Transport Choices and Vehicle Ownership with Autonomous Vehicles

DEGREE PROJECT IN ENERGY AND ENVIRONMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018

A modelling effort on car ownership, transport mode choice and travel demand with Driverless Technology

VIDE RICHTER

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT Transport Choices and Vehicle Ownership with Autonomous Vehicles

A modelling effort on car ownership, transport mode choice and travel demand with Driverless Technology VIDE RICHTER

Supervisor at KTH: Anna Pernestål Brenden

Supervisor at WSP: Svante Berglund

Examiner: Monika Olsson

Degree project in Energy and Environment, Second cycle The main field of study Sustainable Technology KTH Royal Institute of Technology School of Architecture and Built Environment Department of Sustainable Development, Environmental Science and Engineering SE-100 44 Stockholm, Sweden

TRITA-ABE-MBT-18427

Abstract

Transport is one of the basic needs of a functioning society. Unfortunately, transport also pollutes our cities and release greenhouses gases. Driverless technology is a technology predicted to disrupt the future transport system, and perhaps change how we travel from private cars to shared vehicles. This study focuses on the aspect of privately owned versus shared driverless vehicles, to create more knowledge of how the future transport system will look. A utility-based demand model is used to find the demand for private and shared transport when driverless vehicles are available. The utility of different transport options is estimated by looking at earlier studies about the performance of driverless cars, driverless buses and shared driverless taxis, which is used as input for the utility model. The results indicate that driverless technology will not be a catalyst that makes transport go from private to shared. While driverless buses can improve public transport, and shared driverless taxis outcompete current taxis, driverless technology will also improve private vehicles. The results in this study imply that the sustainability improvements earlier reports have predicted with a high use of shared driverless transportation might not materialise unless efforts are done to increase use of shared transportation.

Keywords Autonomous vehicles, shared autonomous vehicles, driverless buses, vehicle ownership modelling, travel demand modelling

Sammanfattning

Transport är ett av de grundläggande behoven för ett välfungerande samhälle. På samma gång släpper transporter ut både växthusgaser och skadliga partiklar. Självkörande teknik är något som förväntas revolutionera framtidens transportsystem, förhoppningen är att de ska förändra hur folk reser från privata bilar till delade transporter. Denna studie fokuserar på den förhoppningen. Kommer framtidens transporter ske i privata självkörande fordon eller delade självkörande fordon och vad i sin tur betyder det för framtidens transportsystem? Med en nyttobaserad efterfråge- och bilinnehavsmodell modelleras efterfrågan av självkörande delade taxis, självkörande bussar och självkörande privatbilar. Resultaten indikerar att självkörande teknik inte nödvändigtvis kommer vara en katalysator som får människor att sluta äga och använda privatbilar. Självkörande bussar kan göra kollektivtrafiken bättre, och självkörande delade taxibilar kommer troligtvis användas mer än dagens taxis. Men självkörande privatbilar kommer också ha många fördelar, och de som äger dem kommer dessutom troligtvis köra längre sträckor än dagens bilister. Resultatet av denna rapport indikerar därför att de stora förväntningarna som finns på självkörande teknik gällande delade transporter kan vara felaktiga, om inte andra åtgärder också görs för att öka delning. Att delningen inte ökar gör också att de hållbarhetsförbättringar som vissa tidigare rapporter förutspått inte nödvändigtvis kommer ske.

Nyckelord Självkörande bilar, självkörande delade taxis, självkörande bussar, bilinnehavsmodellering, efterfrågemodellering

iii

Acknowledgements

I would like to thank my two supervisors, Svante Berglund at WSP Sweden and Anna Pernestål Brenden at Integrated Transport Research Lab, KTH. I did most of my work at the WSP office in Stockholm with Svante, where he helped me with knowledge and guidance regarding transport systems and transport modelling. Furthermore, Svante helped me by coding the changes in LuTRANS I needed to modify it for my driverless scenarios. Anna Pernestål Brenden helped me with knowledge and guidance about the driverless vehicles research field, to make sure my assumptions were up to date.

Table of contents

Abstract ...... iii Keywords ...... iii Sammanfattning ...... iii Nyckelord ...... iii Acknowledgements ...... iv Abbreviations ...... vi 1. Introduction ...... 1 1.1 Background ...... 1 1.2 Aim and objectives...... 2 1.3 Related work ...... 2 2. Method ...... 3 2.1 Modelling transport choice ...... 3 3. Transport options with driverless vehicles ...... 5 3.1 The OECD – International Transport Forum Scenarios ...... 5 3.2 The Austin Scenario ...... 6 4. Modifying LuTRANS for driverless transport ...... 6 4.1 Scenario year ...... 6 4.2 Overview of Cities...... 8 4.3 Transport options ...... 9 4.4 Overview of choices ...... 16 4.5 Implementation in LuTRANS ...... 16 5. Results ...... 17 5.1 Scenario results ...... 17 5.2 Sensitivity analysis – VoTT of Privately owned driverless vehicles ...... 21 6. Discussion ...... 24 6.1 Competitiveness of different transport options ...... 24 6.2 Car ownership ...... 24 6.3 Sources of error ...... 25 6.4 Induced travel ...... 25 6.5 About Shared Driverless Taxis ...... 25 6.6 Aligning with other studies...... 26 6.7 Implications for sustainability ...... 27 6.8 Transferability ...... 28 6.9 Insights about regulations ...... 28 6.10 Future things to study ...... 28 7. Conclusion ...... 29 8. References ...... 29

Abbreviations

CDC Conventionally driven car

DB Driverless buss

DT Driverless taxi

PDV Privately owned driverless vehicle pkm Passenger kilometres

SDT Shared driverless taxi

VKT Vehicle kilometres travelled

VoTT Value of travel time

1. Introduction

1.1 Background Vehicle transport is a backbone of modern society, we use it to get to our workplaces, to our commodities and to our relatives. However, the transport sector is not without consequences. Transport is one of the main sectors behind climate change, responsible for 7.0 GtCO2 eq (14% of overall emissions) in 2010 and projected to rise to 12 GtCO2 eq by 2050, if not radical mitigation policies are implemented (Intergovernmental Panel on Climate Change, 2015). Transport is also estimated to be behind half of all harmful air pollution, estimated to kill more than 3 million people and cost the world 3.5 trillion USD per year (OECD, 2014). A key issue for societies in the future is to find affordable and sustainable transport systems, which is also a part of the global goals for sustainable development (UN General Assembly, 2015). In that area, driverless vehicles are an emerging technology that has the potential to disrupt current travel systems. What kind of effects they will have on the sustainability of the transport system is however disputed.

In the Swedish context, one-third of Swedish territorial greenhouse gas emissions are from transportation, and two-thirds of the transportation emissions are from road passenger transport (Statistics Sweden, 2018). From 1990 to 2016 road passenger transport greenhouse gas emissions have been reduced by 19% (Statistics Sweden, 2018). During the same time, total territorial greenhouse gas emissions in Sweden have been reduced by 26% (Swedish Environmental Protection Agency, 2017). The main reason for lowered road transport emissions are more efficient vehicles and new propellants, while the reason reductions have not been higher is due to increased vehicle kilometres travelled (VKT) (Swedish Environmental Protection Agency, 2017). For 2016, road passenger transport in Sweden emitted a total of 11 MtCO2 eq (Statistics Sweden, 2018).

Driverless technology is predicted to disrupt the transport system, by enabling vehicles to drive without an active driver. In this report, the term driverless is used over autonomous to distinguish that it is about vehicles that can operate completely without a human driver. Childress et al. (2015) modelled the effect of privately owned driverless vehicles (PDV) and showed that a transport system with PDVs could increase VKT by 19.6%, while also increasing congestion and time spent travelling, due to the increased comfort of PDVs over conventional cars. However, driverless vehicles could also be used as Driverless Taxis (DTs). Greenblatt and Saxena (2015) calculated emission per VKT in a system with right-sized electric DTs, approximating an 87-94% decrease. Chen, Kockelman and Hanna (2016) modelled an electric DT fleet, which replaced up to 6.8 private cars per DT, resulting in reduced congestion and emissions.

Another often suggested transport system with driverless vehicles is to have the DTs that drive several passengers at the same time, this is often called Shared Driverless Taxis (SDT). In models, fleets of SDTs have shown to carry socioeconomically beneficial effects. The International Transport Forum has similarly released three evolving reports around the use of SDTs in Lisbon (OECD – International Transport Forum, 2015; 2016; 2017a). The third and final report models a system with SDT, driverless buses (DB) and with current trains, in the greater metropolitan area of Lisbon, the system would cost

85% of today’s public transport costs, remove 95% of the vehicles and parking spots, reduce CO2e emissions of transport by 62%, while providing better mobility (OECD – International Transport Forum, 2017a).

As can be seen there are different scenarios depicted of system effects that driverless technology could have on society. Models with high amounts of shared driverless transport usage show positive effects in terms of emissions, VKT, parking and accessibility. While models with PDV usage show increased VKT and emissions. The ratio of transportation done with shared driverless transportation methods compared to private options is therefore of interest. Additionally, shared driverless options are also likely to need high market penetration to perform optimally. Bösch, Ciari and Axhausen (2016) have

shown that SDTs become more efficient with higher market penetration, decreasing the number of vehicles needed per user.

Car ownership has been shown to be a determinant in mode choice and VKT travelled by different modes (de Jong et al., 2004). Furthermore, improvements to public transport have been shown to reduce car ownership (Fairhurst, 1975). This indicates that the increased utility of other travel options that do not require a private vehicle should be able to affect car ownership. However, behavioural studies have shown that car owners at times have an irrational preference for using cars (Innocenti, Lattarulo and Pazienza, 2013).

In this study, the utility-based model LuTRANS, used today for car ownership and transport mode choice in Stockholm, will be modified and used. The modification will be to introduce driverless effects on the transportation utility cost of shared and private transport modes. With a forecasted population of Stockholm County, the model will then produce transport data in terms of transport choices for the population. The results will hopefully provide some insight on future transport systems, and what policymakers can do to influence them.

1.2 Aim and objectives The aim is to predict the competitiveness of driverless shared modes compared to owning a private vehicle.

Objectives:

 Find a set of transport options that are expected to exist with driverless technology  Look at earlier reports of such transport options to create a reasonable picture of their performance  Use scenarios and modelling tools to model the use of shared transport options versus private transport options  Analyse results in terms of societal impacts and sustainability of future transport systems.

1.3 Related work There are some earlier studies that have researched similar issues. There have been two studies estimating the market share of SDTs in Austin (Chen and Kockelman, 2016; Liu et al., 2017). The main difference in this study is that it will include scenarios with PDVs, and this model has an integrated car ownership model. Chen and Kockelman (2016) and Liu et al. (2017) also have some assumptions that do not seem to align with current knowledge in transport science. Both have the price per kilometre for conventionally driven cars (CDC) in the mode choice model set to include the fixed cost of car ownership. However, when a traveller already owns a car the variable cost of an additional trip is the main influencer for mode choice (Davidson and Spinoulas, 2016). Furthermore, the value of travel time (VoTT) modifiers for SDTs in both Chen and Kockelman (2016) and Liu et al. (2017) are lower than usual values for public transport, which seems optimistic. While Liu et al. (2017) assume no increased travel time compared to CDCs, which seems unlikely as SDTs must pick-up and drop-off other passengers to be shared.

There have also been studies on vehicle ownership impacts of PDVs, but both focused on households changing from several vehicles to one (Schoettle and Sivak, 2015; Zhang, Guhathakurta and Khalil, 2018). Car ownership in this study will instead focus on if households own any private car, with no focus on how many cars are owned by households that own vehicles. The studies are therefore not quite comparable.

