapati, NREL

DOE Vehicle Technologies Office

2018 Annual Merit Review and Peer Evaluation Meeting

June 20, 2018

Project ID # eems009 
CONSORTIUM

SMART MOBILITY LAB

Connected & 

Automated 

Vehicles

Mobility Decision 

Science

Multi-Modal 

Transport

Urban Science

Advanced 

Fueling Infrastructure

ENERGY EFFICIENT MOBILITY SYSTEMS PROGRAM 

INVESTIGATES 

MOBILITY ENERGY 

PRODUCTIVITY 

THROUGH FIVE EEMS 

ACTIVITY AREAS

Advanced R&D 

Projects

Smart Mobility 

Lab Consortium

HPC4Mobility &

Big Transportation Data Analytics

Core Evaluation & 

Simulation Tools

Living Labs
Click to edit Master title style

CONSORTIUM

SMART MOBILITY LAB

7 labs, 30+ projects, 65 researchers, 

$34M* over 3 years.

Connected & 

Automated 

Vehicles

Mobility Decision 

Science

Multi-Modal 

Transport

Urban Science

Advanced 

Fueling 

Infrastructure

*Based on anticipated funding
OVERVIEW

4

• Project start date: 10/1/2016 

• Project end date: 9/30/2019

• Percent complete: 60% (FY18)

• Computational models for CAVs, design, and 

simulation methodologies for AMDs

• Lack of real-world data to support AMD 

modeling efforts

Timeline Barriers

• SMART Mobility Laboratory Consortium:

o National Renewable Energy Lab (NREL)

o Oak Ridge National Laboratory (ORNL)

o Idaho National Laboratory (INL)

• Greenville County [Primary Collaborator], 

South Carolina

• Jacksonville Transit Authority

• Vanderbilt University

Partners

• Total project funding

– DOE share: $930K

– Contractor share: $0

• Funding for FY 2017: $290K

• Funding for FY 2018: $320K

Budget
WHAT IS AN AMD?

5

An automated mobility district (AMD) is a campus-sized implementation of 

connected/automated vehicle (CAV) technology to realize the full benefits of a fully 

electric automated mobility service within a confined region or district.
RELEVANCE

6

• Project Objectives

• Quantify the net mobility gains and energy 

impacts of automated, connected, electric 

and/or shared (ACES) vehicles deployed in 

dense urban districts

• Develop modeling capabilities for VTO to 

estimate the energy and environmental effects 

of AMDs

• Integrate AMD model into existing regional 

travel models to simulate AMDs as a “special 

generator” in the region to quantify energy and 

mobility impacts.

Intra-District 

Impacts

•Mobility and 

energy of AMD 

fleet

•Land use 

changes.

Inter-Regional 

Impacts

•Modal choice

•Route choice

•Activity choice.

Boundary 

Issues/ Effects

•Mode transfer/ 

parking

•Boundary 

services

•TNCs, car 

sharing/rental.

The Energy Efficient Mobility Systems 

(EEMS) Program envisions an affordable, efficient, 

safe, and accessible transportation future in which 

mobility is decoupled from energy consumption. The 

program will conduct early-stage R&D at the vehicle, 

traveler, and system levels.

Source: https://www.energy.gov/eere/articles/energy-andtransportation-departments-commit-supporting-cities-future
MILESTONES

7

Month/Year Description of Milestone or Go/No-Go Decision Status

March 2017

White paper, “Initial Assessment and Modeling 

Framework Development for Automated Mobility 

Districts,” presented at 2017 ITS World Congress

Complete

September 2017

Identify early adopter stakeholders:

• Non-disclosure agreement (NDA) with Robotic 

Research 

• Memorandum of understanding (MOU)/Partnership 

with Greenville, SC

Complete

Final Stages of 

Approval

February 2018

Conference paper, “Quantifying the Mobility and Energy 

Benefits of Automated Mobility Districts Using 

Microscopic Traffic Simulation,” selected for presentation 

at the ASCE-ICTD conference to be held in Pittsburgh, PA

Complete

September 2018 Exercise the AMD modeling toolkit for a real-world 

deployment On Schedule
APPROACH

8

Travel Demand

• Origin-Destination data 

from regional travel 

demand model

• Local surveys or counts

• Induced travel demand.

SUMO 

(Mobility Analysis)

• SUMO ― Simulator of 

Urban Mobility

• Carries out the network 

simulation of vehicles

• SUMO will output travel 

trajectories.

