This presentation was shared at TRB's 2nd Annual Workshop on Road Vehicle Automation at Stanford, by Dr. Kara Kockelman and Dan Fagnant.

1. Environmental Implications for Shared Autonomous Vehicles
Using Agent-Based Model Simulation
Dan Fagnant & Dr. Kara Kockelman
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Introduction
Shared
Autonomous
Vehicle Fleet...
Google’s Autonomous Prius Car2Go’s Shared Smart Car
 Less than 20% of newer (& 15% of all) personal vehicles are in-use at
peak times of day, even with 5-minute pickup & drop-off buffers.
 Car-sharing programs like ZipCar & Car2go have expanded quickly, with
the number of U.S. users doubling every year or two over the past
decade.
 Shared Autonomous Vehicles (SAVs) can help overcome car-sharing
barriers, like return-trip certainty & vehicle access distances.
An Agent-Based Model Framework
 Urban area is gridded, with 900 to 2500 0.25 x 0.25 sq. mile zones.
 Trip generation:
Poisson distribution for trip productions per cell.
Higher trip production & attraction rates closer to city center.
Round-trip travel, with most (78%) travelers returning via SAV.
Randomized departure times & trip distances (2009 NHTS).
 SAVs travel at fixed speeds, with 5-minute intervals.
AM & PM peak congestion, with speeds slowing by 12 mph.
Scenario Parameters
Example Trip Generation
Relocation Strategies
1) Large Blocks (R1): Area divided into 25 blocks. In blocks with excess
SAVs, the available (unoccupied) SAVs sent to adjacent blocks, while
blocks with few SAVs pull from adjacent blocks, after trips served.
2) Small Blocks (R2): Same idea as R1, but with 100 smaller blocks.
3) Filling in White Spaces (R3): Available SAVs travel to nearby zones,
where no SAV will be within one zone at the start of the next period.
4) Shifting Stacks (R4): SAVs shifted to adjacent zones when the
difference in available SAVs in the next period is 3 or more.
Case Study Evaluations
 Initialization: Sets base model’s parameters.
 Trip Generations: Generates trips with origins,
destinations, & departure times.
 Warm Start: Runs model to estimate the required
numbers & starting locations of SAVs, across zones.
 Full Model Run: Simulates SAV travel for one or
more 24-hour days.
 Report Results: Outputs summary measures.
Model Operation
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Trip Dist. Distribution (mi.)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Midnight-3AM
3AM-6AM
6AM-9AM
9AM-Noon
Noon-3PM
3PM-6PM
6PM-9PM
9PM-Midnight
Dwell Time Distribution (hrs.)
1 0 0 1 0 0 1 1
0 0 0 0 0 1 1 0
0 0 0 4 0 1 0 0
0 0 2 2 0 1 0 1
0 1 0 1 0 0 3 0
1 1 1 0 2 2 1 0
2 3 1 1 0 0 0 0
0 1 1 0 0 2 0 0
2 2 1 1 1 4 0 4
3 2 4 1 1 1 1 2
2 2 1 1 2 1 3 2
1 1 1 2 0 1 2 3
2 3 0 1 1 1 1 1
0 4 1 0 1 1 3 1
2 4 1 1 1 0 1 1
2 5 1 0 2 3 0 1
Reallocation
-4 -4 -9 3 5 -4 0 -6 3 5
-1 8 5 9 -1 0 1 4 5 3
2 4 -3 -3 12 2 4 -3 2 3
3 0 -6 0 -3 3 -2 -4 0 1
2 0 -8 -6 -5 2 -3 -5 -6 -5
Check block balances
Initial SAV locations
Strategy R1
Environmental Implications
Acknowledgements
We wish to thank the TRB Vehicle Automation Committee for selecting this paper for presentation, the
Southwest Research Institute & ITS America for selecting this paper as the winner of the ITS America Student
Paper Competition, & the Southwest University Transportation Center for funding support. We would also like
to thank Annette Perrone for administrative support, Steve Dellenback of SWRI, & other dissertation committee
members for insights & support.
Strategy R3
Strategy R4
Parameter Value
Service area 10 mi. x 10 mi.