There are two earlier studies modelling the use of driverless vehicles in Stockholm. Rigole (2014) developed an early model to test the performance of SDTs in Stockholm. The model was simplified but still showed that SDTs could perform well in Stockholm, albeit without sharing or without acceptance

of delays the total VKT would increase compared to today. Pernestål Brenden and Kottenhof (2018) modelled driverless shuttles replacing bus-lines with low frequencies. The report showed that both as a feeder service and as a cross heavy capacity modes service the transport could be popular and increase public transport usage in the area (Pernestål Brenden and Kottenhof, 2018).

2. Method

The method will be to use a transport model to model transport choice and car ownership in Stockholm when driverless technology exists.

To do this, a literature review will be performed to create a view of what transport options are expected to exist with driverless technology. Both in terms of private transport options, and in terms of shared transport options. After that, an extended literature review of the chosen transport options will be performed. This literature review will focus on the performance of chosen transport options in terms of utility for the user. The goal is to get reasonable data as input to use in a transport model to model the choice of transport for Stockholm County.

For the modelling step, LuTRANS will be used. LuTRANS is an established transport model used to model transport choice and car ownership in Sweden. The model used in this report is created for the Mälardalen region, but only Stockholm County will be used. LuTRANS has a set of variables which calculates the utility of different transport modes, which is what will be modified.

Lastly, the results of the ratio of use of shared versus private transport modes and the result of car ownership will be discussed in terms of likely societal impacts. This will be done by combining the knowledge gained from the results with results from earlier studies.

2.1 Modelling transport choice When choosing transport, individuals will assume the utility of different transport options and choose the options which are most likely to maximise utility for the individual. The utility cost of transport is affected by quantitative factors, where travel time (divided into in-vehicle time, waiting time and walking time) and cost of travel are the two major areas (Ortúzar and Willumsen, 2011). Qualitative factors such as comfort, perceived safety and opportunities to perform other activities during travel also affect utility (Ortúzar and Willumsen, 2011). The qualitative factors affect the quantitative factors in that it changes the utility loss per travel time. This concept is often described as the Value of Travel Time (VoTT) of a travel mode. A higher comfort of travel creates a lower VoTT, as VoTT is the negative utility cost of travel. For example, people are more likely to choose a four-hour trip with a seat rather than a three-hour trip standing up, as the seat offers comfort and opportunities to perform other activities which lowers VoTT.

Looking at a system perspective, choice of transport is also affected by characteristics of the traveller and the trip. Trip makers with more money are likely to value cost lower and comfort and speed higher. Moreover, things such as age, household structure and driving license ownership affect the choice of transport (Ortúzar and Willumsen, 2011). For the characteristics of a trip, daily commutes to work or school are more likely to be done with public transport due to the regularity of the trip (Ortúzar and Willumsen, 2011).

There are many different methods to model car ownership that are used for different types of results (de Jong et al., 2004; Ortúzar and Willumsen, 2011). Basic early models for infrastructure planning only consisted of time-series extrapolations with expected growth of car ownership until a saturation level (Ortúzar and Willumsen, 2011). However, other models for transport policy decisions have focused on household-level characteristics, with household-level income and residential density as major factors (Ortúzar and Willumsen, 2011). Later developed models have integrated car ownership and car use models, including household level income and residential density as well, but adding data from car use models (Ortúzar and Willumsen, 2011).

2.1.1 LuTRANS The model for this thesis is done in LuTRANS. Articles about LuTRANS have earlier been publicised, and in Jonsson et al. (2011) the model is described in short. However, as a source for information about LuTRANS in this report, an internal WSP company document (WSP, 2016) was used, the document is in Swedish and can be accessed by contacting the author of this thesis.

LuTRANS is a simplified version of the standard Swedish transport model Sampers (Trafikverket, 2018). LuTRANS aggregate trips into work trips and other trips instead of looking at more disaggregated trip purposes. LuTRANS also aggregate socioeconomic data over small zones. In the Mälardalen region there are 2996 zones in with the average income specified, whereof 1364 are in Stockholm County and used in this report. LuTRANS uses Emme (INRO, 2018) for the road networks and in-vehicle time calculations. Trips in LuTRANS can be performed by car as a driver, by car as a passenger, by public transport, by bike or by walking.

LuTRANS contains a car ownership model, which calculates the percentage of households in an area that has access to a car. The car ownership model uses three variables for car ownership. The average household income in the zone, the population density and on the utility of car trips compared to the utility of other travel modes from the area to places of work. The chance of owning a car is expected to increase with added household income, increasing quickly at low-income levels and then levelling out. The car ownership model only uses work trip utility of car ownership, as the utility of other trips was tested as an explanatory factor when building the model, but the result was non-significant (WSP, 2016).

For the utility of trips, LuTRANS considers in-vehicle time, waiting time, walking time and monetary cost. Each time component has a different value, depending on the utility loss the user feels when the time increases. This value can be interpreted as the comfort level during the time spent on the specified activity. VoTT is the in-vehicle value of a specific vehicle. As the model is written, monetary cost is programmed with 2005 years value of SEK. In this report, however, values are written in 2018 years value of € to create comparability to other studies. Things modelled with a value of 3 SEK takes the value of €0.35 in the report.

The model is built to logically outline how transport choice is performed. Thereafter, attitudes such as the utility value of time and comfort compared to the utility value of money for the citizens in the city is calibrated. The calibration is performed using revealed preference travel habit surveys. Revealed preference travel surveys reduce bias compared to stated choice studies (Fifer, Rose and Greaves, 2014). The model used in this study is calibrated to data from RES 2005-2006, a national revealed preference study performed in Sweden 2005-2006 (SIKA, 2007). RES 2005-2006 is still commonly used to calibrate transport models in Sweden, due to newer surveys having lower answering frequency and hence lower data quality (Persson and Jiang, 2015).

The current transport choices in the model will be modified to better represent driverless vehicle transport systems. The public transport option will be modified for how different shared modes with driverless technology are expected to perform, and the private car option will be modified to the expected performance of private vehicles with driverless technology. As this is not a complete redesign of a model that accommodates system change to driverless technology, the modification will be done in the transport mode choice part of the code, where the calculation of utility of different transport modes is performed.

To change the model to accommodate driverless transport changes will be done to the categories of values that affect transport choice: walking, waiting and travel time as well as VoTT and monetary cost. To give accurate new values literature on other models of driverless transport systems will be used. The changes in the LuTRANS code necessary was written by Svante Berglund, the thesis supervisor at WSP and one of the creators of LuTRANS.

3. Transport options with driverless vehicles

To modify LuTRANS for reasonable performance with driverless technology there is a need for driverless transport options to use in the model. For shared transport, there are two main transport systems identified. One system is improved public transport through DBs. OECD – International Transport Forum (2017a; 2017b; 2017c) reports of Lisbon, Helsinki and Auckland have these as a transport option. The second option is SDTs, which is one of the most discussed expected transport modes with driverless technology. There are several models on SDTs, but one of the more well-studied cities in terms of SDT performance is Austin (Liu et al., 2017; Loeb, Kockelman and Liu, 2018; Loeb and Kockelman, 2018). The OECD scenario operates with both DBs and SDTs as available options, as well as retains high capacity public transport already available in the cities. For private transport, PDVs will be used. PDVs are expected to be similar to CDCs in terms of usage.

The next parts of the thesis will describe how the shared systems operate in the selected references. The picked-out references being the OECD – International Transport Forum studies, with several transport options, and the Austin SDT studies. The point of the chapter is to give an understanding of the two different driverless transport systems. The output data from these models will also be used to an extent as input data for LuTRANS, albeit other studies will also be used as references.

3.1 The OECD – International Transport Forum Scenarios In the OECD – International Transport Forum studies shared transport is done by one of three modes. DB directly to the destination, high capacity public transport with DB as feeder if needed, or by SDT.

The SDT go door to door and are booked in real time, like taxis. Some key differences do exist. Sharing is one of those differences, which means that the SDT will pick up several passengers at the same time if effective. Moreover, the SDTs are directed by an operator to optimize transport after system demand, with limits for how much single customers can wait or be detoured. The maximum wait time is 5 minutes for trips that are less or equal to 3 km and up to 10 minutes for trips that are above or equal to 12 km. This is regulated with a maximum detour time; the maximum detour time includes the time the passenger had to wait and is 7 minutes for a trip that is less or equal to 3 kilometres up to 15 minutes for trips that are equal or longer than 12 kilometres. The SDT is a minivan rearranged with six seats for easy entrance and exit. (OECD – International Transport Forum, 2017a)

The other option for direct trips is the DB. Like the SDTs the DBs operate as a fleet optimized according to demand which makes them different from current buses. DBs are booked at least 30 minutes in advance and boarding can be up to 400m away from your preferred position. Waiting time has a ten- minute tolerance from the preferred booking time. Travel time is at minimum equal to linear speed from origin to destination of 15 km/h. If there is no acceptable bus stop close, or if there is no good way to generate a bus with at least 50% capacity used at some parts of the trip and at least 25% distance-based occupancy the passenger upgraded to an SDT but still paying the DB price. The buses are 8 or 16 seats with no place for standing. (OECD – International Transport Forum, 2017a; 2017b; 2017c)

The last option is high capacity public transport supported by DB. These trips are a sub-category of the DB ordered trips, but where passengers are assigned to take high capacity public transport as a part of their trip. Again, some parameters of minimum expected quality for the passenger is used. The end or start point must have a walking time of less or equal to 10 minutes. The passenger can only need to perform one intra-high capacity public transport transfer, which creates a maximum of two transfers per trip with the transfer between the DB and the high capacity public transport. Moreover, trips are only assigned to the high capacity network for trips longer than 5 km with the high capacity mode. All trips that do not fill one of these factors are assigned a direct DB instead. (OECD – International Transport Forum, 2017a)

For the Helsinki and Auckland reports, several scenarios of replacement of current trips with the new system were modelled (OECD – International Transport Forum, 2017b; 2017c). Different of these scenarios will be referenced in this thesis depending on what fits the report.

3.2 The Austin Scenario Austin, Texas is one of the most modelled cities for driverless shared transport. Chen, Kockelman and Hanna (2016) made the first model with DTs and proposed the settings. In Chen and Kockelman (2016) the model was further developed, testing pricing impact on the choice of transport mode. Thereafter three more studies have been conducted in the transport model program MATSim developing the concept and introducing SDTs (Liu et al., 2017; Loeb, Kockelman and Liu, 2018; Loeb and Kockelman, 2018).

The Austin models use normal five seat cars, calculating with a maximum of 4 riders per vehicle (Loeb and Kockelman, 2018). The model is similar to the SDT case of the OECD – International Transport Forum (2017a; 2017b; 2017c) reports in that it is vehicles on demand that go the door-to-door. In Loeb and Kockelman (2018) performance of different vehicles as SDTs are tested, a hybrid electric vehicle performs the best in terms of price and waiting time.

The model used in the Austin MATSim studies is a modified version of a MATSim model created by Bösch, Ciari and Axhausen (2016) for Driverless Vehicles. MATSim is an activity-based multiagent transport simulation framework, it calculates utilities both of travel and of activities to model choices of individuals in a co-evolutionary process until a dynamic user equilibrium is reached (Horni, Nagel and Axhausen, 2016). The trip demand and road network are created by Liu et al. (2017), using travel demand predictions for 2020.

Travellers are simulated to do their requests five minutes before preferred pick-up time. If a vehicle arrives before that, it waits. If a vehicle is not there, the passenger waits, and the vehicle is counted as late. When the vehicle has arrived at the last location of the trip, the vehicle is available for a new trip. Movement is modelled as direct relocation (“teleportation”) with a travel time delay. The delay for requests is calculated with beeline distance and average speeds. The delay for actual trips uses MATSim, which represents travel time from realistic traffic simulations. No redistribution of the vehicles is done, they wait where their last request was finished. (Bösch, Ciari and Axhausen, 2016)

SDTs are generated by being placed at the start of the simulation at the locations of the first requests. Ridesharing is modelled with a first-in-last-out model due to coding restrictions in the system where the vehicle can’t change course before its intended arrival time. The system has a goal to serve 95% of trips, trips that are not served are determined with a stochastic variable. The chance of the user cancelling the trip is 0% until 7.5 minutes wait time and then going towards 100% when wait time becomes one hour. (Loeb and Kockelman, 2018)

4. Modifying LuTRANS for driverless transport

4.1 Scenario year LuTRANS needs a scenario year. The year set affects population and economy, using projected values from government institutions. Having a scenario year is also necessary to be able to make reasonable choices for the expected performance of driverless vehicles. For the choice of year, an overview of current projects and literature of implementation of driverless vehicle technology is performed. The temporal dimension will be chosen at the time driverless vehicle usage is likely to be widespread.

