Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method
Abstract
:1. Introduction
2. Literature Review
- Acceptability of AVs and sharing, which influence the willingness to change the car ownership, and furthermore, it may affect the car fleet size and the modal share.
- Parking preference, comparing on-street and off-street parking.
- Possible solutions for urban space repurposing.
2.1. Acceptability: AVs and Shared Use
- Private shared (PSAV): private vehicle usually shared among family members.
- Shared use with single occupancy (SAVSO): use of a vehicle from a car-sharing system by one person on a trip.
- Shared use with multiple occupancies (SAVMO): use of a vehicle from a car-sharing system by more than one person on a trip.
2.2. Parking Preference: On-Street or Off-Street Parking
2.3. Urban Space Repurposing
- Focusing on green space quality rather than quantity in urban planning priorities.
- Investing in small public green spaces rather than in big parks.
- Investing in cleanliness and maintenance within public green spaces and improving plant species richness.
- AVs and shared acceptability are related to many variables influenced by behaviour and demographic characteristics.
- Parking charges, cruising time, and walk time are significant aspects considered in parking choice.
- Citizens desire a high level of liveability through the implementation mainly of green spaces.
3. Methods
3.1. Scenario Building Method
- Fleet size: conventional vehicles or AVs.
- Modal share: conventional vehicles, AVs, public transport, active transport, etc.
- Car ownership: private, PSAV, SAVSO, or SAVMO.
- Parking preference: on-street or off-street parking.
- Urban space repurposing priorities: private space, pick-up and drop-off areas, public spaces such as bike lanes, sidewalks, parks, etc.
- Current data are available,
- Current data are not available, and
- Forecast future data to assess future changes.
- General travel behaviour: the current car ownership and modal share.
- Parking: most used type of parking.
- Willingness to change: the willingness to change AVs and shared mobility forms, as well as the situations in which the use of AVs and shared mobility forms would be favourable.
- Priorities of citizens and decision-makers: priorities about reallocation of urban space.
- Socioeconomic data: age, gender, income, number of children in the household, education level, and employment status.
3.2. Calculation Method for Urban Space Transformation
4. Results
4.1. Scenarios
4.2. Urban Space Transformation: Case Study in Budapest
- Scenarios 1 (Current) and 2 (Near Future): saved space does not result, the number of parking spaces needed is the same as today.
- Scenario 3 (Transitional): one-third of the on-street parking spaces (818 units) can be eliminated.
- Scenario 4 (Future with AVs): double the space saved in Scenario 3 can be eliminated (65%, 1618 parking spaces).
- Scenario 5 (Mix of SAV fleets): almost 83% of parking spaces (2072 units) can be eliminated.
- Increasing the SAV fleet leads to a reduction in pollutants compared to conventional vehicles. Gains in the reduction of pollutants varied from 2% to 18% (Scenario 3: Transitional) and increased proportionally to the increase in SAV fleet, reaching 5% to 45% (Scenario 5: Mix of SAV fleets).
- If green spaces are formed in the saved space, the current air pollution level can be mitigated. Shrubs planted in all the saved space might remove up to 13 kg of SO2 and CO, 19.4 kg of NOx, and almost 188 kg of PM10 annually in Scenario 5.
- Number of trips/distance would remain the same as recently, and
- The pollution for vehicle running is the same as recently (not considering electric propulsion).
5. Discussion
6. Conclusions
- Five scenarios can depict the possible future situations according to the literature review and the results of a questionnaire survey.
- Car ownership is the most critical aspect of transformation in urban space due to its potential to decrease parking demand.
- SAVs can significantly minimise air pollution caused by parking infrastructure as they decrease parking demand (from 33% up to 83%). Also, for other pollutants, the reduction can reach 45%.
- SAVs use positively impacts the amount of space for repurposing (around 83%) through reducing parking demand.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- A.
- General Travel Behaviour
- 1.
- Mark the sentence which best describes you (Evaluation: Current Car ownership):
- a.
- I have my own car (Private)
- b.
- I share the vehicle with members of the family (Private shared)
- c.
- I drive a shared car (Carsharing with single occupancy)
- d.
- I take a taxi (Sharing with single occupancy)
- e.
- I share the same vehicle with other people (Ridesharing/Carsharing with multiple occupancy)
- 2.
