Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies

Figures & data

Figure 1. Transport land use feedback cycle (Wegener & Fürst, 1999; adapted by Bertolini, 2012).

Table 1. Summary of study characteristics and results on impacts of AVs on travel behaviour and land use.

Author(s) (year)	Study region/area	Methodology	Main model assumptions	Scenarios/ assumptions on transport supply	Main Results
Burns, Jordan, and Scarborough (2013)	Ann Arbor, USA	Queuing and simulation Models	Constant travel speed with peak hour factors; Grid network; avg. waiting time: 0,4 min	Replacement of all trips of vehicles driven less than 70 miles/day with SAVs	−85% vehicles
Kornhauser (2013)	New Jersey, USA	Activity-based-Model	Constant travel speed Grid network	Replacement of all private vehicle trips with SAVs (ridesharing) with pick-up stations	−46% vehicles
Bourghout, Rigole, and Andreasson (2014)	Stockholm, Sweden	Hybrid micro-mesoscopic traffic simulation Model	Link speeds as 75% of free-flow speeds for trip assignment model	Replacement of all home-based work private vehicle trips with SAVs	+24% VKT; −92% vehicles
				Replacement of all home-based work private vehicle trips with SAVs (ridesharing); acceptable travel time increase 30% to 50%	−11% to −24% VKT; +13–25% VHT −95% vehicles
Fagnant and Kockelman (2014)	Hypothetical city, USA	Agent-based-Model	Constant travel speed for peak/off-peak; Grid network; avg. waiting time: 0,3 min	3,5% of trips served by SAVs	+11% VMT −91% vehicles
Gucwa (2014)	San Francisco, USA	Activity-based Modelling approach	Mode-Choice Model	+100% road capacity	+2% VMT
				−100% VOT for private AVs	+13% VMT
				+10% to+100% road capacity, −25% to −100% VOT for private AVs	+4% to +15% VMT
Spieser et al. (2014)	Singapore, Singapore	Design-oriented approach	Avg. travel speed is periodically time-varying	Replacement of all private vehicle trips with SAVs	−66% vehicles
Childress, Nichols, Charlton, and Coe (2015)	Seattle, USA	Activity-based-Model	Mode and Trip-Choice Model Capacity changes for freeways and major arterials	+30% road capacity	+4% VMT; −4% VHT
				+30% road capacity, −35% VOT (household trips) for private AVs	+5% VMT; −2% VHT
				+30% road capacity, −35% VOT, −50% parking cost for private AVs	+20% VMT; +17% VHT −0.3 and −1.6 percentage points in PT and walk share
				SAVs with cost of $1.65/mile (no private vehicle trips possible)	+35% in VMT; −41% in VHT +4 and +5 percentage points in PT and walk share
Fagnant, Kockelman, and Bansal (2015)	Austin, USA	Agent-based-Model	Hourly varying link-level travel speeds	1,3% of regional trips served by SAVs	+8% VMT −89% vehicles
ITF (2015)	Lisbon, Portugal	Agent-based-Model	Link travel speed based on trip assignment Rule-based Mode-Choice Model based on existence/non-existence of PT Three different sizes of SAVs	Replacement of all motorised trips by SAVs	+44% to +89% VKT; −84% to −89% parking spaces −77% to −83% vehicles
				Replacement of all motorised trips by SAVs (ridesharing)	+6% to +22% VKT; −93% to −94% in parking spaces −87% to −90% vehicles
Kim, Rousseau, Freedman, and Nicholson (2015a)	Atlanta, USA	Activity-based-Model	Mode and Trip-Choice Model	+50% road capacity	+4% VMT
				+50% road capacity, −50% VOT for private AVs	+13% VMT
				−50% VOT, +50% road capacity, −71% operating and −100% parking cost for private AVs	+24% VMT; +12% VHT; −0.8 percentage points in PT share
Kim, Yook, Ko, and Kim (2015b)	Seoul, South Korea	Agent-based-Model	Travel behaviour and residential location choices incl. locational preference factors	Increase in road capacity, 100% market share of private AVs Preference for road and city centre accessibility and low-price regions	More dispersed development of urban space