A popular five-level classification of automation of vehicles is provided by SAE International (SAE International, 2016). A description of the levels with examples to classify can be seen in table 1, taken from Pernestål Brenden and Kottenhof (2018). For the sake of this report, level 4 and 5 vehicles are necessary, as that is the level of automation needed to provide shared transport without a chauffeur.

Level 4 and level 5 is also the levels usually considered necessary to define the vehicle as driverless, which is a more specific term than autonomous.

Table 1: The SAE levels of vehicle automation, as explained in Pernestål Brenden and Kottenhof (2018). Level Description Example 0 No automation - 1 Driver assistance ABS, cruise control 2 Partial automation Lane following, adaptive cruise control 3 Conditional automation Autopilot functions 4 High-level automation Fully self-driving under certain conditions 5 Full automation Fully self-driving any- where

Looking at current progress, there are many test projects for driverless cars which are currently operating. Waymo, the former Google project, has driverless cars with a total of 500 000 miles driven in real-world environments and has started a ride-hailing service (The Guardian, 2017a). Uber, the current biggest ride-hailing service, but with drivers, are ordering 24 000 cars from Volvo from 2019- 2021, which are to be installed with driverless technology (The Guardian, 2017b).

While this indicates a soon adoption, the technology is still facing issues. Uber’s driverless technology still needs human interference once every mile, and Waymo’s once every 5000 miles (The Guardian, 2017a). In March 2018, one of Ubers test vehicles killed a woman in Arizona after failing to detect and brake in dark conditions, something that technology is supposed to be able to do (BBC, 2018). Moreover, snow or rain is an issue for the light-based radar, Lidar, a technology used by both Waymo and Uber. Only just recently have autonomous cars been tested at all in such conditions (Bloomberg, 2017). All in all, no vehicle can truly be argued to be level 4 yet, even if Waymo is getting close.

In terms of industry predictions, major car producers have announced that they will start selling driverless vehicles 2021 (MIT Technology Review, 2016a). However, when MIT Technology Review (2016b) interviewed Princeton and University of California experts, their prediction was that driverless vehicles in 2021 will be very limited in their autonomy.

For Sweden, there are two notable autonomous vehicle projects focusing on the transportation of people, a bus project in Stockholm, which operates at 20km/h and at a predefined route, and a Volvo cars project in Gothenburg, where the cars are only driverless at some predefined routes in the city (Aftonbladet, 2017). Both examples are more limited than the international projects earlier presented as they are performed in very limited areas.

Looking at scientific journals there has been a few different predictions done on implementation rate of private level 4 or 5 vehicles. Litman (2017) makes a rough prediction with experience from earlier vehicle technology implementation data, assuming driverless vehicles to be available at a premium price somewhere in the 2020s, a moderate price in the 2030s, low price in the 2040s and included in most vehicles in the 2050s. Lavasani, Jin and Du (2016) made a mathematic model from earlier technology adoption both in terms of disruptive technology, such as cell phones, as well as from vehicle technology and replacement. The model from Lavasani, Jin and Du (2016), assuming a start of driverless vehicles in 2025, had 1.31% penetration in 2030, 36.03% and 83.60% in 2040. Bansal and Kockelman (2016) had an earlier start date, of 2020, and then used a survey of current willingness to pay for AV technology and forecasted market penetration depending on price reduction of technology and a possible increase in willingness to pay. Depending on the increase in willingness to pay and decrease in prices the market penetration of driverless vehicles was between 10 and 34% for 2030 and between 36 and 75% for 2040 (Bansal and Kockelman, 2016).

While these studies look at the implementation of PDVs, shared driverless vehicles should be available at about the same time as the same technology is required. It is further likely that operators planning to invest in shared fleets will be able to pay premium prices, as the cost reduction from replacing a salaried driver is expected to be significant. However, that cost saving is only available when technology and law allow vehicles to drive completely without supervision. On the other hand, private vehicle owners might pay for automation even when some interference is needed, as it still gives some benefits.

Regarding the legal issue, currently in Sweden, trials are only allowed with a supervisor who is ready to intervene and counts as responsible for the vehicle (Swedish Code of Statues 2017:309). For level 4 or 5 autonomous vehicles, there are many other legal and moral issues that need to be handled, such as who is responsible in a crash caused by a fault in a driverless vehicle and whose safety the vehicle should prioritize. For a Swedish context, Svedberg (2016) goes through the legal and moral hurdles for driverless vehicles. While legal issues are not likely to totally stop implementation in the long run, they could affect both the rate and start of implementation in a country.

While autonomous vehicles are already out on the roads and might soon reach level 4 automation, there is likely to be some years until driverless vehicles are common in Sweden. The predictions from Bansal and Kockelman, (2016) Lavasani, Jin and Du (2016) and Litman, 2016) presented earlier seem to assume niche penetration in the 2020s, while the 2030s is when it is likely to take off at a larger scale. For the case of the scenarios in this thesis 2040 will be used as the model year. In 2040 it is likely that both PDVs and shared driverless vehicles are available for use to the average Swedish citizen, and that the technology is sufficient to rely on these vehicles. The year factor will be used for projected population and economic data in the model, as well as support for additional decisions regarding the driverless vehicles in the transport model.

4.2 Overview of Cities A short overview is done on the difference between the earlier modelled cities and Stockholm. The purpose is to see how well the results from the other models could fit Stockholm County in 2040. When possible, the data will come from the model reports and from prognoses regarding Stockholm. To give a general feeling for the differences that can affect the results of driverless vehicle systems in Stockholm County in 2040 compared to the modelled cities.

Looking at table 2, Stockholm County is not very dense compared to most modelled cities, while Austin is even less dense. The lower density for Stockholm County and Austin is mostly due to the larger model areas which include non-urban areas. For average travel times in table 3, Stockholm has the lowest ratio of travel time between the car and public transport travel times of the cities where data existed. In table 4, Helsinki and Stockholm have a similar share of car use, which should indicate about the same potential to decrease environmental effects, as cars carry the highest environmental burden per passenger. Table 4 also shows that Stockholm County has the same share of public transport use as Lisbon, Helsinki has a lower use of public transport but a higher use of biking and walking. Auckland has the highest car use and the lowest use of public transport.

Table 2: Population and areas modelled in as well as population and projected population for Stockholm (OECD – International Transport Forum, 2017a; 2017b; 2017c; Liu et al., 2017). The population for Stockholm County in 2040 is from the LuTRANS model when using 2040 as scenario year. The furthest official prognosis is Stockholm County Council (2017a), estimates population for Stockholm County 2026 to be 2 600 000. Lisbon (2011) Helsinki Auckland Austin (2020) Stockholm (2012) (2013) County Population 2 300 000 2 800 000 1 100 000 1 300 000 2 300 000 Area 6 524 km2 2 957 km2 770 km2 2 233 km2 13 730 km2 Density 350 950 1 410 580 170 2040 Pop 2 850 000 - - - - 2040 Density 437 - - - -

Table 3: Average travel times in the base scenario for Helsinki and Auckland as well as currently for Stockholm (Stockholm County Council, 2017b; OECD – International Transport Forum, 2017b; 2017c). Stockholm Lisbon Helsinki Auckland Austin Car 26 min - 21 min 18 min - Public 44 - 41 44 - transport Ratio 1.7 - 2 2.5 -

Table 4: Current share of trips performed by car and public transport from the OECD – International Transport Forum reports and in Stockholm (Stockholm County Council, 2016; OECD – International Transport Forum, 2017a; 2017b; 2017c). Stockholm Lisbon Helsinki Auckland Austin Car 41% 46% 41% 86% - Public 32% 32% 27% 4% - transport

For high capacity public transport in table 5, Stockholm has the most extensive and well used high capacity public transport of the three cities. Lisbon, which has the same share of current public transport use, does so in large with buses which are replaced in many of the shared transport scenarios in the models (OECD – International Transport Forum, 2017a).

Table 5: High capacity public transport infrastructure and use in modelled areas and Stockholm County. (Stockholm County Council, 2017b; Carris, 2011; Lisbon Metro, 2011; Helsinki Region Transport, 2013; Auckland Transport, 2014; Capital Metropolitan Transportation Authority, 2017) Stockholm (2016) Metro Commuter train Local trains/tram Stations 100 54 115 Route length 108 km 241 km 120 km Trips per year 360 000 000 90 000 000 50 000 000 Lisbon (2011) Metro Commuter train Tram Stations 52 67 115 Route length 40 km 180 km 26 km Trips per year 182 781 000 88 300 000 20 000 000 Helsinki (2012) Metro Commuter train Tram Stations 17 38 262 Route length 21 km 80 km 38 km Trips per year 62 200 000 47 200 000 57 200 000 Auckland (2013) Commuter train Ferry - Stations 42 21 - Route length 120 km - - Trips per year 10 000 000 5 500 000 - Austin (2016) Commuter train - - Stations 9 - - Route length 51 km - - Trips for one year 820 000 - -

4.3 Transport options The unchanged version of LuTRANS contains five options for travel, car as a driver, car as a passenger, public transport, walking and biking. The changes will be done to the option “car as a driver” and “public transport” respectively. Car as a driver will be changed to give a view of transport mode choice impacts of the added comfort but also added costs of a PDV. Public transport will be changed to accommodate for the different possible systems driverless public transport could use compared to current systems. For

public transport, there will be two separate systems tested SDTs and DB supported public transport. The SDT system and the DB system will be tested in separate scenarios, where they compete with CDCs or PDVs separately.

Table 6 shows a full list of the different variations that will be tested. As CDC and current public transport are scenarios where the values in the model are not changed, there are only three different transport choices where new values are needed. Which is: privately owned driverless vehicles (PDV), driverless buses (DB) and Shared Driverless Taxis (SDT).

Table 6: List of scenarios for testing Private vehicle Public transport Reference CDC Current Scenario CDC+DB CDC DB Scenario PDV+DB PDV DB Scenario CDC+SDT CDC SDT Scenario PDV+SDT PDV SDT Scenario PDV+Current PDV Current

4.3.1 Privately owned driverless vehicle The PDV option is developed to give a fair overview of transport mode choices in the future. Earlier models have typically compared driverless shared transport systems to conventional private vehicle ownership, but it is likely that the choice in the future rather is shared driverless transport or private driverless transport. The model does not accommodate for driving empty vehicles. New possible uses that a PDV can have is therefore not modelled, such as driving several family members independently in different trips or having the PDV perform errands by itself.

Cost Cost for a private vehicle can be divided into two parts, fixed cost and variable cost. Fixed cost is the cost of owning the vehicle, this does not change by the amount the vehicle is driven once owned, and the variable cost is the added cost per kilometre driven. The distinction is important as fixed and variable costs affect ownership of vehicles, but once the vehicle is already bought and available, variable cost is the main influencer for short-term mode choice (Davidson and Spinoulas, 2016). Fixed cost includes value depreciation by ageing, time-dependent maintenance and insurance. Variable costs include depreciation due to mileage, energy costs and maintenance due to usage.