Outer trip generation rate 10 trips / zone / day
Inner trip generation rate 40 trips / zone / day
Off-peak speed 33 mph
Peak speed 21 mph
AM peak 7 AM - 8 AM
PM peak 4 PM - 6:30 PM
Trip share returning by SAV 78%
Measure Mean S.D.
Trips 65,530 360
SAVs 1,908 37.8
Trips per SAV 34.34 0.72
5-minute wait periods 241 175
Avg. wait time per trip 0.26 0.03
Un-served trips 0 0
% waiting 5 min + 0.40% 0.27%
Total VMT 358,100 2,500
Unoccupied VMT 33,030 410
Avg. trip distance 5.39 0.01
Unoccupied mi. per trip 0.5 0.01
% induced travel 10.2% 0.1%
% max in use 98.1% 1.2%
% max occupied 94.7% 2.7%
Hot starts per trip 0.75 0.02
Cold starts per trip 0.059 0.003
Key Results
 100 days were simulated to
assess SAV travel implications.
 Each SAV can replace 10 to 13
conventional vehicles.
 Avg. wait times  15 sec.
 Fewer than 1 in 200 travelers
waits more than 5 min.
 10.2% new induced travel
generated by unoccupied
vehicles traveling to a new
rider, or relocating to a better
spot.
 During the heaviest time of
day, almost 95% of SAVs were
occupied, & 67% of the others
were relocating to better
spots.
 Just 0.81 starts per trip, 7.2%
of which are cold starts, vs.
0.94 starts per trip, 68% of
which are cold starts for
conventional vehicles.
Model Variations & Conclusions
Environmental
Impact
Sedan Life-Cycle Inventories Average U.S. Fleet vs.
(Pickup Trucks & SUVs not shown) SAV Emissions Inventories
Operating
(Running)
Manufacture Parking Starts
US Vehicle
Fleet
SAVs Difference % Change
Energy (GJ) 890 100 15 0 1230 1082 -148 -12%
GHG (m. tons) 69 8.5 1.2 0 90.1 84.6 -5.4 -6.0%
SO2 (kg) 3.9 20 3.6 0 30.6 24.6 -6 -19%
CO (kg) 2100 110 5.2 1400 3833 2573 -1260 -33%
NOx (kg) 160 20 6.4 32 243 200 -43 -18%
VOC (kg) 59 21 5.2 66 180 93 -87 -48%
PM10 (kg) 20 5.7 2.7 0 28.2 28.0 -0.2 -0.80%
 High trip density & low congestion
are greatest keys to success.
 Global-view relocation strategies are
most effective, with R1 reducing long
delays more than R2-R4 combined.
 Good level of service possible with
up to 39 trips per SAV (1700 SAVs).
 Shared SAVs result in net positive
energy & environmental impacts, though induced travel may be a
concern for small programs.
 Future work will apply this framework to an actual urban area, for
trip generation & attraction, as well as incorporating dynamic
ridesharing capabilities.
 Benefits from vehicle fleet change, fewer starts & less parking.
 But they bring new VMT emissions.
 Reductions for energy use & all pollutants, particularly CO & VOCs.
 25 additional scenarios tested parameter variations, relocation
strategies & smaller SAV fleets.
Scenario Description
SAVs per
trip
5-minute wait
intervals
Avg. wait
time
% induced
travel
Cold starts
per trip
Base case scenario 34.3 241 0.26 10.20% 0.059
Twice as many trips 35.9 203 0.14 6.60% 0.059
Half as many trips 32.7 303 0.49 11.80% 0.063
A quarter as many trips 30.4 309 0.81 13.10% 0.075
Greater peak congestion* 30.6 2,145 0.32 9.60% 0.071
Less peak congestion* 37.9 136 0.24 10.20% 0.051
Lower SAV Fleet Delays
Selected Scenarios & Results
* Greater peak congestion extends AM and PM peaks by 1 hour each, and reduces peak speeds by 3mph.
Lesser peak congestion reduces AM peak by 0.5 hours, PM peak by 1 hour, and increases peak speeds by 3 mph.