- Choose the three most used (please consider the number of trips) modes by you (Evaluation: Current modal share):
- a.
- Public Transport
- b.
- Bicycle
- c.
- Walking
- d.
- Car as a driver
- e.
- Car as a passenger
- f.
- Taxi
- g.
- Motorbike
- h.
- Carsharing
- i.
- Bike-sharing
- j.
- Ridesharing as a passenger
- k.
- Ridesharing as a driver
- B.
- Parking
- 3.
- Choose the two most used (please consider the number of parking sessions) parking facility types by you (Evaluation: identifying the most used type of parking, confirming whether on-street is the most used):
- a.
- Private parking at home (e.g., garage)
- b.
- Private parking (e.g., a paid building garage which has a private owner)
- c.
- Workplace
- d.
- Public parking at curb-side
- e.
- Supermarkets, markets, shops
- f.
- Parking and Ride (P + R) parking facility
- C.
- Willingness to change
- 4.
- What is the probability of you using an autonomous vehicle? (Evaluation: user acceptability):
- a.
- None
- b.
- Very unlikely
- c.
- Unlikely
- d.
- Likely
- e.
- Very likely
- 5.
- Are you willing to change your current car ownership? Car ownership: you have a car, you share a car with member of your family, you use carsharing or ridesharing (Evaluation: sharing acceptability):
- a.
- No, the current option will remain.
- b.
- Yes, I would buy my own car (private ownership).
- c.
- Yes, I would share my car with members of my family in order to reduce the number of cars that we have (private shared ownership).
- d.
- Yes, I would use taxi or carsharing as a driver (shared with single occupancy ownership).
- e.
- Yes, I would use ridesharing as a driver (shared with multiple occupancy).
- f.
- Yes, I would use ridesharing as a passenger (shared with multiple occupancy).
- 6.
- Mark the options which describe you best when completing the sentence: I would use AVs… (Evaluation: Situations in which respondents would use AVs, adapted [57]):
- a.
- To avoid parking
- b.
- When personal vehicle is unavailable (maintenance or repairs)
- c.
- As an alternative to driving (e.g., after drinking alcohol)
- d.
- For long trips
- e.
- For short trips
- f.
- Other
- g.
- Never
- 7.
- Mark the options which describe you best when completing the sentence: I would share an AV with someone at the same time… (Evaluation: Situations in which respondents would use SAVs with multiple occupancy, adapted [57]):
- a.
- When riding alone
- b.
- When riding with an adult family member or friend
- c.
- When riding with my child
- d.
- Only at times of day I think are safe
- e.
- For work
- f.
- For shopping
- g.
- For recreational trips
- h.
- For all trips
- i.
- I would not use the service
- D.
- Priorities of Citizens/Decision Makers
- 8.
- If parking spaces on streets could be eliminated, how would you use this additional space? Order according to priority, 1 is the highest priority and 9 is the lowest priority (Evaluation: identifying the priorities of decision makers regarding changes in urban space):
- a.
- New buildings
- b.
- Additional traffic lane
- c.
- Green spaces (e.g., gardens and parklets)
- d.
- Pick-up and drop-off points in every block
- e.
- Bike lanes
- f.
- Multifunctional zones (loading/unloading, food trucks, parklets)
- E.
- Socioeconomic Data
- 9.
- Age
- a.
- 18–35
- b.
- 36–60
- c.
- >60
- 10.
- Gender
- a.
- Female
- b.
- Male
- c.
- Other
- 11.
- Do you have a driving licence?
- a.
- No
- b.
- Yes
- 12.
- Employment
- a.
- Student
- b.
- Self-employed
- c.
- Employee in a private company
- d.
- Employee in the public sector
- e.
- Retired
- 13.
- Adults in the household
- a.
- 1
- b.
- 2
- c.
- >2
- 14.
- Children in the household
- a.
- 0
- b.
- 1
- c.
- 2
- d.
- 3
- e.
- >3
- 15.
- Education level
- a.
- Studied until high school
- b.
- Technical College
- c.
- Bachelor’s degree
- d.
- Master’s degree
- e.
- Doctoral degree
- 16.
- Income level per person (HUF) per month
- a.
- <=100,000
- b.
- 100,001–200,000
- c.
- 200,001–300,000
- d.