For fixed costs, the main difference with driverless technology is the increased cost of purchase, which will add to the value depreciation. Insurance could also be cheaper if PDVs are safer than CDCs, which PDVs likely must be to become legal. Currently, adding autonomy technology to vehicles can cost up to $100 000 (LA Times, 2018). However, the technology is far from mature, which it is likely to be in 2040. Litman (2017) argues for an increase of €800-€2 500 in annual costs when driverless technology is mature, due to increased depreciation and cost of software maintenance, whereof €400 could be gained back due to lower insurance costs. Bösch et al. (2018) use the value of 20% added purchase cost and therefore depreciation, which gives a similar value for average priced cars. For this report, a fixed cost increase for PDV ownership of €1 250 per year is used.

For variable costs, there are several factors affecting things in different directions. There is increased maintenance due to increased use of technology but also decreased maintenance due to better driving (Bösch et al., 2018). There is increased energy usage for the technology but also decreased energy usage due to more efficient driving (Gawron et al., 2018). Bösch et al. (2018) assume that maintenance cost will stay similar. Gawron et al. (2018) show that energy costs could be both higher and lower depending on how much better the vehicle drives compared to humans. For this study, the assumption is that variable costs stay the same as for CDCs. In the model, that is currently €0.218 per passenger kilometre (pkm).

Value of travel time For the expected VoTT of a transport option in this study comfort of travel, possibility to do other things while travelling and feeling of safety are discussed.

For comfort, it can be assumed that it will be increased as there is no need to accommodate for the possibility to drive the vehicle which should leave space for more focus on comfort. There is also the possibility to do other things during travel. However, driverless cars might induce motion sickness, especially if passengers do other things than pay attention to the road (Diels and Bos, 2016). De Looff et al. (2017) calculated VoTT for PDVs designed as office spaces or for leisure using a stated choice survey in the Netherlands. They found the office interior to give a VoTT of 75% of driving a CDC, and the leisure interior to give a VoTT of 129% of driving a CDC (de Looff et al, 2017). This means that travellers would drive a CDC over travelling with a PDV with leisure interior unless the CDC had more than 29% longer travel time. The leisure interior results are not in line with general transport science knowledge, where the possibility to perform other activities is assumed to reduce VoTT (Ortúzar and Willumsen, 2011).

A reason for the high VoTT in the de Looff (2017) study might be that people today do not believe driverless vehicles to be safe. Fraedrich et al. (2016) showed that Germans thought the safety of driverless vehicles was a top priority, but also did not think driverless vehicles would be safe. This indicates that the current expectation is that driverless vehicles will be less safe compared to driving an own vehicle. On the other hand, one of the main perceived benefits of driverless vehicles is that they could increase safety by reducing the possibility of accidents from human error which is responsible for 90% of crashes (Fagnant and Kockelman, 2015). As studies of VoTT of PDVs can currently only be performed with stated choice surveys, the studies also risk being affected by hypothetical bias (Fifer, Rose and Greaves, 2014). As this study uses 2040 as scenario year, it is assumed that driverless vehicles are accepted and that feeling of safety is comparable to driving your own car today.

Concas and Kolpakov (2009) recommend a value of in-vehicle time to 25-35% of hourly salary for seated public transport usage and 50% of hourly salary for driving your own vehicle. Consequently, seated public transport have 50%-70% VoTT compared to driving your own vehicle. The LuTRANS value for work trips with public transport is 60% of driving your own vehicle which is in line with Concas and Kolpakovs (2009) recommendation. A reasonable assumption is that PDVs should be more comfortable than public transport, due to factors such as increased privacy, possibility to design for higher comfort and the possibility to leave belongings behind in your vehicle. For this study the assumed VoTT will be 50% compared to driving a CDC.

As the real VoTT of PDVs is hard to know at this stage, a sensitivity analysis of the VoTT is also conducted. This sensitivity analysis adds 40% and 60% scenarios to the current 50% scenarios for VoTT compared to CDC. 60% seems like a reasonable maximum value it could take, as that is the same as public transport in the model, and 40% is if in fact the privacy and ownership of PDVs make the VoTT much lower than public transport of today.

Travel time In this study, it is assumed that driverless vehicles are developed enough in 2040 to not have to drive slower than CDCs. There are several studies which have predicted road capacity increases due to autonomous and connected vehicles, but large effects are only seen as penetration of autonomous vehicles are high (Milakis, van Arem and van Wee, 2017). In 2040 it is still expected to be a mix of conventionally driven vehicles and driverless vehicles (see 4.1 Scenario year), therefore no assumption on increased capacity or speed of travel is done. Walking time is assumed to be the same as for current CDCs, while waiting time is assumed to be non-existent for private vehicles.

Parking Parking affects only private vehicles in the model but not shared transport. In the model, there are two aspects of parking. One of the aspects is the price of parking, and the other is the availability of parking.

For the availability, a number is given for how many cars can park in Stockholm City and therefore how many private vehicles can be used to travel to Stockholm. In this study, the maximum capacity is removed for PDVs, as it is assumed that finding a parking to be able to use a private vehicle will no longer be an issue. If no parking is found close by the vehicle will drive itself to a parking space further away. In the case of the parking price, it is not changed from the model. This as the alternative to parking in the city includes extra driving which has a cost. Decisions about parking prices are also political and therefore hard to predict.

Driving permission Troung et al. (2017) found that PDVs will likely increase trips for people at ages 13-30 and above 67. This as those below 30 is less likely to have a driver’s license and those above 67 due to disabilities that prevents driving (Troung et al., 2017). In LuTRANS a driver’s license model is used before mode choice to make the choice of car possible. In this scenario, the probability of having a driver’s license is changed to 100% for everyone over 18. However, no further change is done to the model to accommodate the increased availability in age groups 13-17 and 67 and above.

4.3.2 Shared Driverless Taxis The SDTs are modelled of values from reports on driverless vehicle fleets which cooperate to satisfy travel demand, and where one vehicle can pick up several independent passengers when their trips align to reduce the number of vehicles needed per passenger trip completed. Several such reports exist, a more in-depth overview of one of the efforts can be read in 3.1.

Incomplete market penetration of the SDTs will be the starting point, and values will be taken from models that have taken this into account. That means, for example, that values from the OECD – International Transport Forum (2017a; 2017b; 2017c) scenarios where 20% of cars are replaced will be used rather than 100% car replacement.

In the scenarios with SDTs, no other shared transport is available to choose from. This is due to model constraints, where adding more transport options than currently existing would need extensive model development.

Cost The most common way to calculate the cost is the cost per pkm for the passenger. In table 7 an overview of the different results is shown. For the OECD – International Transport Forum (2017b) Helsinki report, the results from scenarios further analysed in the report is presented. The prices are much higher than the other presented here, but OECD – International Transport Forum (2017b) calculates with a salaried driver and no included cost for driverless technology (OECD – International Transport Forum, 2017b). Loeb and Kockelman (2018) made a cost calculation for SDTs in Austin using hybrid or electric vehicles. Another calculation by Bösch et al. (2018) for Zurich, with different costs for regional and urban SDTs. Pernestål Brenden and Kristoffersson (2018) has made an overview of different cost estimations for SDTs but also argue that many of the cited articles are missing the cost of empty kilometres and cleaning, hence underestimating the final price. Bösch et al. (2018) are one of the studies that include both empty kilometres and cleaning.

Table 7: SDT price per passenger kilometre. Type of study Price OECD – International Transport Forum Model €0.69/pkm (2017b), Scenario 4 OECD – International Transport Forum Model €0.65/pkm (2017b), Scenario 5 OECD – International Transport Forum Model €0.79/pkm (2017b), Scenario 9 Loeb and Kockelman (2018) Model Hybrid: €0.23/pkm Electric: €0.30/pkm Bösch et al. (2018) Model Urban: €0.37/pkm Regional: €0.29/pkm Pernestål Brenden and Kristoffersson (2018) Meta study High: €0.39/pkm Average: €0.23/pkm Low: €0.13/pkm

The OECD – International Transport Forum (2017b) scenarios included in table 7 are the scenarios the report analysed further. Scenario 4, kept all current public transport in Helsinki, but all cars were replaced inside the city ring road. Scenario 5, replaced 100% of current buses and cars with the new shared transport system. Scenario 9, replaced buses that were used as feeder service to high capacity current transport and 20% of the car trips.

The SDT cost calculations are sensitive to assumptions. One of the main sensitivities is the average occupancy, as the cost per pkm is the cost per vehicle kilometre divided by the average occupancy. Average occupancy is a complex issue affected by SDT fleet size and usage rate as well as fleet relocation strategies, but also by city layout and population density. Bösch et al. (2018) use average occupancy from the OECD – International Transport Forum (2015) report on Lisbon, 2.6 for peak, 2.4 for off-peak, and 2.3 for nights. However, the OECD – International Transport Forum (2015) report on Lisbon only evaluates an urban area, and the numbers are from a system with 100% replacement of current cars and buses. Comparatively, scenarios in OECD – International Transport Forums (2017b) Helsinki report with 20% car replacement has occupancies between 2.03 and 2.18 for SDTs. A 15% decrease in occupancy in Bösch et al. would lead to prices of €0.44/pkm for the urban setting and €0.34/pkm for the regional setting. Another issue is regional pricing, for example, the hybrid vehicles in Loeb and Kockelman (2018) would cost €0.27/pkm only changing the price of gas from Austin prices to Swedish prices.

A value of €0.35/pkm will be used in this report. This is assuming that higher price estimates of SDTs are more realistic, but that no driver salary is needed. An extra scenario will be performed of PDV vs SDT with the SDT cost set to €0.23/pkm, to see the difference if the average from Pernestål Brenden and Krisoffersson (2018) is correct.

Value of travel time Again, VoTT is divided into the comfort of the vehicle, the possibility to perform other activities during travel and safety of the travel. For the comfort of the vehicle, Litman (2017) argues shared driverless vehicles will likely have the interior comfort like public transport, for ease of cleaning. However, in an SDT the rider is guaranteed a seat, which guarantees possibility for other activities, which should be an increase compared to current public transport. Motion sickness can, similarly to the PDV case, be a problem.

For safety, the vehicle safety will be like PDVs. However, there is also the added unsafety of discomfort of other passengers. In an SDT, unless designed with individual spaces, it will be harder to avoid someone that is unpleasant or threatening compared to in a bus or train, where there is more space. Moreover, there is not likely to be that many or any other passengers in the SDT which might add to the discomfort. This could possibly be solved by design, for example by giving SDT passengers individual

travel compartments. The safety issue could also be eased by cameras and identification upon booking the vehicle even if that would reduce passenger privacy.

In this study, the assumed VoTT of SDTs will be the current VoTT for public transport in LuTRANS, as there are reasons to believe both that it could be more and less comfortable. This value is equal to 60% of the VoTT of driving a CDC.

Travel time For shared vehicles and public transport, there are three values for travel time: in-vehicle time, waiting time and walking time. As SDTs goes from the users decided origin to the users decided destination, walking time is assumed to be very low. For SDTs, one minute is used, which is the time it takes walking from your apartment to the road and from the road to the destination. For in-vehicle time and waiting time, data from other reports are used.

For the in-vehicle time, many earlier performed models have included a maximum allowed additional in-vehicle time for SDTs compared to PDVs. The OECD – International Transport Forum (2017a; 2017b; 2017c) has a maximum of total detour time compared to a car depending on the length of the trip, starting on 7 minutes for trips at 3 km or shorter and going to 15 minutes for trips 13 km and longer. Loeb and Kockelman (2018) use 20% as a maximum for delays. In this report, 20% will be used as the increase to in-vehicle time for picking-up and dropping-off additional passengers. Average increased in- vehicle time compared to CDCs might have been a better number to use, but it has not been included in the results of earlier studied models.

Average waiting time differs in different reports. Table 8 presents the studies looked at in this report. In Lisbon, OECD – International Transport Forum (2017a) gives average waiting time for SDTs in different parts of the model area. The Auckland report has much lower waiting times, with 75% waiting less than 3 minutes in all further analysed scenarios (OECD – International Transport Forum, 2017c). For the OECD – International Transport Forum (2017b) Helsinki report, no separate SDT waiting times were presented. The Waiting time presented in the Loeb and Kockelman (2018) report is with hybrid vehicles, which had the lowest waiting time. The meta-study by Pernestål Brenden and Kristoffersson (2018) gives 3-6 minutes as the most common results from modelling but also points out that many models have unserved trips which might not be acceptable for an SDT service. Another noticeable result that exists in models of SDTs is that waiting times are higher at peak times (Zhang et al., 2015; Bischoff and Maciejewski, 2016). In this study, an average of 5 minutes will be used for work trips, while 3 minutes will be used for other trips.

