International Conference on 

Transportation and Development

Pittsburgh, Pennsylvania

July 15-18, 2018

NREL/PO-5400-71872

AUTOMATED MOBILITY DISTRICTS (AMDs)

This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. 

Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and 

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

Quantifying the Mobility and Energy Benefits of Automated

Mobility Districts Using Microscopic Traffic Simulation

Lei Zhu, Venu Garikapati, Yi Hou, and Stanley Young, National Renewable Energy Laboratory 

Yuche Chen, Vanderbilt University; H. M. Abdul Aziz, Oak Ridge National Laboratory

• District-scale implementation of advanced vehicle (AV) technologies

• Full benefits of an AV-shared-mobility service (SAE Level 5) within a 

confined geographic region or district 

• Everything somewhere vs. something everywhere

• AMD models serve as “special generators” in existing urban travel 

demand models (TDMs)—similar to how airports, university campuses, 

and central business districts (CBDs) are currently treated in TDMs 

• AMD modeling and simulation toolkit informed by real-world 

deployments to quantify the mobility and energy impacts of an AMD. 

Figure 1. Conceptual depiction of AMDs

MODEL DESCRIPTION 

• AMD toolkit

• Builds on the Simulation of Urban Mobility (SUMO)―a leading opensource microscopic traffic simulation suite

• Integrates the Future Automotive Systems Technology Simulator 

(FASTSim), a vehicle/powertrain simulation tool maintained by NREL. 

Travel 

Demand

• Origin-destination

(O-D) data from 

TDM

• Local surveys or 

counts

• Induced travel 

demand.

SUMO 

(Mobility 

Analysis)

• Carries out network 

simulation of vehicles

• Outputs detailed travel 

trajectories.

FASTSim

(Energy 

Analysis)

• Outputs vehicle 

energy consumption.

A Hypothetical AMD Scenario

Figure 2. AMD simulation toolkit: model flow

Figure 3. A hypothetical AMD network in SUMO 

Green lines  Car mode

Yellow lines  Pedestrian mode

Red lines  AES mode

• Four AES vehicles operate “ondemand” inside the circuit.

• The simulation is carried out for a 

demand of 300 trips distributed 

across the 13 (O-D) pairs. 

• Traffic demand is distributed as a 

bimodal distribution reflecting 

morning and afternoon peak hours 

during a typical day. 

• The choice set of travel modes 

encompasses: 1) passenger car, 2) 

AES, and 3) walking. 

Scenario

(Car: Walk: AES)

VMT

(miles)

VATT

(seconds)

VATD

(miles)

FC (gal) [gasoline/

electric shuttle case]

Baseline (70:30:0) 128.8 86.5 0.6 5.9

Transitional (60:20:20) 153.8 124.3 0.8 7.0/5.3

Optimistic (50:10:40) 175.7 168.5 1.1 8.0/4.5

Vehicle miles traveled (VMT); vehicle average travel time (VATT); vehicle average travel 

distance (VATD); fuel consumption (FC) in gallons of gasoline

RESULTS AND DISCUSSION

• Mobility impact: compared to the baseline scenario, the transitional and 

optimistic scenarios exhibit an increase in VMT, VATT, and VATD. 

• Energy/fuel impact:

• For the gasoline automated shuttle case, total gasoline consumption 

increases along with total VMT. 

• For the AES case, the transitional and optimistic scenarios see fuel 

consumption decrease by 10% and 26%, respectively. 

• An AMD is a district-scale implementation of connected and 

automated vehicle technologies to realize the full benefits of an ondemand shared automated mobility service within a confined 

geographic region.

• A hypothetical AMD along with real-world AMD implementations, 

e.g., Greenville AMD and the Kansas State University campus, are 

introduced.

• This study develops an AMD modeling and simulation toolkit and 

reports on the preliminary analysis results for hypothetical AMD 

deployment, exercising the toolkit with three scenarios.

• Future research will focus on enhancing the toolkit to integrate 

different mobility operations of AMDs and define and quantify 

various performance metrics for AMDs. 

• The model will also be extended to account for parking-related

issues (availability and access times). 

CASE STUDIES Greenville AMDs

• On-demand fixed route automated shuttles (a-taxi shuttles) are 

operating inside Greenville field test region (AMD)

• Greenville AMD scenario toolkit is informed by the real-world 

traffic demand and network data (Figure 4(a)) from Greenville 

County

• A modeling and simulation scenario for the proposed Greenville 

AMD is developed for mobility and energy assessment

• Multi-modal traffic flow: 1) passenger car, 2) walking, 3) ondemand fixed-route shuttles, 4) on-demand door-to-door ridesharing

• The Greenville AMD analysis is currently underway.

Figure 5. Greenville AMD automated taxi (a-taxi) shuttle 

service test region: (a) detailed road network and traffic 

analysis zone and (b) satellite image (zoomed in)

(a) 

(b) 

(b) 

Kansas State University Campus (Manhattan, KS) 

• Kansas State University campus considered using a personal rapid transit (PRT) 

system to provide last mile mobility on campus 

• Area: 664 acres; population: ~25,000. 

• Analyzed mobility metrics (personal miles traveled [PMT]/personal hours 

traveled [PHT]) with and without PRT system

• Mobility analysis

• 10 AMD miles displaced ~ 4.5 ped & 4.5 vehicle miles

• 4 AMD hours displaced ~3 ped & 1 vehicle hour

• Total system VMT increased 2%–3%

• Total system PHT decreased ~1% (people will travel further to go faster)

• Total system energy (even with conservative assumptions) decreased.

Figure 4. Kansas State University campus: (a) PRT system and (b) test 

region in satellite image

(a) (b) 

This poster and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the 

Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient 

Mobility Systems (EEMS) Program. The authors acknowledge Stan Young of NREL for leading the Urban Science Pillar of the SMART Mobility 

Laboratory Consortium. The authors would particularly like to thank David Anderson with DOE’s Office of Energy Efficiency and Renewable 

Energy for helping to establish the project concept and advance its implementation, and for providing ongoing guidance and support.