- 300,001–400,000
- e.
- 400,001–500,000
- f.
- >500,000
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Sign | Name | Set of Value |
---|---|---|
k | Parking alignment | k = 1…z (1: parallel, 2: 45°, 3: 90°, etc.) |
j | Car ownership | j = 1…m (1: private, 2: private shared, 3: shared with single occupancy, 4: shared with multiple occupancies, etc.) |
x | Type of repurposing | x = 1…y (1: public transport, 2: improving walkability, 3: bike-related strategies, 4: additional traffic lane, etc.) |
e | Pollutant | e = 1…f (1: carbon monoxide (CO), 2: sulphur dioxide (SO2), 3: nitrogen oxide (NOx), 4: volatile organic compounds (VOC), 5: 10 μm particulate matter emissions (PM10)) |
Sign | Description |
---|---|
rate of increase in the number of conventional vehicles in year t | |
number of conventional vehicles in the year (t − 1) according to j ownership | |
rate of increase in the number of autonomous vehicles (AVs) in the year t according to j ownership | |
number of AVs in the year (t − 1) according to j ownership | |
percentage of saved parking space caused by autonomous vehicles according to j ownership | |
percentage of off-street parking space from the total parking spaces | |
percentage of parking spaces according to k alignment | |
area of each parking space according to k alignment (m2) | |
rate indicating how many private areas will be created in year t | |
percentage of the private area created in the previous year (t − 1) | |
rate indicating how many pick-up and/or drop-off areas will be created in year t | |
percentage of pick-up and drop-off areas created in the previous year (t − 1) | |
rate of space applied for new use related to x new use | |
area for x new use correspondent to the previous year (t − 1) (m2) | |
fleet size in the year t—the sum of conventional and autonomous vehicles | |
rate of reduction of pollutant e according to the car ownership j | |
rate of the positive impact caused by new use x of urban space | |
environmental savings on the pollutant e (e.g., (g/m2), (%)) | |
environmental savings of the new use x (e.g., (g/m2), (%)) |
Scenario | Name | Fleet Type | |
---|---|---|---|
1 | Current | Conventional Vehicles | 21% private; 44% private shared autonomous vehicle (PSAV); 13% shared autonomous vehicle with single occupancy (SAVSO); 22% shared autonomous vehicle with multiple occupancies (SAVMO) |
2 | Near Future | Conventional Vehicles | 43% private; 31% private shared; 7% SAVSO; 19% SAVMO |
3 | Transitional | Conventional Vehicles | 48% (private and/or shared) |
AVs | 10% private; 7% PSAV; 16% SAVSO; 19% SAVMO | ||
4 | Future with AVs | Conventional Vehicles | 12% (private and/or PSAV) |
AVs | 10% private; 7% PSAV; 52% SAVSO; 19% SAVMO | ||
5 | Mix of SAV fleets | AVs | 10% private; 50% SAVSO; 40% SAVMO |
Ownership | Study | Characteristics | Reduction in Parking Spaces |
---|---|---|---|
PSAV | [3] | SAVs among the members of the same household | –9.5% to –12.3% number of private vehicles |
SAVSO | [2] | 33% or 100% SAVs | –16% or –48% parking spaces |
[4] | 5% market SAV | –90.3% and –92.4% parking spaces | |
[5] | High occupancy of public transport (PuT); 100% or 50% SAVs | –89.3% or –21.2% parking spaces | |
SAVMO | [6] | Autonomous taxi fleet; 0%, 20%, and 40% of penetration rate | 3 conventional cars could be replaced with 1 autonomous taxi with 40% penetration rate |
[7] | 2% of market penetration level | –90% parking spaces | |
[5] | HHigh-occupancy of PuT; 100% or 50% SAVs | –94.4% or –24.2% parking spaces |
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Silva, D.; Földes, D.; Csiszár, C. Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method. Sustainability 2021, 13, 3008. https://doi.org/10.3390/su13063008
Silva D, Földes D, Csiszár C. Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method. Sustainability. 2021; 13(6):3008. https://doi.org/10.3390/su13063008
Chicago/Turabian StyleSilva, Dahlen, Dávid Földes, and Csaba Csiszár. 2021. "Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method" Sustainability 13, no. 6: 3008. https://doi.org/10.3390/su13063008