Table 8: SDT waiting times from different studies. Type of study Waiting time OECD – International Transport Forum Model City: 2.46 minutes (2017a) Other: 4.43-5.14 minutes OECD – International Transport Forum Model <3 minutes for 75 % of (2017c) travellers Loeb and Kockelman (2018) Model 4.45 minutes Pernestål Brenden and Kristoffersson (2018) Meta study High: 10 minutes Average: 3-6 minutes Low: 1 minute

4.3.3 Driverless buses DBs are assumed to be used in a system similar to the OECD – International Transport Forum (2017a; 2017b; 2017c) system, where passengers can request DBs. It is assumed that DBs are a part of the public transport system and paid as such. For LuTRANS, this means that those travelling to work with DBs will buy a monthly card and thereon forward use public transport without any added cost for both work trips and other trips. While those who use other transport options to commute to work will pay singular tickets for other trips by public transport. For the scenario, it is further assumed that the price, which

includes other public transport modes, won’t change. Instead, if lower cost of operations is possible, it is assumed that savings will be invested in an increased quality of service of public transport.

Incomplete market penetration of the DBs will be assumed. DBs are expected to be a part of and support current high capacity public transport. Which makes the scenario different from the SDT scenario which is thought of as an independent system.

Cost In the Helsinki scenarios, the buses have a cheaper price per pkm than current buses, even though it is calculated with a salaried driver (OECD – International Transport Forum, 2017b). In Sweden, the driver is usually 50% of the cost of operating a bus in public transport (Eriksson et al., 2017). In Bösch et al. (2018) driverless technology for buses is assumed to add a negligible amount of extra cost. This as buses are already expensive per unit, making the added cost of driverless technology a small share of the total cost of purchase. Therefore, the assumption in this study is that with an OECD – International Transport Forum (2017a; 2017b; 2017c) system for buses that are driverless, the price per pkm will be significantly lower than the current bus operating costs. In Pernestål Brenden and Kottenhof (2018), it is assumed that two smaller shuttles would be able to operate for about the same price as one larger bus of today. No change of the cost of public transport for the passenger assumed in the model, as it is assumed that savings will be invested in better public transport.

Value of travel time VoTT is again divided into vehicle comfort, safety and possibility to perform other activities. The DBs are assumed to have the same interior comfort as current public transport. Like SDT travel passengers could feel unsafe due to driverless technology, but buses are safer if they collide, which could increase the feeling of safety compared to SDTs. Like for SDTs, unpleasant passengers can be a problem as there is no driver or otherwise salaried staff, but it should be less of an issue in a buss where there are likely several passengers. Putting things together there are arguments for a slight change in VoTT, but they are not strong or proven, therefore the current value for the in-vehicle time of public transport will be used.

Travel time Travel time should be divided into in-vehicle time, waiting time and walking time. For average improvements of travel time, the numbers are unreliable as the results from the OECD – International Transport Forum (2017a; 2017b; 2017c) reports is from both SDT and DB use compared to current public transport. In general, Auckland shows great improvements in shared transport travel time while the Helsinki improvements are more moderate (OECD International Transport Forum, 2017b; 2017c). A likely reason for the larger improvements in Auckland is the current lack of good public transport, while Stockholm and Helsinki both have rather well developed public transport systems today. For the model in this report the in-vehicle travel time for buses will be assumed to be similar of today, this as the driverless vehicles will not necessarily increase speed driven nor make fewer stops. Instead, improvements are rather assumed to be regarding waiting and walking time.

For waiting times in the OECD – International Transport Forum (2017b; 2017c) reports, most scenarios in Auckland show reductions of about 50%, while the further analysed scenarios in Helsinki has reductions of 10% to 35%. The Helsinki scenarios are more likely to be comparable with Stockholm as Stockholm and Helsinki presently have well-developed public transport. However, for this report, it is assumed that cost reductions of the system are invested into increased availability, which is not true in the OECD – International Transport Forum (2017a; 2017b; 2017c) scenarios. Pernestål Brenden and Kottenhof (2018) showed that for similar costs, two DBs could have 15 departures per hour instead of the 4 departures a large bus with a driver performs. This is equal to a 73% reduction in waiting time, which for public transport is based on trip frequency. However, the Pernestål Brenden and Kottenhof (2018) scenario operate on an existing route with the same stops, while the OECD – International

Transport Forum (2017a; 2017b; 2017c) scenarios have on-demand stops, which could have an impact on waiting times.

For the sake of this report, a 50% reduction in waiting time will be assumed for work trips. This as if the savings of DBs was invested in additional vehicles instead of system cost reductions, it is assumed that the waiting time reductions would be larger than in the OECD – International Transport Forum (2017a; 2017b; 2017c) reports. For other trips, the waiting time is reduced further to 25% of the original value. This because the other trips have higher waiting times in the model originally, as they are expected to happen off-peak hours. Furthermore, because with no driver the variable cost of running buses is lower, which means that it makes more sense to run a higher percentage of all buses at all times. Lastly, as this report assumes that buses can be demanded, and it is likely that the waiting times for off-peak trips are lower than peak trips due to lower demand, like for SDTs.

The DBs will always stop within 400 meters from passengers in the OECD – International Transport Forum (2017a; 2017b; 2017c) scenarios. It is assumed that there is a similar walk from the drop off place, giving a total of 800 metres walked as the maximum. The walking speed set in LuTRANS is 6km/h, which gives a maximum walking time of 8 minutes. A maximum of 4 minutes’ walking time is also tested in an additional scenario, as this could be the average walking needed.

4.4 Overview of choices In table 9, an overview of the choices done for the model is found. Due to some uncertainties, a few additional choices have will be tested by only modifying that value, and only in the fully driverless scenarios. Moreover, a full sensitivity analysis is performed of the VoTT for PDVs separately to the other results. Choices that affect all scenarios is that the model year is set to 2040 which affects population and household economy.

Table 9: Assumed values for the LuTRANS model. PDV DB SDT Fixed cost €1 250/year increase Same as PT No fixed cost Variable cost Same as CDC Same as PT €0.35/pkm VoTT 50% of CDC Same as PT Same as PT In-vehicle time Same as CDC Same as PT 20% increase from PCV Waiting time - 50% PT for work trips, 5 min for work trips, 25% PT for other trips 3 min for other trips Walking time Same as CDC Average of 8 minutes 1 minute Parking availability No limit - - Parking price Same as CDC - - Driving license Everyone above 18

4.5 Implementation in LuTRANS Here follows an overview of how the changes in values were implemented in LuTRANS. This as some implemented changes where approximations due to constraints in the model

4.5.1 Private Driverless Vehicles The VoTT is changed by multiplying current private car VoTT in the model with 0.5. The parking changes are completed by removing the limit on parking, in the original private car scenario max 60 000 CDCs can park in the inner city of Stockholm during work trips and 80 000 CDCs for other trips. The increased expected price of PDVs over CDCs is done by reducing the yearly income of households in the car ownership model. The yearly income was reduced by 11 000 SEK per year, in 2005 years value for SEK, which is equal to about €1 250 in 2018. This is done as the yearly average household-income is what is used to calculate car ownership, there is no input for car prices. The change in yearly income does not affect other parts of the model as it is done separately in the car ownership model.

4.5.2 Shared Driverless Taxis The modelling was done by changing public transport utility parameters in LuTRANS. The travel time for the utility cost of SDTs was set to travel time for cars of the same distance times 1.2. The monetary cost was set to the travel distance for cars in kilometres times 3 SEK, which is about €0.35 in 2005 years value of SEK. The walking time was set to 1 minute and waiting time 5 minutes for work trips and 3 minutes for other trips. As changes were done to the utility calculation and not the actual trips taken in the transport model, reliable data on VKT for SDTs are not available.

4.5.3 Driverless buses As in the case of SDTs, the modelling was done by changing public transport utility parameters in LuTRANS. In the model, the utility loss of waiting is changed by multiplying the current values of waiting times for work trips with 0.5 and for other trips with 0.25. The walking time is done by setting the utility loss of walking to a maximum of what is equal to 8 minutes of walk in the mode choice part of the model. This means that those who normally would have to walk more than 8 minutes act in their choice of transport as if they had 8 minutes to walk exactly. As changes were done to the utility calculation and not the actual trips taken in the transport model, reliable data on VKT for DBs are not available.

5. Results

5.1 Scenario results Scenarios tested can be seen in Table 10. Compared to the initial list of scenarios, PDV+DB SW (short walk) and PDV+SDT LP (low price) are added to test some additional possible values for a specific input. PDV+DB SW halves walking time to 4 minutes, from 8, while PDV+SDT LP lowers the price of SDTs from €0.35/pkm to €0.23/pkm.

Table 10: Final scenarios modelled in LuTRANS. Scenario Private vehicle Public transport Reference Conventional Current CDC+DB Conventional DB PDV+DB Driverless DB PDV+DB SW Driverless DB, 4 minutes walking time CDC+SDT Conventional SDT PDV+SDT Driverless SDT PDV+SDT LP Driverless SDT, €0.23/pkm. PDV+Current Driverless Current

In figure 1 it is shown how many households that have access to private cars (CDCs or PDVs) in different scenarios. In CDC+DB, CDC+SDT and PDV+Current, only one of the travel options has been changed compared to the reference scenario. In CDC+DB it is shown that DBs slightly decreases car ownership, while CDC+SDT show that SDTs if replacing public transport slightly increases car ownership, PDV+Current show that PDVs also increase car ownership slightly. PDV+DB show that PDVs increase car ownership even with DBs, and scenario PDV+DB SW that this is true even if walking time is reduced to 4 minutes. Scenario PDV+SDT show that PDVs and SDTs in parallel further increase car ownership, and scenario PDV+SDT LP shows that lower-priced SDTs slightly reduce car ownership.

82,4%

81,9%

80,9%

80,6%

80,1%

80,0%

79,4% 78,7%

Figure 1: Car ownership

In figure 2 the share of trips taken by different types of transport is presented. CDC+DB shows that DBs increase share of public transport trips, while CDC+SDT shows that SDTs decrease it. PDV+Current show that PDVs increases trip share of cars. PDV+DB and PDV+DB SW show that PDVs improve private travel enough to make the share of public transport lower even with DBs compared to the reference. CDC+DB and PDV+Current show that DBs respectively PDVs take shares from walking and biking compared to the reference, and PDV+DB show that in combination the non-motorized trips are reduced even more. However, CDC+SDT shows that SDTs increase use of biking and walking, when no other public transport option is available. An exception to the introduction of PDVs reducing biking exists in comparing CDC+SDT to PDV+SDT, where the latter scenario has 0.4% more biking, but it also has 0.5% less walking.

Car Car passenger Shared Walk Bike

6,2% 5,6% 4,8% 4,7% 8,4% 8,8% 7,8% 5,2% 9,3% 7,9% 7,7% 8,5% 10,1% 12,4% 11,9% 11,0%

37,9% 38,7% 17,7% 22,1% 33,7% 40,5% 45,3% 28,1% 3,8% 3,7% 3,3% 3,3% 3,9% 3,4% 3,4% 3,1% 57,9% 55,4% 46,2% 45,7% 47,1% 49,1% 39,8% 36,7%

Figure 2: Share of trips by each transport option for work trips.

In figure 3 changes in private cars (PDVs and CDCs) trips are presented. The number of total trips in all modes is mostly unchanged through the different scenarios. However, the total VKT by private car increases compared to the reference in all scenarios with PDVs. This is a combination of increased trips

in cars due to the increased share of total trips that PDVs take (which was seen in figure 2) and due to the increased average car trip length, that can be seen in figure 3.

