Environmental Implications for Shared Autonomous Vehicles

Using Agent-Based Model Simulation

Dan Fagnant & Dr. Kara Kockelman f

f

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 - 3 AM

3 AM - 6 AM

6 AM - 9 AM

9 AM - Noon

Noon - 3 PM

3 PM - 6 PM

6 PM - 9 PM

9 PM - 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.