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User Preferences for Automated Shared Mobility Services: An Alternative-Specific Mixed Logit Regression Approach

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Abstract

Ridesharing transportation services as the leading transport systems in recent years have already invested in shared autonomous vehicles (SAVs) as the next generation of shared transportation systems. Recent years have seen a noticeably accelerating rate in utilizing advanced communications and mapping service technologies that opened new doors to the ridesharing operators to provide a better quality of service to the users; therefore, it is necessary to have a better insight in-to the preferences of the users for using SAVs. This study aims to identify individuals’ preferences for using SAVs and investigate related demographic characteristics and travel behavior attributes. An online Adaptive Choice-Based Conjoint (ACBC) survey will be designed and implemented between March to May 2020 in the United States. Then a series of mixed logit models (MXLs) were used to estimate participants’ preferences. The results show that females are more tended to use door-to-door services, younger riders are more interested in using SAVs and also accept longer travel times, and riders with high-income levels are willing to use SAVs with higher equipment and convenience door-to-door service and not share their trips with others. Furthermore, more educated riders are more likely to use door-to-door service, select a service with shorter travel time, and pay a little more money to insure their trip against possible delays.

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Abbreviations

ACBC:

Adaptive Choice-Based Conjoint

AV:

Autonomous Vehicle

CNL:

Cross-Nested Logit

DDA:

Descriptive Data Analysis

ESMS:

Electric Shared Mobility Services

HOV:

High-Occupancy Vehicle

IIA:

Independence of Irrelevant Alternatives

LCM:

Latent Class Model

MXL:

Mixed Logit Model

MNL:

Multinomial Logit Model

MDCP:

Multiple Discrete-Continuous Probit

RA:

Regression Analysis

SAV:

Shared Autonomous Vehicle

WTP:

Willingness-To-Pay

WTU:

Willingness-To-Use

References

  1. Khadem, N.K., Nickkar, A., Shin, H.-S.: A Review of Different Charging Stations Optimal Localization Models and Analysis Functions for the Electric Vehicle Charging Infrastructure, in International Conference on Transportation and Development 2020. 262-276 (2020)

  2. Shafaghat, A., Keyvanfer, A., Muslim, N.H.B.: Drivers’ adaptive travel behaviors towards green transportation development: a critical review. Archives of Transport. 38(2), 49–70 (2016)

    Article  Google Scholar 

  3. Shin, H.-S., Farkas, Z.A., Nickkar, A.: An analysis of attributes of electric vehicle owners’ travel and purchasing behavior: the case of Maryland. in International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities. American Society of Civil Engineers Reston, VA. (2019)

  4. Nickkar, A., Khadem, N.K., Shin, H.-S.: Willingness to Pay for Autonomous Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach. In: International Conference on Transportation and Development 2020. American Society of Civil Engineers Reston, VA. p. 1-14 (2020)

  5. Nickkar, A., Lee, Y.-J.: Evaluation of dedicated lanes for automated vehicles at roundabouts with various flow patterns. arXiv preprint arXiv:1904.07025, (2019)

  6. Shin, H.-S., et al.: User Acceptance and Willingness to Pay for Connected Vehicle Technologies: Adaptive Choice-Based Conjoint Analysis. Transp. Res. Rec. 2531(1), 54–62 (2015)

    Article  Google Scholar 

  7. Etminani-Ghasrodashti, R., et al.: Modeling users’ adoption of shared autonomous vehicles employing actual ridership experiences. Transp. Res. Rec. 2676, 03611981221093632 (2022)

    Article  Google Scholar 

  8. Haboucha, C.J., Ishaq, R., Shiftan, Y.: User preferences regarding autonomous vehicles. Transp Res Part C Emerg Technol. 78, 37–49 (2017)

    Article  Google Scholar 

  9. Krueger, R., Rashidi, T.H., Rose, J.M.: Preferences for shared autonomous vehicles. Transp Res Part C Emerg Technol. 69, 343–355 (2016)

    Article  Google Scholar 

  10. Wang, S., et al.: Attitudes towards privately-owned and shared autonomous vehicles. Transport. Res. F: Traffic Psychol. Behav. 72, 297–306 (2020)

    Article  Google Scholar 

  11. Yuen, K.F., et al.: Factors influencing the adoption of shared autonomous vehicles. Int. J. Environ. Res. Public Health. 17(13), 4868 (2020)

    Article  Google Scholar 

  12. Hamadneh, J., Esztergár-Kiss, D.: Impacts of Shared Autonomous Vehicles on the Travelers’ Mobility. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). (2019)