FASTSim

(Energy Analysis)

• FASTSim ― Future 

Automotive Systems 

Technology Simulator

• FASTSim will output 

vehicle energy 

consumption.

AMD Simulation Toolkit: Model Flow
APPROACH

9

2016

•Build on existing AMD 

analysis

•NREL authored 

Institute of Electrical 

and Electronics 

Engineers (IEEE) 

Conference paper 

(Chen et al., 2015), 

an analysis of a 

proposed automated 

mobility system on a 

university campus. 

2017

•Develop and 

implement a modeling 

framework 

•Assess 

mobility/energy 

impacts of AMDs

•Model development 

in SUMO

2018

•Exercise the model 

with partners either 

implementing AMDs, or 

seriously considering 

them.

2018/19

•Integrate into a 

regional travel demand 

model (Austin  Task 

2.1.3)

•Produce case studies 

replicable/transferable 

to other proposed 

sights.

Location

Collaboration

Potential AMD 

Deployments

Military 

Base

Residential 

Community

University 

Campus Neighborhood

Greenville (SC)

Miramar Base (CA)

Houston University (TX)

Jacksonville (FL)

District-scale implementations of automated mobility systems are getting off the 

ground in multiple deployment locations in the United States. 
APPROACH

10

Source: Presentation from Greenville 

County

 Bare bones 

simulation setup

 Hypothetical 

demand and 

network data

 Dedicated 

guideways and fixed 

schedules for 

automated electric 

shuttles (AES).

Version 1

 Real network and 

fixed demand

 Location of 

application: 

Greenville

 On-demand 

services for AES

 AES simulation in 

conditions.

Version 2

 Real network and 

dynamic (induced) 

demand.

Version 3
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

11

FY 2017 (Previous Accomplishments)

•Conducted extensive literature and practice search on mobility and energy impact analysis of 

automated transit systems 

•Applied framework to existing Personal Rapid Transit (PRT) / Automated Transit Network (ATN) 

study in Manhattan, Kansas

•Developed initial framework for assessing mobility/energy impacts of AMDs

•Explored tools available for modeling AMDs and select tools to be used

•Hosted an AMD workshop at the Princeton Smart Driving Car Summit.

FY 2018

•Coded a hypothetical network in the chosen network model in SUMO

•Coded an on-demand automated electric shuttle mode in SUMO

•Ran a few preliminary scenarios with assumed demand and modal distribution on the hypothetical 

network

•MOU underway with Greenville, SC. Greenville won a $4M U.S. Department of Transportation (DOT) 

grant to deploy automated taxi (A-taxi) shuttle systems in three neighborhoods

•Received travel demand and traffic network data from Greenville, currently being coded into SUMO

•Hosted two AMD sessions at the American Society of Civil Engineers (ASCE) ― Automated People 

Movers Conference.
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

12

Car

Walk

AES

Sample of hypothetical AMDs using mixed modes (car, walking, 

automated electric shuttles) coded in SUMO to simulate intra-district 

mobility and energy impacts

Preliminary AMD Simulation
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

13

Preliminary AMD Simulation
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

14

Network • A hypothetical trapezoidal network is generated in SUMO. 

Travel Demand • For the preliminary analysis, hypothetical traffic demand is 

generated and distributed across the 13 origin-destination (OD) pairs in the network.

Mode Share • This initial study intends to understand the mobility and 

energy impacts of an AMD, so the mode shares are 

“assumed” for various scenarios. 

AES Fleet and 

Operation

• A total of four AES serve the designated demand in the AMD. 

• AES pick up only one passenger at a time. They stop after 

dropping a passenger and wait for the next trip request.

Vehicles • Cars  2016 Toyota Camry

• AES  2016 Nissan Leaf 

Preliminary AMD Simulation ― Assumptions
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

15

Results

Scenario

VMT

(miles)

VATT

(seconds)

VATD

(miles)

FC (gal) [gasoline/

electric shuttle case]

Baseline 128.8 86.5 0.6 5.9

Transitional 153.8 124.3 0.8 7.0/5.3

Optimistic 175.7 168.5 1.1 8.0/4.5

• Vehicle Miles Traveled (VMT) ― the sum of all private vehicle and automated shuttle mileage for the scenario

