Environmental Implications for Shared Autonomous Vehicles
Using Agent-Based Model Simulation
Dan Fagnant & Dr. Kara Kockel...
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Environmental Implication for Shared Autonomous Vehicles

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

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Environmental Implication for Shared Autonomous Vehicles

  1. 1. 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-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.

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