Total trips Private car trips Private car average trip length Private car VKT

162%

157%

146%

140%

139%

125%

119%

118%

117%

112%

111%

102%

101%

100% 100% 100%

100%

100% 100% 100% 100%

99%

93% 93%

Figure 3: Travel statistics for work trips.

In figure 4 the share of trips by each transport option in other trips is presented. What can be seen is that shared driverless modes increases in all but PDV+SDT compared to the reference scenario. Both DBs and SDTs increase percentage of shared transport in scenarios with CDCs. As for work trips, PDVs increase the percentage share of private car trips in PDV+Current. However, unlike for work trips, the PDV+DB scenario has a lower percentage of private car trips than the reference. In comparing CDC+DB with PDV+DB and the reference scenario with PDV+Current it can also be seen that introduction of PDVs can increase both private car trips and shared transport trips, by reducing non-motorized trips. Comparing PDV+SDT and PDV+SDT LP show that lower price increases SDT use substantially for other trips, taking shares mainly from walking but also from PDV use. It can further be seen that all modelled driverless transport options take shares from non-motorized trips, as the reference scenario has the highest amount of non-motorized trips.

Car Car passenger Shared Walk Bike

4,3% 3,8% 3,6% 3,5% 4,1% 3,9% 3,6% 4,0%

33,0% 32,0% 39,4% 35,2% 37,7% 36,0% 33,7% 37,0%

19,7% 25,4% 20,1% 29,0% 29,5% 31,5% 21,2% 20,3% 9,7% 9,6% 9,4% 9,1% 9,7% 8,5% 8,6% 8,4% 30,7% 26,6% 23,4% 25,3% 24,6% 27,6% 28,2% 29,0%

Figure 4: Share of trips by each transport option for other trips.

In terms of VKT by private car, figure 5 gives the results for other trips. Like figure 3 the changes in total trips are few. However, PDVs do not increase the trip length of other trips as much as for work trips. This together with that PDVs do not increase the share of private transport as much in other trips as in work trips means that the total increase in VKT due to PDVs are less for other trips. For PDV+DB and PDV+DB SW, there is almost no change in private transport VKT compared to the reference.

Total trips Private car trips Private car average trip length Private car VKT

124%

116%

115%

109%

107% 107%

107%

106%

105%

104%

101%

100%

100% 100% 100% 100%

99%

96%

94%

89% 88%

Figure 5: Travel statistics for other trips.

Figure 6 and 7 are included to give an extra analysis of SDTs, as this is a completely new transport method for the model. Both figures show the percentage of trips done by different transport options at different trip lengths. Figure 6 shows the reference scenario and is included to have something to compare figure 7 with, which shows the CDC+SDT scenario. In figure 6 public transport has a noticeable amount of share of all transport lengths, while biking and walking trips are non-existent for longer trips. Comparing figure 6 and 7, SDTs are more competitive than current public transport for shorter trips, mainly by taking shares from walking. However, SDTs have a smaller share for trips longer than 7.5 km and fall off almost completely for the very long trips.

Car Car passenger Shared Walk Bike

50%

49%

46%

43%

41%

40%

26%

16%

12%

0% 0% 0% 0% 0%

0 - 7 . 5 K M 7 . 5 - 1 5 K M 15- 2 5 K M 2 5 + K M

Figure 6: Share of trips at different length intervals travelled with each transport option in the Reference scenario.

Car Car passenger Shared Walk Bike

82%

62%

47%

41%

37%

25%

22%

16%

11%

0% 0% 0% 0% 0%

0 - 7 . 5 K M 7 . 5 - 1 5 K M 15- 2 5 K M 2 5 + K M

Figure 7: Share of trips at different length intervals travelled with each transport option in the CDC+SDT scenario.

5.2 Sensitivity analysis – VoTT of Privately owned driverless vehicles In Table 11 an overview of all sensitivity analysis scenarios performed is shown. The reference scenario is also included in the sensitivity analysis figures. The scenarios with PDV 50% is equivalent to scenarios in 5.1, as 50% was the chosen VoTT for PDVs for the original scenarios.

Table 11: Sensitivity analysis scenarios performed Scenario: Private vehicle Public transport Reference Conventional Current PDV 40%+DB PDV 40% DB PDV 50%+DB PDV 50% DB PDV 60%+DB PDV 60% DB PDV 40%+SDT PDV 40% SDT PDV 50%+SDT PDV 50% SDT PDV 60%+SDT PDV 60% SDT PDV 40%+Current PDV 40% Current PDV 50%+Current PDV 50% Current PDV 60%+Current PDV 60% Current

In figure 8 car ownership depending on VoTT of PDVs is shown. It follows that car ownership is higher when the perceived comfort of private cars is higher, but the change is not linear. In the PDV+SDT scenarios, the largest difference is created by changing the VoTT, which is 2%. Car ownership is higher than the reference scenario in all scenarios but PDV 60%+DB, where it is reduced by 0.8%.

82,8%

82,4%

80,9%

80,8%

80,6%

80,4%

80,1%

79,4% 78,6%

Figure 8: Car ownership in different scenarios in the sensitivity analysis.

Figure 9 shows similar results as in figure 8, in that where car ownership increases in figure 8, the share of trips by car increases in figure 9. The percentage changes in the share of trips shown in figure 9 are however larger than the changes in figure 8. Car usage goes down when perceived VoTT is lowered, and the change is not linear. Especially or the DB and SDT scenarios going from a PDV VoTT of 50% to 60% creates a larger change in private transport use than when the PDV VoTT changes from 40% to 50%. Only the PDV 60%+DB scenario show a lower use of private transport and higher use of shared transport than the reference scenario.

Car Car passenger Shared Walk Bike

6,2% 4,6% 4,8% 5,5% 8,9% 8,8% 8,5% 5,0% 5,2% 5,4% 7,6% 7,9% 9,1% 8,2% 8,5% 8,8% 10,1% 11,8% 11,9% 12,4% 15,4% 17,7% 32,5% 36,6% 37,9% 26,0% 33,7% 34,8% 40,5% 44,2% 3,9% 3,8% 3,5% 3,3% 3,3% 3,5% 3,4% 3,4% 3,4% 3,0% 60,0% 57,9% 47,8% 49,6% 50,7% 49,1% 47,6% 39,8% 46,2% 38,3%

Figure 9: Share of trips by each transport option for work trips in the sensitivity analysis.

In figure 10 travel statistics for the sensitivity analyses can be seen. What can be seen is that increased VoTT of PDVs slightly increases total trips, but not by a lot. However, it does affect the number of private car trips more, which could also be seen in figure 9 by the increased share private transport takes with lower VoTT. Moreover, increased VoTT increases average trip length. Added together, VoTT has a large effect on total travelled kilometres by private car, which is seen in all three sensitivity scenarios. Again PDV 60%+DB stands out, this time being the only scenario with less travelled kilometres than the reference scenario.

Total trips Private car trips Private car average trip length Private car VKT

173%

162%

157%

152%

151%

146%

140%

136%

130%

125%

123%

121% 121%

121%

119%

118%

117%

114%

113%

111%

102%

101%

100%

100% 100% 100% 100%

99%

98%

97% 96%

Figure 10: Travel statistics for work trips in the sensitivity analysis.

In figure 11 the same statistics are shown as in figure 9, but for other trips. The results in figure 11 have the same directions as the results in figure 9 due to VoTT changes. However, the large change going from VoTT 50% to VoTT 60% in the DB and SDT scenarios seen in figure 9 does not display for other trips in figure 11. Instead, the percentage of private trips seem to change more linearly for other trips. In reverse to the work trips, most other trips scenarios have increased use of shared transport compared to the reference, only PDV 40%+SDT, PDV 50%+SDT and PDV 40%+Current have a lower percentage of shared transport trips. However, most scenarios still have an increase in private car usage, with only PDV 50%+DB and PDV 60%+DB having a reduction in private car trips. This is possible as all scenarios reduce non-motorized trips compared to the reference.

Car Car passenger Shared Walk Bike

4,3% 3,5% 3,6% 3,7% 3,8% 3,9% 4,1% 3,9% 4,0% 4,1%

32,2% 39,4% 33,0% 34,1% 35,0% 36,0% 37,5% 36,0% 37,0% 38,0%

18,9% 20,1% 28,6% 29,5% 30,9% 19,7% 21,0% 19,7% 20,3% 20,9% 10,1% 9,6% 9,7% 9,3% 10,1% 9,7% 9,3% 9,1% 8,6% 8,2% 32,3% 26,6% 26,7% 25,3% 23,1% 30,7% 28,2% 30,3% 29,0% 27,7%

Figure 11: Share of trips by each transport option for other trips in the sensitivity analysis.

In figure 12, the travel statistics for other trips are displayed. Like in figure 10, it is only in PDV 60%+DB that the VKT of private transport is less than the reference scenario. However, the increase is smaller for other trips in figure 12 than work trips in figure 10. In sensitivity PDV 50%+DB and PDV 60%+Current, the increase is marginal. For PDV 40%+DB the increase in private car VKT is almost only due to increased trip length, while other scenarios with an increase in VKT are mostly due to a combination of

increased private car trips and trip length. Lower PDV VoTT also slightly increases total other trips, but not substantially.

Total trips Private car trips Private car average trip length Private car VKT

140%

132%

124%

122%

116%

115%

114%

109%

107%

106%

105%

104%

103%

102%

101%

101% 101%

101%

100%

100% 100% 100% 100%

99%

96%

88% 87%

Figure 12: Travel statistics for other trips in the sensitivity analysis

6. Discussion

6.1 Competitiveness of different transport options The first thing to discuss is what the results from the model indicate for 2040. Driverless technology should in theory only provide new modes of transport that increase the overall utility of transport for travellers, or the mode of transport is not likely to be implemented. The scenarios with DBs show a higher share of shared transport than the scenarios with current public transport or SDTs. Scenarios with PDVs show a higher share of private transport than scenarios with CDCs. Therefore, it could be said that the scenarios with DBs and PDVs show the scenarios with the most improved utility of shared respectively private transport modes evaluated in this report. With this in mind, scenarios with DBs and PDVs are most likely to be similar to the real world in 2040 of the scenarios in this report, in terms of the competition between shared and private transport.

The results in DB and PDV scenarios indicate that shared transport will not take over the market only by introducing driverless technology, private vehicles seem to still be very popular. Moreover, PDVs seemingly increase the utility of owning a vehicle more than driverless technology can improve the utility of shared transport. Another indication from the results is that shared driverless transport is likely to have a larger impact on off-peak trips. This is a logical result in the sense that driverless shared transport will have a much lower per trip cost of already existing vehicles, with no driver salary being paid. This means that while today many vehicles are unused during non-peak-hours, more shared transport vehicles should be able to be active at low-demand times with driverless technology.

6.2 Car ownership One of the surprising results might be the relatively small changes in car ownership in all scenarios tested. The results indicate that changes to trip utility of owning a vehicle and not owning a vehicle due to driverless technology will not make a large difference for car ownership. At least unless the utility changes to are much larger than expected in this report. A reason for the small changes is likely that local population density and household income are two major factors for determining car ownership. For population density, no change was done between different scenarios. For household income, they were changed in PDV scenarios as a proxy for the increased price of PDVs. However, most households had an income which indicated a high chance of car ownership even with the reduction due to PDVs.

This as Sweden is a high-income country and the model included expected increases in household income to 2040.

6.3 Sources of error The model does not allow a comparison where all choices are possible at the same time. In the real world in 2040, people are likely to be able to own CDCs, PDVs and use an improved shared transport with DBs and SDTs. Introducing all these options in a model would change the results. In a model where both CDC and PDV were available, it would likely create a small increase in car ownership and use. This would be mainly those who cannot afford PDVs but still have a high utility of a private vehicle. However, being able to choose between SDTs and DBs should increase the total use of shared transport noticeably, as arguably SDTs and DBs are distinct choices that can be utilized for different trips or different travellers. This should also reduce the VKT by private vehicles, as a part of the increase in VKT with PDVs is due to an increase in the share of motorized trips.