  13. Khan, M.A., et al.: Integrating Shared Autonomous Vehicles into Existing Transportation Services: Evidence from a Paratransit Service in Arlington, Texas. Int J Civil Eng. 20(6), 601–618 (2022)

    Article  Google Scholar 

  14. Mousavi, S.M., et al.: Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial. Accid. Anal. Prev. 152, 105982 (2021)

    Article  Google Scholar 

  15. Nickkar, A., Lee, Y.-J.: Willingness to Pay for Advanced Safety Features in Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach. Transp. Res. Rec. 2676, 03611981221077077 (2022)

    Article  Google Scholar 

  16. Obaid, M., Torok, A.: Macroscopic Traffic Simulation of Autonomous Vehicle Effects. Vehicles. 3(2), 187–196 (2021)

    Article  Google Scholar 

  17. Sohrabi, S., et al.: Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research. Accid. Anal. Prev. 152, 106003 (2021)

    Article  Google Scholar 

  18. Burns, L.D.: A vision of our transport future. Nature. 497(7448), 181–182 (2013)

    Article  Google Scholar 

  19. Fagnant, D.J., Kockelman, K.M., Bansal, P.: Operations of shared autonomous vehicle fleet for Austin, Texas, market. Transp. Res. Rec. 2563(1), 98–106 (2015)

    Article  Google Scholar 

  20. Ahangari, S., Chavis, C., Jeihani, M.: Public transit ridership analysis during the Covid-19 pandemic. medRxiv, p. 2020.10.25.20219105 (2020)

  21. Golbabaei, F., Yigitcanlar, T., Bunker, J.: The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature. Int. J. Sustain. Transp. 15(10), 731–748 (2021)

    Article  Google Scholar 

  22. Nickkar, A., Lee, Y.-J., Shin, H.-S.: Willingness-to-pay for shared automated mobility using an adaptive choice-based conjoint analysis during the COVID-19 period. Travel Behav. Soc. 30, 11–20 (2023)

    Article  Google Scholar 

  23. Narayanan, S., Chaniotakis, E., Antoniou, C.: Shared autonomous vehicle services: A comprehensive review. Transp Res Part C Emerg Technol. 111, 255–293 (2020)

    Article  Google Scholar 

  24. Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics. 1(3), 355–387 (2021)

    Article  Google Scholar 

  25. Spurlock, C.A., et al.: Describing the users: Understanding adoption of and interest in shared, electrified, and automated transportation in the San Francisco Bay Area. Transp. Res. Part D: Transp. Environ. 71, 283–301 (2019)

    Article  Google Scholar 

  26. Hudson, J., Orviska, M., Hunady, J.: People’s attitudes to autonomous vehicles. Transp. Res. A Policy Pract. 121, 164–176 (2019)

    Article  Google Scholar 

  27. Kim, S.H., Mokhtarian, P.L., Circella, G.: Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia. Transp. Res. Part D: Transp. Environ. 82, 102291 (2020)

    Article  Google Scholar 

  28. Winter, K., et al.: Identifying user classes for shared and automated mobility services. Eur. Transp. Res. Rev. 12(1), 36 (2020)

    Article  Google Scholar 

  29. Rahimi, E., et al.: Perceived risk of using shared mobility services during the COVID-19 pandemic. Transport. Res. F: Traffic Psychol. Behav. 81, 271–281 (2021)

    Article  Google Scholar 

  30. Turoń, K., Kubik, A., Chen, F.: Electric shared mobility services during the pandemic: Modeling aspects of transportation. Energies. 14(9), 2622 (2021)

    Article  Google Scholar 

  31. Brown, A., Gonder, J., Repac, B.: An analysis of possible energy impacts of automated vehicles. In: Meyer, G., Beiker, S. (eds.) Road vehicle automation. Lecture notes in mobility, pp. 137–153. Springer, Cham (2014)

    Chapter  Google Scholar 

  32. Miller, S.A., Heard, B.R.: The environmental impact of autonomous vehicles depends on adoption patterns. Environ Sci Technol. 50(12), 6119–6121 (2016). https://doi.org/10.1021/acs.est.6b02490

    Article  Google Scholar 

  33. Miller, K., Chng, S., Cheah, L.: Understanding acceptance of shared autonomous vehicles among people with different mobility and communication needs. Travel Behav. Soc. 29, 200–210 (2022)

    Article  Google Scholar 

  34. Shin, J., et al.: Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types. Transp Res Part C Emerg Technol. 60, 511–524 (2015)