• Vehicle Average Travel Time (VATT) ― the average time of travel in vehicle (does not include walking), averaged 

across private vehicles and automated shuttle trips

• Vehicle Average Travel Distance (VATD) ― the average travel distance (excluding any pedestrian links), averaged 

across private vehicle and automated shuttle trips

• Fuel Consumption (FC) ― in gallons of gasoline across the entire system

Scenarios Car mode Walk mode Automated Shuttle mode

Baseline 70% 30% 0%

Transitional 60% 20% 20%

Optimistic 50% 10% 40%

Scenario Development
TECHNICAL ACCOMPLISHMENTS AND PROGRESS

16

Greenville AMDs: Phase 0 and Phase 1

• Phase 0: Two electric automated vehicles (EAVs) will be repurposed as A-Taxis in Greenville at CU-ICAR, 

connecting with nearby academic and business collaborators [Already Underway]

• Phase 1: A-Taxi service will expand across I-85 (4-6 A-Taxis) to serve the nearby Verdae development with its 

housing, office, retail, and commercial centers [3–6 months following the ATCMTD Grant]
RESPONSES TO PREVIOUS YEAR REVIEWERS’ COMMENTS

17

1. The reviewer commented that two DOE laboratories and two universities are project partners, 

but no city partner that is planning to implement AMDs has been identified. [Two other reviewers 

emphasized the need for an AMD deployment partner].

Response: The project team has partnered with Greenville, SC which is currently testing A-Taxi 

shuttle systems in three locations in Greenville. Greenville has already provided the project team 

with travel demand and network information. Data from a-taxi operations will be collected in this 

fiscal year. The team continues communications with other leading deployments/demos.

2. The reviewer stated the project may need many bounds or a scenario-based approach to 

understand the uncertainty of AMD energy impact, similar to how general Automated Vehicle (AV) 

energy impacts have been bounded, but hopefully with much less uncertainty. 

Response: The authors are indeed considering a scenario-based approach for testing AV energy 

impacts. Some preliminary work has been published and included as a part of this presentation.

3. The reviewer said that military base collaboration is promising, and stated that there is a 

program underway with Major Brandon Newell that should be considered. 

Response: The authors are in active discussions with Major Brandon Newell to seek data from 

AES deployment in Miramar Military base for AMD model tool kit development. 
COLLABORATION AND COORDINATION

18

Within VTO

• SMART Mobility Consortium Laboratories: NREL, ORNL, and INL

• SMART Mobility Pillars: Advanced Fueling Infrastructure, CAVs, Mobility 

and Decision Science

Outside VTO

Collaborators Type Extent

Greenville County/City AMD deployment partner, providing 

travel demand and network supply data

Robotic Research Industry Automated shuttle operations data from 

Greenville deployment

Texas Southern University Potential AMD deployment partner

Miramar Military Base Government Potential AMD deployment partner

Vanderbilt University Energy consumption modeling (Sub)
REMAINING CHALLENGES AND BARRIERS

19

• No existing modeling and simulation tools exist for AMD impact 

analysis

• Requires multi-modal simulation (road, pedestrian, transit, parking, 

and AMD) 

• Most tools focus on single mode, simplifying others — though some 

previous art in automated people movers and airport modeling may 

apply

• Virtually none of the studies that focus on shared automated 

mobility is based on data from actual field implementation of 

automated vehicles

• Connecting the “special generator” AMD toolkit into a region’s travel 

demand model is going to be a key challenge, once the AMD toolkit 

is fully developed

• Collaborating with Task 2.1.3 [SMART Mobility Modeling for Typical 

Mid-Size City] in the Urban Science pillar to address this.
PROPOSED FUTURE RESEARCH

20

• FY18 – Remaining • Complete the intra-district micro-simulation (SUMO) component of the AMD modeling 

toolkit

• Exercise the AMD toolkit based for Greenville, SC

• FY19

• Integrate the AMD toolkit into a regional travel demand model

• Model inter-district and boundary impacts of AMDs in conjunction with the regional 

travel demand model

• Produce case studies transferable to other sites

“Any proposed future work is subject to change based on funding levels.”
SUMMARY

21

• AMD research, under SMART Mobility Urban Science, anticipates early 

deployment of fully automated vehicles in geographically constrained 

areas as a public mobility service ― increasingly supported by early 

deployment announcements 

• Objective is to develop modeling capabilities for VTO to estimate energy, 

emission, and mobility impacts of AMDs 

• FY18 efforts to date include preliminary modeling and simulation efforts 

using hypothetical network and demand data

• Preliminary results published in a conference paper

• Remaining efforts in FY18 will be focused on further refining the AMD 

modeling and simulation process using data from a real-world deployment 

(Greenville)

• FY19 efforts will focus on:

• Integrating the model toolkit into a regional travel demand model

• Produce case studies transferable to other sites.
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency 

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency 

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

NREL/PR-5400-71347

Thank You