Zhang, Guhathakurta, and Fang (2015)	Hypothetical city, USA	Agent-based-Model	Constant travel speed for peak/off-peak; Grid network; Avg. Waiting Time: 2 min	2% of trips served by SAVs	−90% parking spaces
				2% of trips served by SAVs (car- and ridesharing)	−91% parking spaces
Bischoff and Maciejewski (2016)	Berlin, Germany	Agent-based-Model	Time-varying link travel times Demand-supply balancing dispatching strategy Max. waiting time: 15 min	Replacement of all private vehicle trips by SAVs	−91% vehicles
Boesch, Ciari, and Axhausen (2016)	Zurich, Switzerland	Agent-based-Model	Travel times from MATSim for actual trips Max. waiting time: 10 min	Replacement of all private vehicle trips by SAVs	−90% vehicles
Chen, Hanna, and Kockelman (2016)	Hypothetical city, USA	discrete-time Agent-based-Model	Constant travel speed for peak/off-peak; Grid network Avg. waiting time: 7 to 9 min	10% of trips served by SAVs	+7% empty VMT; −87% vehicles
				10% of trips served by electric SAVs (with recharge time and vehicle range)	+7% to +14% empty VMT; −85% to −73% vehicles
Chen and Kockelman (2016)	Hypothetical city, USA	discrete-time Agent-based-Model	Constant travel speed for peak/off-peak; Multinomial logit Mode-Choice Model Grid network; avg. waiting time: 3 min	−65% VOT, $0.85/mile operating cost for SAVs (with battery recharge time and vehicle range)	−25 percentage points in car share −3 percentage points in PT share
Correia and van Arem (2016)	Delft, Netherlands	Assigning private AV trips to road network	Mode-Choice Model Travel times change after trip assignment	Replacement of private vehicles with private AVs in households	+17% VMT; +3 percentage points in car share
				Replacement of private vehicles with private AVs in households, −50% VOT for private AVs	−49% VMT; +9 percentage points in car share
Fagnant and Kockelman (2016)	Austin, USA	Agent-based-Model	Hourly varying link-level travel speeds Avg. waiting time carsharing: 1,9 min Avg. waiting time ridesharing: 1,2 to 1,4 min	1,3% of regional trips served by SAVs 1,3% of regional trips served by SAVs (ridesharing) Acceptable travel time increase for ridesharing 20% to 40%	+9% VMT; −90% vehicles +2% to +5% VMT −90% to −91% vehicles
Friedrich and Hartl (2016)	Stuttgart, Germany	Macroscopic and microscopic travel demand Model	Link travel speed based on trip assignment Rule-based Mode-Choice Model based on existence/non-existence of PT Two different sizes of SAVs	Replacement of all motorised trips by SAVs	+18% to +39% VMT; −77% to −81% vehicles −77% to −83% parking spaces
				Replacement of all motorised trips by SAVs (ridesharing)	−20% to +36% VMT; −90% to −93% vehicles −90% to −93% parking spaces
Hörl, Erath, and Axhausen (2016)	Sioux Falls, USA	Agent-based-Model	Time-varying link travel times Demand-supply balancing dispatching strategy Mode-Choice Model; max. waiting time: 17 min	−65% VOT, $0.85/mile cost for SAVs	+60% VMT; −20 percentage points in private car share −10 and −8 percentage points in PT and walk share
Thakur, Kinghorn, and Grace (2016)	Melbourne, Australia	Land Use and Transport Interaction Model	Travel behaviour and residential location choices with accessibility to employment as explanatory variable	−50% VOT for private AVs	+4% population in inner parts of the city −3% population in the far outer suburbs
				Replacement of private vehicles with SAVs (ridesharing) with 0.49€/km operating cost	−4% population in inner parts of the city +3% population in the far outer suburbs
Auld, Sokolov, and Stephens (2017)	Chicago, USA	Activity-based travel demand Model	Mode and Trip-Choice Model Link travel speed based on trip assignments	+12% to 77% road capacity	+1% to +4% VMT
