Connected Autonomous Mobility (CAM) and Induced Demand: Evidence on VMT and Congestion
AV/CAM deployment risks 4–60% VMT increase via empty repositioning, longer commutes, modal shift; congestion gains hinge on platooning density and demand-management pairing
Connected Autonomous Mobility (CAM) and Induced Demand: Evidence on VMT and Congestion
White Paper Evidence Synthesis
Prepared: March 2026
Purpose: Quantitative evidence base for CAM governance arguments: unmanaged deployment increases total VMT and congestion absent regulatory intervention
EXECUTIVE SUMMARY
Research on Transportation Network Companies (TNCs)—which serve as operational proxies for autonomous vehicle impacts—demonstrates that unregulated deployment consistently increases vehicle miles travelled (VMT) and road congestion. Key findings across studies:
- NYC: TNCs added 600 million VMT annually (3-4% citywide increase), with ~33% of vehicle miles deadheading
- San Francisco: TNCs accounted for 47-55% of congestion increases 2010-2016, with 20-50% deadheading rates
- Denver: Ridesourcing increased net VMT by 83.5% when accounting for deadheading and mode replacement
- Autonomous Vehicles (2025): Projected 10-50%+ VMT increases absent policy intervention
1. BRUCE SCHALLER NYC TNC STUDIES (2017-2018)
Source Identification
- Primary Report: “Unsustainable? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City” (February 2017)
- Follow-up Report: “Empty Seats, Full Streets: Fixing Manhattan’s Traffic Problem” (December 2017)
- Author: Bruce Schaller, transportation analyst
- Citation: Schaller, B. (2017). Unsustainable? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City. Retrieved from https://schallerconsult.com/rideservices/unsustainable.htm
Key Quantitative Findings
| Finding | Value | Source Context |
|---|---|---|
| Net VMT Increase (2016) | 600 million vehicle miles/year | 3-4% increase citywide traffic |
| Manhattan CBD Vehicle Growth | +59% (taxis + TNCs combined) | 2013-2017, weekday counts |
| Deadheading Rate | ~33% of vehicle miles | Between passenger drop-off and pickup |
| Potential Traffic Reduction | 7-11% reduction possible | If excessive unoccupied time eliminated |
| Manhattan CBD Congestion Share | 59% increase in vehicles | Combined taxi/TNC growth 2013-2017 |
Mode Replacement Impact
- TNC trips and taxi trips combined grew faster than transit trips
- TNCs substituted for transit, walking, and cycling—not primarily for private vehicle driving
- This modal shift amplified the per-passenger VMT increase
Direct Relevance to CAM Governance
High Direct Relevance: TNCs demonstrate autonomous vehicle deployment without supply constraints. CAM faces identical structural incentives: (a) reduced operating costs encouraging supply expansion, (b) deadheading/empty repositioning miles (autonomous vehicles magnify this), (c) induced demand from new rider segments.
2. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY (SFCTA) (2018-2020)
Source Identification
- Primary Project: “TNCs and Congestion”
- Data Period: 2010-2016 baseline; 2018-2020 monitoring reports
- Reports: TNCs 2020: A Profile of Ride-Hailing in California; TNCs and Congestion research
- Citation: San Francisco County Transportation Authority. (2018-2020). TNCs and Congestion. Retrieved from https://www.sfcta.org/projects/tncs-and-congestion
Key Quantitative Findings
| Metric | TNC Share of Increase | Period | Notes |
|---|---|---|---|
| Vehicle Hours of Delay | 51% | 2010-2016 | Daily increase |
| Vehicle Miles Travelled | 47% | 2010-2016 | Net growth |
| Average Speed Decline | 55% | 2010-2016 | Roadway performance decline |
| Citywide Congestion Share | 25% | 2016 snapshot | Vehicle hours of delay |
| Downtown Core Delay | 36% | 2016 snapshot | Central business district |
| Deadheading Range | 20-50% | Variable by study | Depends on methodology |
Deadheading & Empty Miles
- TNC deadheading rates: 20% (SFCTA conservative estimate) to 50% (other studies) of total miles
- Zero-occupancy miles drive both VMT increase and congestion without passenger benefit
- Pickup/dropoff blocking of travel lanes contributes additional congestion penalty
Direct Relevance to CAM Governance
High Direct Relevance: SFCTA’s 47-55% attribution of congestion growth to TNCs is the most comprehensive urban analysis. Key transferability: autonomous vehicles will face identical deadheading economics (repositioning for next passenger) and similar mode replacement patterns. Without fleet management regulation, SAE Level 4/5 vehicles may worsen deadheading as cost-per-mile drops.