Another uncertainty is that most of the changes in the model are qualified guesses with support from literature. Driverless vehicles do not exist today and therefore no real data exists yet. The VoTT of PDVs is one of the things that both effects the results and is very hard to predict. The sensitivity analysis shows that depending on how comfortable people perceive PDVs, the transport system of tomorrow might look very different. The PDV 40-50-60%+DB scenarios show that depending on the perceived comfort of PDVs, the length driven by private vehicles can either go slightly down or increase by as much as 50% for work trips.

Another source of error is that there are no attitude or policy changes included in the model. It is likely that there will be changed policies and changed attitudes regarding transport in Stockholm until 2040. It is also worth to note that the attitudes displayed in the model are attitudes calibrated from 2005-2006 travel data, as that is when the travel survey behind the calibration of the model was performed. However, the results still show that driverless technology is not likely to be a catalyst that radically changes the transport system from privately owned vehicles to shared vehicles.

Regarding SDT, the attitude part could make a larger difference than for DB. As the SDTs replace public transport in the model, the calibrations which are done regarding attitudes to public transport in 2005- 2006 in Sweden is also used for the SDTs. It is very hard to say exactly how SDTs will be perceived and therefore the calibration used is a source of error.

6.4 Induced travel In terms of induced travel and increased travel due to better availability of transport, this report shows some effects but not all. For PDVs, everyone above 18 is set to have a driver’s license, this does not capture all increased travel due to not needing to operate the PDV done by those below 18 or those who for other reasons today do not drive CDCs. The PDV+DB scenarios are those with the largest share of motorized transport. The scenarios show that with decreased utility costs of motorized transport, be it shared or by car, the number of motorized trips increase. Partially due to increased number of total trips, but mostly due to taking trips from biking and walking. A future system will likely have SDTs as an additional motorized option compared to the PDV+DB scenario, increasing utility of motorized transport even more.

6.5 About Shared Driverless Taxis The SDT results are worth extra focus, as there is much discussion regarding their potential effects. The SDTs in this report have a lower use than current public transport in the reference scenario, but they still perform a significant number of trips. In comparison, regular taxis are not included in the original model as they are not considered to have enough share of the transport to have an impact on the travel system at large in Stockholm. If a scenario with SDTs and public transport (with or without DB) existed as shared transport choices, the share of SDTs would likely be substantially lower than the results in the

scenarios presented in this report. However, even then they should have a usage large enough to have an impact on the travel system.

What can be seen in figure 6 and 7 is that SDTs take a larger share of short trips than current public transport, but almost no shares of long trips. This makes sense in that SDTs have a high cost for long trips. Moreover, the 20% longer travel time than cars means that SDTs might take longer time than conventional public transport for long trips, where trains or long trip buses can be fast. In a real scenario, the increased allowed trip length for customers due to other customers could be decided by the operator. As such the operator could decide to let the increase in maximum detour time have a limit at a certain point, as it is set up in the OECD – International Transport Forum (2017a; 2017b; 2017c) reports. For the monetary part, an SDT operator might decide to have part of the price in an access fee, which was included in the Liu et al. (2017) model and is how taxi companies operate today. This would better represent the real cost of driving to the user in the start and would increase the utility of long trips while decreasing utility of short trips.

Overall, the performance of SDTs in PDV+SDT scenarios might be a disappointment. However, looking at the input data it is not that surprising, SDTs costs more per kilometre, takes longer time, and are less comfortable than PDVs (except in PDV 60%+SDT when PDV and SDT are modelled to have the same VoTT). The downside of PDVs is the high fixed costs while the SDTs have no fixed cost, but the results indicate that this downside is not large enough to make people not own a PDV and use the SDTs.

6.6 Aligning with other studies To see the robustness of the results the scenarios can be compared to other studies done with similar scenarios. Scenario 3 in Childress et al. (2015) with a 19.7% increase in driven km is comparable to PDV+Current in this report. In PDV+Current the driven km of private vehicles is 45.3% higher than the reference scenario. However, in Childress et al. (2015) most trips are already performed by private vehicles in the reference scenario. While in this report, the PDV+Current scenario shows an increase in the share of trips done by private transport and thus the amount of PDV trips. Looking instead at the increased average length of private car trips going from PDV to CDC, Childress et al. (2015) have a 14.5% increase this report has a 16.7% increase, which is more comparable numbers.

There are two other studies that have looked at the share of driverless taxis in a future transport system, both without changing other transport options to driverless (Chen and Kockelman, 2016; Liu et al., 2017)., Therefore, in table 12 they are compared to the CDC+SDT from this report which is most similar. Chen and Kockelman (2016) report is on non-shared driverless taxis, but the utility choices available will still be alike with similar VoTT and cost, except that no increased in-vehicle time due to sharing is needed. Liu et al. (2017) have modelled with sharing, but not modelled with any increase to in-vehicle time due to sharing. The CDC+SDT scenario in this report includes non-motorized trips but not current public transport as options. For Chen and Kockelman (2016) and Liu et al. (2017), the reverse is true, but the current public transport in Austin is not that well-developed. With all these differences, the results in this report are not that far away from the earlier studies when VoTT and prices are close.

Table 12: Comparison of results from models calculating the use of driverless taxis. VoTT Access price Price/pkm Share of DT Chen and Kockelman (2016) 25% Free €0.43 30.64% Chen and Kockelman (2016) 35% Free €0.43 27.10% Chen and Kockelman (2016) 50% Free €0.43 19.51% Chen and Kockelman (2016) 35% Free €0.38 39.05% Chen and Kockelman (2016) 35% Free €0.51 14.36% Liu et al. (2017) 50% Urban: €0.82 €0.25 43.3% Suburban: €1.64 Extra urban: €2.46 Liu et al. (2017) 50% Urban: €0.82 €0.38 16.7% Suburban: €1.64 Extra urban: €2.46 Liu et al. (2017) 50% Urban: €0.82 €0.51 8.9% Suburban: €1.64 Extra urban: €2.46 Liu et al. (2017) 50% Urban: €0.82 €0.64 7.0% Suburban: €1.64 Extra urban: €2.46 CDC+SDT 60% Free €0.35 Work: 28.1% Other: 21.2%

Liu et al. (2017) have another interesting result, wherein low-price scenarios SDTs dominate for long trips, while in high price scenarios they have a much lower market share and are mostly used for short trips. This means that the high price scenarios correlate well with the result in this study, even if the price in this report is at the lower end of the different variants in Liu et al. (2017). However, the model in this report has a much higher utility cost for long trips in SDTs. Because of this report using a 20% increase in travel time compared to CDCs and 60% of CDCs VoTT, while Liu et al. (2017) use no increase in travel time and 50% of CDCs VoTT.

6.7 Implications for sustainability Concerning ecological sustainability, the first conclusion is that private vehicle ownership and use is not likely to be reduced radically by driverless technology. This means that the full reductions in emissions, vehicles and VKT seen in reports such as the high penetration scenarios of OECD - International Transport Forum (2015; 2016; 2017a; 2017b; 2017c) and Greenblatt and Saxena (2015) are not likely to happen. However, a part of the reduced emissions in Greenblatt and Saxena (2015) is due to electrification, which has not been investigated in this report.

Instead, this report has indicated an increase in VKT by private vehicles due to driverless technology. Albeit it is unclear if the large private vehicle VKT increase seen in PDV+DB will happen with both SDTs and DBs as shared choices. On the other hand, empty kilometres are not included, which will exist with driverless technology. Additionally, kilometres driven by SDTs and DBs are going to be higher than by taxis and buses today if they are to perform as in the models. Because of these factors not modelled in this study, the total change in VKT in Stockholm due to driverless technology is unclear, but the study indicates that a substantial total VKT increase is likely. This would continue the worrying trend stated at the start of this report from the Swedish Environmental Protection Agency, that reductions in transport emissions due to cleaner technology are offset by increased VKT.

The effect on walking and biking is also interesting. In PDV+DB there is a 23% reduction of the share of work trips and a 16% reduction of the share of other trips done by biking or walking. In a scenario with both SDT and DBs, the share of biking and walking would likely decrease even more. Especially considering the results in figure 7 which show that SDTs perform best for short trips, where biking and walking are common. The reduction in other trips done by non-motorized modes can influence both social and environmental sustainability. Social in terms of the health benefits of walking and biking, and environmentally as walking and biking are the most environmentally friendly trips.

Looking deeper at social sustainability, it is important first to mention that increased travelled kilometres, except for empty ones, are also increased accessibility. One of the likely effects of driverless technology, be it shared or private, is also increased accessibility for those who have the least now. In the future young, old and disabled people who can’t drive or afford taxis with drivers will be able to use PDVs, order SDTs or use the improved public transport with DBs. However, on the negative side of social sustainability, the increased VKT are likely to increase harmful air pollution and possibly reduce accessibility at times due to increased congestion. Another negative part is likely to be the loss of work for drivers, which are usually workers without much education.

For economic sustainability, most effects of driverless technology are likely to be good. Increased access to workplaces and possibility to work during travel together with accepting longer travels with PDVs will have positive economic effects. Furthermore, the results of this study indicate that the car industry does not need to be worried about losing customers. On the negative side of things, increased congestion can have a huge cost for society, and the increased VKT risks increasing congestion. Moreover, the loss of available work driving taxis and buses will have a negative economic effect on society.

6.8 Transferability The model used in this report is of Stockholm County. However, the modifications of utility are done based on the general results of earlier reports on driverless vehicles. Two main things should not be transferable. Firstly, the reference is done in Stockholm, and Stockholm is a city with well-developed public transport and a high share of public transport usage. Therefore, the increase in the share of trips done by private vehicles due to PDV is not transferable to cities where a large majority of trips are already done in private vehicles. Secondly, for DBs, the change to waiting time is done in percentage, which means that the current public transport in Stockholm is a part of that model. This implies that the share of public transport performed by DBs is not transferable to cities which do not have well developed public transport. However, such cities should be able to reach the same results if they invest in public transport to a similar extent that Stockholm County does.

6.9 Insights about regulations Another question is what can be done in terms of public decision making and regulations to affect transport mode and car ownership choices. Something that can be seen in the results is that perceived comfort has a large effect on transport mode choice in the scenarios. This is an expected result in a 2040 scenario, as the higher projected economic strength of citizens means that the experience of the trip is going to matter more and the monetary cost less. VoTT of PDVs is not something that can be changed by regulations, but transport time can be, which has the same effect on the utility cost of a transportation choice. Regulations could focus on decreasing travel time of shared transport modes, both buses and shared taxis, at the cost of increasing it for private cars. This for example creating lanes and roads where only shared vehicles can travel.

Another option could be efforts to increase comfort and therefore lower VoTT in shared modes. For SDTs, a likely comfort reducer is the fact that there can be unknown passengers in a relatively small space and no one there that is responsible. Such perceived discomfort could be fixed by designs where all passengers are separated by walls and have their own entrance, which would also take away the privacy advantage of private cars.

6.10 Future things to study This study can count as one of the early tries to model the driverless transport system with a demand model, and the first to the author’s knowledge with a car ownership model. However, as discussed earlier, there are many improvements that could be made to get a clearer picture of how the demand for different ways of transportation will function with driverless technology. To create a model that can function with CDCs, PDVs, SDTs and DBs as separate choices would be another step to get a full picture of the coming system. Another step again would be to pair a demand model with a transport model that supports driverless features such as empty vehicles moving. Possibly, this could be performed in

MATSim which has been used by several articles cited in this report (Bösch, Ciari and Axhausen, 2016; Bischoff and Maciejewski, 2016; Loeb and Kockelman, 2018; Loeb, Kockelman and Liu, 2018).

