1. Modeling impacts of autonomous vehicles on travel demand with Activity-Based Model
Peter Vovsha, Jim Hicks, Ashish Kulshreshta, Surabhi Gupta (WSP)
Vladimir Livshits, Kyunghwi Jeon (MAG)
Session “Automated/connected vehicles”
May 15, 2017

2. They are coming! Travel demand Vehicle routing, empty trips
Network performance
Improved mobility & accessibility
Vehicle sharing, MAAS
Connectivity, optimization of road & intersection capacity

3. Modeling aspects & assumptions in previous attempts to model AVs
Direct impacts:
Secondary assumptions:
Increase auto availability
Decrease auto time coefficient due to convenience & productivity
Decrease auto access time and parking search time
Increase freeway capacity
Increase auto occupancy
Increase trip rates for non-work and non-home-based trips
Decrease parking cost
Modal shifts
Less demand for parking
More demand for pocket lanes and pick-up/drop-off facilities

4. 3 possible approaches Adapt existing travel model:
If not an advanced one it will require many a priori external assumptions
Develop a stand-alone QRS:
Can serve long-term strategic purposes
More flexible but hardly a replacement for detailed modeling
Adjust advanced ABM:
Back to behavioral foundations
Revise network procedures

5. MAG CT-RAMP2 Approach 100% penetration of AVs: AVs owned:
Partial penetration version is on the way
AVs owned:
Shared AVs will require and additional layer
Focus on primary impacts:
Try to avoid secondary assumptions but some are necessary

6. How will autonomous vehicles affect travel demand and traffic?
(1) Elderly, youth, disable, and other people w/o driver license will have access to cars
ABM: SOV, HOV/driver modes not constrained by age

7. How will autonomous vehicles affect travel demand and traffic?
(2) Cars available at any location any time, not necessarily from home for entire tour
ABM: Trip mode combinations on the tour less restrictive with any sequence of auto and transit
Home
Work
Shop
Shop

8. How will autonomous vehicles affect travel demand and traffic?
(3) Empty repositioning trips made by AVs
ABM: Certain travel tours with 1 destination and long duration (4h+) may have car repositioning trips to and from home
Home
Work

9. How will autonomous vehicles affect travel demand and traffic?
(4) General convenience of AV-KNR versus PNR and transit with walk access/egress:
ABM: KNR convenience parameters equalized to auto
Home
KNR
Work

10. How will autonomous vehicles affect travel demand and traffic?
(5) In-Vehicle Time Productivity:
ABM: productivity “Bonus” for premium transit (-25% of IVT) applied to CAV

11. How will autonomous vehicles affect travel demand and traffic?
(6) Optimized use of highway capacity, more efficient driving, increased intersection capacity:
ABM: Assumed link capacity growth 5-10%

12. Trips by Mode: CAV vs. Baseline 2035
Substantial growth in auto “driver” trips
Elimination of many passenger, walk, taxi, and school bus trips
Shift of transit users to KNR

13. Trips by Mode (Impact on Transit): CAV vs. Baseline 2035
Shift of transit users to Kiss and Ride

14. Le Vine et al, 2017
Scenarios based on assumptions on upper limit of vehicle deceleration and reaction time

15. AVs freeway capacity improvements (Mahmassani, 2016)
Leader
Follower
Spacing
Connected
Sc (73 feet)
Autonomous
Sa (73 feet)
Regular
Sr (146 feet)

16. AVs freeway capacity improvements (Mahmassani, 2016)
Event
Probability
Spacing
Connected vehicle (CV) Pc
Autonomous vehicle (AV) Pa
Regular vehicle (RV) Pr
CV follows CV
(Pc)2
Sc (73 feet)
AV follows any vehicle
Sa (73 feet)
RV follows any vehicle or CV follows AV
1- Pa - (Pc)2
Sr (146 feet)
Mix
Sc(Pc)2 + SaPa + Sr[1-Pa-(Pc)2 ]

17. AVs freeway capacity improvements (Mahmassani, 2016)
Regular vehicles
Connected vehicles
Autonomous vehicles
Average lane capacity, veh/h
100% 0%
1,800
50%
2,057
2,400
2,880
3,600

18. Conclusions on current State of Practice in modeling AVs
A lot of uncertainty and need for thinking out of box, especially for MAAS
Need to focus on behavioral foundations and modeling of direct impacts
Modeling secondary assumptions is less useful and can be misleading
Advanced ABM offers useful features for modeling AVs
Gradual AV penetration scenarios have to be considered

19. Contact(s) Peter Vovsha, PhD Vladimir Livshits, PhD
Assistant Vice President, WSP
Systems Analysis Group
Vladimir Livshits, PhD
Maricopa Association of Governments (MAG)
Program Manager