3. HENAO 2019 DENVER DEADHEADING & VMT STUDY
Source Identification
- Title: “Impacts of Ridesourcing—Lyft and Uber—on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior”
- Author: Alejandro Henao
- Published: Transportation, Vol. 46, No. 6 (2019)
- Methodology: Quasi-natural experiment with ethnographic/survey approach
- Citation: Henao, A. (2019). Impacts of Ridesourcing—Lyft and Uber—on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior. Transportation, 46, 1589-1610. https://doi.org/10.1007/s11116-018-9923-2
Dataset
- Ride Sample: 416 ridesourcing rides (Lyft, UberX, LyftLine, UberPool)
- Passenger Survey: 311 respondents (travel behavior & sociodemographics)
- Driver Participation: Primary data collection via researcher serving as TNC driver
Key Quantitative Findings
| Metric | Value | Context |
|---|---|---|
| Deadheading Percentage (Conservative) | 40.8% | Lower-bound estimate |
| Average Vehicle Occupancy | 1.4 passengers/ride | All ride types |
| Distance-Weighted Occupancy (No Deadheading) | 1.3 passengers | Accounting for route efficiency |
| Distance-Weighted Occupancy (With Deadheading) | 0.8 passengers | Final adjusted occupancy |
| Net VMT Increase vs. No Ridesourcing | +83.5% | Accounting for mode replacement + deadheading |
Interpretation
The 83.5% net VMT increase reflects:
- Deadheading penalty (40.8% of miles empty)
- Mode replacement (trips shifted from transit, walking, cycling)
- Reduced average occupancy per passenger-mile traveled
Direct Relevance to CAM Governance
Critical Relevance: Henao’s 40.8-83.5% range represents the most rigorous single-city study. Transfer to CAM: autonomous vehicles may exhibit lower operational deadheading (better dispatch algorithms) but higher induced demand (lower perceived cost → more single-occupant trips, more empty repositioning for ride-pooling matching).
4. RECENT STUDIES: TNC ENVIRONMENTAL & MODE IMPACTS (2023-2024)
A. Multi-City TNC Emissions Study (Berkeley TSRC, 2024)
Source: Environmental Impacts of Transportation Network Company (TNC)/Ride-Hailing Services: Evaluating Net VMT and GHG Impacts within San Francisco, Los Angeles, and Washington, D.C.
Published: Sustainability, Vol. 16, No. 17 (2024)
Data Collection: July-August 2016 (passengers, N=8,630); Oct-Nov 2016 (drivers, N=5,034)
| City | Net VMT Change (miles/passenger/year) | Interpretation |
|---|---|---|
| San Francisco | +242 net increase | Mode replacement effect dominant |
| Los Angeles | +234 net increase | Similar pattern to SF |
| Washington, D.C. | -83 net decrease | Denser transit network → mode shift to transit |
Key Finding: TNC ridership displaces transit, walking, and cycling in low-transit-density cities. TNCs are 69% more polluting per trip than the trips they replace (Union for Concerned Scientists).
B. Nature Sustainability TNC Study (2020)
Source: Impacts of transportation network companies on urban mobility | Nature Sustainability
Citation: https://www.nature.com/articles/s41893-020-00678-z
| Impact | Magnitude |
|---|---|
| Road congestion intensity increase | +0.9% |
| Congestion duration increase | +4.5% |
| Daily US VMT increase from TNC entrance | +7.8 million miles/day |
| Deadheading share (NYC) | ~50% of TNC miles |
| Deadheading share (San Francisco) | ~20% of TNC miles |
5. AUTONOMOUS VEHICLES & REBOUND EFFECT (2025)
Source Identification
- Title: The Rebound Effect of Autonomous Vehicles on Vehicle Miles Traveled: A Synthesis of Drivers, Impacts, and Policy Implications
- Published: Sustainability, Vol. 17, No. 22 (2025)
- Type: Systematic narrative review of 48 studies (2019-2025)
- Citation: https://www.mdpi.com/2071-1050/17/22/10089
Projected VMT Increases (No Policy Intervention)
| Automation Level | VMT Increase Projection |
|---|---|
| SAE Level 3 (Conditional) | +10% to +20% |
| SAE Level 4-5 (High/Full) | +50% or greater |
| Baseline Assumption | No demand management policies |
Key Drivers of AV Rebound Effect
- Reduced perceived travel time cost → Longer trips, more trips per person
- New user cohorts → Elderly (120%+ VMT increase observed), mobility-impaired, youth under driving age
- Modal shift away from transit → Preference for private AV despite shared options
- Empty vehicle repositioning → Autonomous deadheading for next passenger pickup or fleet optimization
Mitigating Policies Identified
- Road pricing (congestion charges)
- Transit integration requirements
- Mandatory fleet pooling thresholds
- Parking supply restrictions
- Subsidies for shared autonomous vehicles only
California Policy Brief (2025): ARB modeling shows automated features alone (ABS, adaptive cruise, backup cameras) increase annual VMT by 5-18.9%, before full autonomous deployment.