This report also points to the importance of estimating VoTT for driverless transport, and PDVs especially. However, it is likely very hard to get good results before driverless transportation exists.

7. Conclusion

This study is an early try to model the competitiveness of different transport options when driverless technology exists. There are several things which affect the results that might differ greatly in how things are in 2040. One of them is that there is no scenario with all transport options available simultaneously. Another one that the input data used is qualified guesses based on earlier studies with qualified guesses, and not actual knowledge of how driverless vehicles will perform. Nonetheless, the study adds knowledge to the growing research on likely societal impacts of driverless technology.

The main takeaway from this study is that private vehicles are still likely to be popular with driverless technology. That result, together with the VKT results from this study and earlier studies with PDVs, indicate that driverless technology will increase total VKT decrease it, at least in cities that currently have well-developed public transport systems. Increased VKT further means increased emissions and congestion. Eco-driving, electric vehicles, and the congestion reducing effects that are predicted when transport is fully autonomous and connected can counteract these effects but are not examined in this report.

8. References

Aftonbladet (2017). Här rullar självkörande fordon i Sverige. [online] Available at: https://www.aftonbladet.se/bil/a/gPaa91/har-rullar-sjalvkorande-fordon-i-sverige [Accessed 26 Mar. 2018].

Auckland Transport (2014). Annual Report 2013

Bansal, P., & Kockelman, K. (2018). Are we ready to embrace connected and self-driving vehicles? A case study of Texans. Transportation, 45(2), 641-675.

BBC (2018). Uber barred from resuming Arizona self-drive trial. [online] Available at: http://www.bbc.com/news/technology-43554965 [Accessed 27 Mar. 2018].

Bischoff, J. and Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia computer science, 83, pp.237-244.

Bloomberg (2017). If You Hate Driving in the Snow, a Robot Can Now Do It For You. [online] Available at: https://www.bloomberg.com/news/articles/2017-12-20/martti-is-a-fully-autonomous- car-made-for-snowy-streets [Accessed 26 Mar. 2018].

Bösch, P., Becker, F., Becker, H., and Axhausen, K. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76-91.

Bösch, P., Ciari, F., and Axhausen, K. (2016). Autonomous Vehicle Fleet Sizes Required to Serve Different Levels of Demand. Transportation Research Record: Journal of the Transportation Research Board, 2542(2542), 111-119.

Capital Metropolitan Transportation Authority (2017). 2016 Annual Report

Carris (2011). Annual report & accounts 2010

Chen, T., and Kockelman, K. (2016). Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes. Transportation Research Record: Journal of the Transportation Research Board, 2572(2572), 37-46.

Chen, T., Kockelman, K. and Hanna, J. (2016). Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions. Transportation Research Part A: Policy and Practice, 94, pp.243-254.

Childress, S., Nichols, B., Charlton, B. and Coe, S. (2015). Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2493, pp.99-106.

Concas, S., and Kolpakov, A. (2009). Synthesis of research on value of time and value of reliability.

Davidson, P and Spinoulas, A (2016). Driving alone versus riding together - How shared autonomous vehicles can change the way we drive. Road & Transport Research: A Journal of Australian and New Zealand Research and Practice, 25(3):51–66, 2016. de Jong, G.D., Fox, J., Daly, A., Pieters, M. and Smit, R., (2004). Comparison of car ownership models. Transport Reviews, 24(4), pp.379-408. de Looff, E., Correia, GHDA., van Cranenburgh, S., Snelder, M. and van Arem, B., (2018). Potential Changes in Value of Travel Time as a Result of Vehicle Automation: a Case Study in the Netherlands (No. 18-03109).

Diels, C., and Bos, J. (2016). Self-driving carsickness. Applied Ergonomics, 53, 374.

Eriksson, T., Betanzo, M., Johansson, M. and Norheim, B. (2017). Hur får vi mer kollektivtrafik för pengarna? Ekonomisk analys av perioden 2000–2015 av svensk upphandlad kollektivtrafik.

Fagnant, D. and Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, pp.167-181.

Fairhurst, M.H., (1975). The influence of public transport on car ownership in London. Journal of Transport Economics and Policy, pp.193-208.

Fifer, S., Rose, J. and Greaves, S., (2014). Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?. Transportation research part A: policy and practice, 61, pp.164-177.

Fraedrich, E., Cyganski, R., Wolf, I., and Lenz, B. (2016). User perspective on autonomous driving: A Use-Case-Driven Study in Germany. Arbeitsberichte. Gerographisches Institut, Humboldt-Universität zu Berlin, 187(187), 112.

Gawron, J., Keoleian, G., De Kleine, R., Wallington, T., and Kim, H. (2018). Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects. Environmental Science & Technology, 52(5), 3249-3256.

Greenblatt, J. and Saxena, S. (2015). Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature Climate Change, 5(9), pp.860-863.

Helsinki Region Transport (2013). Annual Report 2012

Horni, A., Nagel, K. and Axhausen, K.W. (eds.). (2016). The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press.

Innocenti, A., Lattarulo, P. and Pazienza, M.G., (2013). Car stickiness: Heuristics and biases in travel choice. Transport Policy, 25, pp.158-168.

INRO. (2018). Emme. [online] Available at: https://www.inrosoftware.com/en/products/emme/ [Accessed 17 Jul. 2018].

Intergovernmental Panel on Climate Change. (2015). Climate change 2014: mitigation of climate change (Vol. 3). Cambridge University Press.

Kong, G.D., Fox, J., Daly, A., Pieters, M. and Smit, R., (2004). Comparison of car ownership models. Transport Reviews, 24(4), pp.379-408.

Jonsson, D., Berglund, S., Almström, P., and Algers, S. (2011). The Usefulness of Transport Models in Swedish Planning Practice. Transport Reviews, 31(2), 251-265.

LA Times (2018). Lidar costs $75,000 per car. If the price doesn't drop to a few hundred bucks, driverless cars won't go mass market. Available at: http://www.latimes.com/business/la-fi-hy- ouster-lidar-20171211-htmlstory.html [Accessed 12 Apr. 2018].

Lavasani, M., Jin, X. and Du, Y. (2016). Market Penetration Model for Autonomous Vehicles on the Basis of Earlier Technology Adoption Experience. Transportation Research Record: Journal of the Transportation Research Board, 2597, pp.67-74.

Lisbon Metro (2011). Report & accounts 2010

Litman, T. (2017). Autonomous vehicle implementation predictions. Victoria Transport Policy Institute.

Liu, J., Kockelman, K., Bösch, P., and Ciari, F. (2017). Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation. Transportation, 44(6), 1261- 1278.

Loeb, B. and Kockelman, K., (2018). Fleet performance & cost evaluation of a shared autonomous electric vehicle (SAEV) Fleet: A case study for Austin, Texas. Transport. Res. Part A: Policy Pract (submitted for publication).

Loeb, B., Kockelman, K. and Liu, J. (2018) Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transportation Research Part C: Emerging Technologies, 89, pp.222–233.

Milakis, D., van Arem, B. and van Wee, B., (2017). Policy and society related implications of automated driving: a review of literature and directions for future research. Journal of Intelligent Transportation Systems, 21(4), pp.324-348.

MIT Technology Review (2016a). 2021 May Be the Year of the Fully Autonomous Car. [online] Available at: https://www.technologyreview.com/s/602196/2021-may-be-the-year-of-the-fully- autonomous-car/ [Accessed 26 Mar. 2018].

MIT Technology Review (2016b). Prepare to be Underwhelmed by 2021’s Autonomous Cars. [online] Available at: https://www.technologyreview.com/s/602210/prepare-to-be-underwhelmed-by-2021s- autonomous-cars/ [Accessed 26 Mar. 2018].

OECD – International Transport Forum (2015). Urban Mobility System Upgrade: How shared self- driving cars could change city traffic

OECD – International Transport Forum (2016). Shared Mobility - Innovation for Liveable Cities

OECD – International Transport Forum (2017a). Transition to Shared Mobility – How large cities can deliver inclusive transport services

OECD – International Transport Forum (2017b). Shared Mobility Simulations for Helsinki

OECD – International Transport Forum (2017c). Shared Mobility Simulations for Auckland

OECD (2014). The cost of air pollution: Health impacts of road transport.

Ortúzar, J., and Willumsen, L. (2011). Modelling transport (4th ed.). Chichester, West Sussex, United Kingdom: John Wiley & Sons.

Pernestål Brenden, A. and Kottenhof, K. (2018). Self-driving shuttles as a complement to public transport – a characterization and classification. In: 7th Transport Research Arena TRA 2018.

Pernestål Brenden, A., and Kristoffersson, I. (2018). Effects of Driverless Vehicles: A Review of Simulations. CTS Working Paper 2018:11, June. https://www.cts.kth.se/polopoly_fs/1.827829!/CTS2018-11.pdf.

Persson, C. and Jiang, S. (2015). Framtagande av kalibreringsmål för Sampers regionala modeller

Rigole, P.J. (2014). Study of a Shared Autonomous Vehicles Based Mobility Solution in Stockholm

SAE International. (2016). Surface vehicle recommended practice.

Schoettle, B., and Sivak, M. (2015). Potential impact of self-driving vehicles on household vehicle demand and usage.

SIKA. (2007). RES 2005–2006 Den nationella resvaneundersökningen.

Statistics Sweden. (2018). Greenhouse gas emissions and removals. [online] Available at: http://www.scb.se/en/finding-statistics/statistics-by-subject- area/environment/emissions/greenhouse-gas-emissions-and-removals/ [Accessed 30 Jul. 2018].

Stockholm County Council (2016). Resvanor i Stockholms län 2015.

Stockholm County Council (2017a). Befolkningsprognos 2017-2026/50

Stockholm County Council (2017b). Fakta om SL och länet 2016.

Svedberg, W. (2016). Nya och gamla perspektiv på ansvar? En rättsvetenskaplig studie om ansvar i en straffrättslig kontext gällande självkörande/uppkopplade fordon. VTI, Statens väg- och transportforskningsinstitut.

Swedish Code of Statues 2017:309. Förordning om försöksverksamhet med självkörande fordon. Stockholm: Ministry of Enterprise and Innovation

Swedish Environmental Protection Agency (2017). Fördjupad analys av svensk klimatstatistik 2017.

The Guardian (2017a). Google sibling Waymo launches fully autonomous ride-hailing service. [online] Available at: https://www.theguardian.com/technology/2017/nov/07/google-waymo-announces- fully-autonomous-ride-hailing-service-uber-alphabet [Accessed 26 Mar. 2018].

The Guardian (2017b). Uber plans to buy 24,000 autonomous Volvo SUVs in race for driverless future. [online] Available at: https://www.theguardian.com/technology/2017/nov/20/uber-volvo-suv-self- driving-future-business-ride-hailing-lyft-waymo [Accessed 26 Mar. 2018].

Trafikverket. (2018). Sampers. [online] Available at: https://www.trafikverket.se/tjanster/system- och-verktyg/Prognos--och-analysverktyg/Sampers/ [Accessed 23 Jul. 2018].

Truong, L. T., De Gruyter, C., Currie, G., and Delbosc, A. (2017). Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia. Transportation, 44(6), 1279-1292.

UN General Assembly (2015). Transforming our world: The 2030 Agenda for Sustainable Development; Resolution Adopted by the General Assembly on 25 September 2015. A/RES/70/1.

WSP (2016). LuTRANS V4 - Dokumentation. Version 2016-08-02. [Available upon request]

Zhang, R., Spieser, K., Frazzoli, E. and Pavone, M. (2015). Models, algorithms, and evaluation for autonomous mobility-on-demand systems. In American Control Conference (ACC), 2015 (pp. 2573- 2587). IEEE.

Zhang, W., Guhathakurta, S., and Khalil, E. B. (2018). The Impact of Private Autonomous Vehicles on Vehicle Ownership and Unoccupied VMT Generation. Transportation Research Part C, 90, 156–165.

TRITA TRITA-ABE-MBT-18427