				−25% to −75% VOT, 20% market share of private AVs	+2% to +18% VMT
				−25% to −75% VOT, 75% market share of private AVs	+10% to +59% VMT
				+77% road capacity, −25% to −75% VOT, 100% market share of private AVs	+21% to +79% in VMT
Bangemann (2017)	Munich, Germany	Microscopic travel demand Model	SAVs with two seats	Replacement of all private vehicle trips with electric SAVs (with recharge time and vehicle range)	+14% VMT −91% vehicles
Gelauff, Ossokina, and Teulings (2017)	Netherlands	Land Use transportation interaction Model	Home and job location and commuting mode choices incl. prices, accessibility of jobs, travel costs	−20% VOT and travel time for private AVs	−1% population in big cities; −2,5% population in mid-sized cities; +1% population in non-urban regions
				−20% travel time (compared to private car), −100% out-of-vehicle travel time for PT, −50% access/ egress time of trains	+3% in population in big cities; −3% population in suburbs of smaller cities; −2% population in non-urban regions
Heilig, Hilgert, Mallig, Kagerbauer, and Vortisch (2017)	Stuttgart, Germany	Agent-based travel demand Model	Combined Destination and Mode-Choice Model Avg. waiting time: 7,5 min	−45% cost/mile compared to private car for SAVs (ridesharing), −70% cost/mile with occupation rate >=1,64	−20% VMT; −85% vehicles; +4 percentage points in PT share; +8 and +5 percentage points walk and cycle share
Levin, Kockelman, Boyles, and Li (2017)	Austin, USA	Cell transmission model-based dynamic network loading simulator	Link travel speed based on traffic flow simulator; Waiting time: 10 min	Replacement of all private vehicle trips with SAVs	−72% vehicles
Martinez and Viegas (2017)	Lisbon, Portugal	Agent-based Model	Link travel speed based on trip assignment Rule-based Mode-Choice Model Max. waiting and travel time increase: 15 min	Replacement of all motorised trips by SAVs (ridesharing)	−25% VKT; −95% vehicles
				Replacement of all motorised trips by SAVs (ridesharing) and taxi-buses (8–16 seats, boarding at specific points)	−29% VKT −97% cars, +568% buses
Meyer, Becker, Boesch, and Axhausen (2017)	Switzerland	Travel demand Model (macroscopic)	Land use effects based on accessibility changes to work places Link level travel speed based on trip assignments AV availability for children, adults without driver license and elderly people	+80% road capacity outside urban areas, +40% in urban areas (private AVs)	Minor gains in accessibility for rural municipalities, no change/small decrease in greater cities
				+80% road capacity outside urban areas, +40% in in urban areas (SAVs)	Moderate accessibility gains in rural municipalities, decrease in larger agglomerations
Liu, Kockelman, Boesch, and Ciari (2017)	Austin, USA	Agent-based-Model	Time-varying link travel times Mode-Choice Model Avg. waiting time: 3 min	−50% VOT, $0.5/mile operating costs for SAVs	+9,8% empty VMT; SAV fleet = 17% of travellers
				−50% VOT, $0.75/mile operating costs for SAVs	+13,2% empty VMT; SAV fleet = 15% of travellers
				−50% VOT, $1/mile operating costs for SAVs	+15,7% empty VMT; SAV fleet = 13% of travellers
				−50% VOT, $1.25/mile operating costs for SAVs	+15,1% empty VMT; SAV fleet = 13% of travellers
Llorca, Moreno, and Moeckel (2017)	Munich, Germany	Agent-based-Model	Time-varying link travel times Demand-supply balancing dispatching strategy Average waiting time: 8 min	Replacement of 20% of private vehicle trips with SAVs	+5% VMT; −14% vehicles (overall)
				Replacement of 40% of private vehicle trips with SAVs	+7% VMT; −28% vehicles (overall)