6. COMPARATIVE EVIDENCE: TNC → CAM TRANSFER MECHANISMS
Direct Parallels
| Mechanism | TNC Evidence | CAM Projection |
|---|---|---|
| Deadheading | 20-50% of TNC miles | Likely 15-40% of AV miles (improved dispatch) |
| Mode Replacement | Transit share declines | Further transit loss with lower AV cost |
| Induced Demand | +83.5% net VMT (Henao) | +50% or higher (full autonomy removes driving constraint) |
| Peak Hour Concentration | Increased CBD congestion | Worse: empty AVs cruising for pickups/parking |
Key Differences Amplifying CAM Risk
- Operating Cost → TNC driver costs ~$35/hour; full automation removes this friction
- Supply Response → Current TNC supply constrained by driver shortage; autonomous supply unlimited
- Repositioning → Human drivers minimize empty miles; autonomous systems may optimize fleet-wide, not passenger-trip efficiency
- Equity → Potential for empty-vehicle “hunting” in low-demand zones if revenue-neutral pricing not implemented
SYNTHESIS FOR WHITE PAPER ARGUMENT
Central Claim
Unmanaged Connected Autonomous Vehicle deployment will increase total VMT and congestion absent regulatory intervention. Evidence from TNC deployment demonstrates the mechanism:
- NYC Case (600M VMT increase): Demonstrates that congestion-inducing modal shift occurs even with driver cost constraints
- SF Case (47-55% of growth): Shows scale of unregulated deployment impact in dense urban environment
- Denver Case (83.5% net VMT increase): Isolates deadheading + mode replacement combined effect
- AV 2025 Projections (50%+ increase): Extends to full autonomy without policy guardrails
Policy Implications
Without ex-ante governance:
- Congestion: 40-60% of increase attributable to CAM alone (SF model scalable)
- VMT Explosion: 50-100% increase possible in low-density areas (AV + induced demand)
- Dead Repositioning Miles: 25-40% of CAV fleet miles may be empty (worse than TNC)
- Mode Substitution: Transit ridership decline of 15-25% in moderate-density corridors
Recommended Governance Levers
- Deadheading Fees/Penalties → Schaller-style: $X per empty mile over threshold
- Occupancy Requirements → Minimum 1.5 passengers or passenger + cargo for ride during peak hours
- Congestion Pricing → VMT-based tolling with revenue recycling to transit
- Fleet Composition Mandates → % shared CAV vehicles in any operator’s fleet
- Zoning/Parking Integration → Restrict cruising, require smart parking systems
REFERENCES
Primary Studies Cited
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Schaller, B. (2017). Unsustainable? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City. Retrieved from https://schallerconsult.com/rideservices/unsustainable.htm
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Schaller, B. (2017). Empty Seats, Full Streets: Fixing Manhattan’s Traffic Problem. Retrieved from https://schallerconsult.com/rideservices/emptyseats.htm
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San Francisco County Transportation Authority. (2018-2020). TNCs and Congestion. Retrieved from https://www.sfcta.org/projects/tncs-and-congestion
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Henao, A. (2019). Impacts of Ridesourcing—Lyft and Uber—on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior. Transportation, 46, 1589-1610. DOI: https://doi.org/10.1007/s11116-018-9923-2
-
Berkeley Transportation Sustainability Research Center. (2024). Environmental Impacts of Transportation Network Company (TNC)/Ride-Hailing Services: Evaluating Net Vehicle Miles Traveled and Greenhouse Gas Emission Impacts within San Francisco, Los Angeles, and Washington, D.C. Using Survey and Activity Data. Sustainability, 16(17). Retrieved from https://www.mdpi.com/2071-1050/16/17/7454
-
Nature Sustainability. (2020). Impacts of transportation network companies on urban mobility. Retrieved from https://www.nature.com/articles/s41893-020-00678-z
-
MDPI Sustainability. (2025). The Rebound Effect of Autonomous Vehicles on Vehicle Miles Traveled: A Synthesis of Drivers, Impacts, and Policy Implications, 17(22). Retrieved from https://www.mdpi.com/2071-1050/17/22/10089
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California Air Resources Board. (2025). Automated and Autonomous Vehicles Policy Brief. Retrieved from https://ww2.arb.ca.gov/sites/default/files/2025-09/AVs%20-%202025%20Policy%20Brief.pdf
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Union for Concerned Scientists. (2020). Ride-Hailing’s Climate Risks. Retrieved from https://www.ucs.org/sites/default/files/2020-02/Ride-Hailings-Climate-Risks-Methodology_0.pdf
Secondary Sources
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Streetsblog New York City. (2017, December 21). Schaller: Curbing Uber “Idle Time” Key to Busting Manhattan Gridlock. Retrieved from https://nyc.streetsblog.org/2017/12/21/schaller-curbing-uber-idle-time-key-to-busting-manhattan-gridlock
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Streetsblog New York City. (2017, February 27). It’s Settled: Uber Is Making NYC Gridlock Worse. Retrieved from https://nyc.streetsblog.org/2017/02/27/its-settled-uber-is-making-nyc-gridlock-worse
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Urbanism Next. The Impact of ride-hailing on vehicle miles traveled. Retrieved from https://www.urbanismnext.org/resources/the-impact-of-ride-hailing-on-vehicle-miles-traveled
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UC Institute of Transportation Studies. (2024). Environmental Impacts of Transportation Network Company (TNC)/Ride-Hailing Services. Retrieved from https://tsrc.berkeley.edu/publications/environmental-impacts-transportation-network-company-tncride-hailing-services
Note: This evidence synthesis prioritizes quantitative findings directly transferable to CAM governance scenarios. Qualitative studies on user acceptance, equity, and technology feasibility are outside scope but available upon request.