Zhang (2017)	Atlanta, USA	Agent-based-Model combined with Monte Carlo simulation and UrbanSim	Residential and employment location choices determined by commute transportation cost (residential) and human capital accessibility, available commercial and industrial spaces (employment) incl. locational preference factors Constant travel speed for different periods	Replacement of all private vehicle trips with SAVs −100% VOT, $0.3/mile operating costs for SAVs	+7% to +10% in median distance to CBD for households with young people (kids/no kids) −7% to −2% in median distance to CBD for households with old people (kids/no kids)
				Replacement of all private vehicle trips with SAVs −100% VOT, $0.13/mile to $0.5/mile operating costs for SAVs −90% parking spaces	−1.8% to −17.5% job density in inner city parts and +0.2% to +9.8% job density in suburban area for secondary sector +0.3% to +11.8% job density in inner city parts and −2.5% to −7,8% job density in suburban area for tertiary sector
Zhang and Guhathakurta (2017)	Atlanta, USA	Agent-based travel demand Model	Constant travel speed for different day periods; Avg. Waiting Time: 3,8 min	5% of trips served by SAVs (car- and ridesharing); −100% parking cost $0.5/minute operating costs (carsharing) and $0.3/minute (ridesharing)	−4,5% in parking land
Zhao and Kockelman (2017)	Austin, USA	Travel demand Model (traditional trip-based four-step Model)	Hourly varying link travel speeds (congested time information) Mode-Choice Model	−25% to −75% VOT for private AVs and SAVs 1$/mile operating cost (private AVs) and 1.5$/mile (SAVs)	+18% to +41% VMT
				−50% in VOT, −50% to −100% parking cost for private AVs and SAVs 1$/mile operating cost (private AVs) and 1.5$/mile (SAVs)	+26% VMT
				−50% in VOT for private AVs and SAVs 1$/mile operating cost (private AVs) and 1$/mile (SAVs)	+28% VMT
				−50% in VOT for private AVs and SAVs 1.5$/mile operating cost (private AVs) and 1.5$/mile (SAVs)	+29% VMT
Auld, Verbas, Javanmardi, and Rousseau (2018)	Chicago, USA	Activity-based travel demand Model	Mode and Trip-Choice Model Fleet penetration based on model (AV cost) Link level travel speed based on trip assignments	47.8% to 100% fleet penetration of private AVs	+6% to +8% VMT
				−30% VOT, 13.4% to 100% fleet penetration of private AVs	+15% to +24% VMT
				−50% VOT, 13.4% to 100% fleet penetration of private AVs	+21% to +43% VMT
Boesch, Ciari, and Axhausen (2018)	Zug, Switzerland	Agent-based-Model	Time-varying link travel times Mode-Choice Model	−38% VOT for private AVs, −54% VOT for SAVs +25% operating cost for private AVs (0,22CHF/km); −50% operating cost for automated PT (0,13CHF/km); 0,46 CHF/km operating cost for SAVs	+16% VMT −12 percentage points in private car share −4 and −16 percentage points in PT and slow modes share
Kröger, Kuhnimhof, and Trommer (2018)	Germany and USA	Aspatial travel demand Model	Travel speeds constant to today Combined mode and distance choice (No traffic assignment) AV market share based on diffusion model AV availability for teenagers, adults without driver license and mobility-impaired people	−25% VOT for private AVs, 7,5% market share	+3,4% in VKT; +1,3 and −0.2 percentage points in car and PT share
				−25% VOT for private AVs, 29,3% market share	+8,6% in VKT; +3,8 and −0.4 percentage points in car and PT share
				−25% VOT for private AVs, 10,1% market share	+2,4% in VKT; +1 and −0.3 percentage point in car and PT share
				−25% VOT for private AVs, 37,6% market share	+8,6% in VKT; +3,7 and −0.9 percentage points in car and PT share
Zhang et al. (2018)	Atlanta, USA	Activity-based travel Model	Varying link travel speeds (congested time information); no trip delay	Replacement of private vehicles with private AVs in households determined by min. number of AVs to satisfy travel demand of household members	+13.3% empty VMT −9.5% vehicles

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