    Article  Google Scholar 

  35. Bansal, P., Kockelman, K.M., Singh, A.: Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transp Res Part C Emerg Technol. 67, 1–14 (2016)

    Article  Google Scholar 

  36. Gkartzonikas, C., Ke, Y., Gkritza, K.: A tale of two modes: Who will use single user and shared autonomous vehicles. Case Stud Transp Policy. 10(3), 1566–1580 (2022)

    Article  Google Scholar 

  37. Krueger, R., et al.: Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity. J Choice Model. 41, 100323 (2021)

    Article  Google Scholar 

  38. Sawtooth Software, ACBC Technical Paper. Sawtooth Software Technical Paper Series. pp. 1-21 (2009)

  39. U.S. Census Bureau; American Community Survey (2017). https://data.census.gov. Accessed 7 Jan 2021

  40. Bösch, P.M., et al.: Cost-based analysis of autonomous mobility services. Transp. Policy. 64, 76–91 (2018)

    Article  Google Scholar 

  41. Fagnant, D.J., Kockelman, K.M.: Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation. 45(1), 143–158 (2018)

    Article  Google Scholar 

  42. Litman, T.: Autonomous vehicle implementation predictions. Victoria Transport Policy Institute Victoria, BC, Canada. (2017)

  43. Alessandrini, A., et al.: CityMobil2: challenges and opportunities of fully automated mobility. Road vehicle automation, p. 169-184 (2014)

  44. Bellem, H., et al.: Comfort in automated driving: An analysis of preferences for different automated driving styles and their dependence on personality traits. Transport. Res. F: Traffic Psychol. Behav. 55, 90–100 (2018)

    Article  Google Scholar 

  45. Nazari, F., Noruzoliaee, M., Mohammadian, A.K.: Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes. Transp Res Part C Emerg Technol. 97, 456–477 (2018)

    Article  Google Scholar 

  46. Levin, M.W., et al.: A linear program for optimal integration of shared autonomous vehicles with public transit. Trans Res Part C Emerg Technol. 109, 267–288 (2019)

    Article  Google Scholar 

  47. Wang, Y., et al.: Understanding consumers’ willingness to use ride-sharing services: The roles of perceived value and perceived risk. Transp Res Part C Emerg Technol. 105, 504–519 (2019)

    Article  Google Scholar 

  48. Zhang, W., Guhathakurta, S., Fang, J., Zhang, G.: The performance and benefits of a shared autonomous vehicles based dynamic ridesharing system: An agent-based simulation approach. In: Transportation Research Board 94th Annual Meeting, vol. 15, p. 2919. (2015)

    Google Scholar 

  49. Hensher, D.A., Greene, W.H.: The Mixed Logit model: The state of practice. Transportation. 30(2), 133–176 (2003)

    Article  Google Scholar 

  50. Sener, I.N., Eluru, N., Bhat, C.R.: An analysis of bicycle route choice preferences in Texas, US. Transportation. 36(5), 511–539 (2009)

    Article  Google Scholar 

  51. Breidert, c, Hahsler, M., Schmidt-Thieme, L.: Reservation price estimation by Adaptive Conjoint Analysis. In: Weihs, C., Gaul, W. (eds.) Classification — the ubiquitous challenge. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg (2005). https://doi.org/10.1007/3-540-28084-7_67

    Chapter  Google Scholar 

  52. Giesen, J., Mueller, K., Taneva, B., Zolliker, P.: Choice-based conjoint analysis: Classification vs. discrete choice models. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference learning, pp. 297–315. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14125-6_14

    Chapter  Google Scholar 

  53. So, Y., Kuhfeld, W.F.: Multinomial logit models. in SUGI 20 conference proceedings. (1995)

  54. Hole, A.R.: Mixed logit modeling in Stata--an overview. In: United Kingdom Stata Users' Group Meetings 2013. Stata Users Group. (2013)

  55. Train, K.E.: Discrete choice methods with simulation, pp. 134–140. Cambridge university press (2009)

    MATH  Google Scholar 

  56. Acock, A.C.: A gentle introduction to Stata, pp. 220–227. Stata press (2008)

    MATH  Google Scholar 

  57. Lim, T.S.: A study of the scheduling effect on shared autonomous vehicles adoption. Transp Res Interdiscip Perspect. 10, 100394 (2021)

    Google Scholar 

  58. König, A., Grippenkoven, J.: Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour. Travel Behav. Soc. 21, 188–202 (2020)

    Article  Google Scholar 

  59. Schwieterman, J., Smith, C.S.: Sharing the ride: A paired-trip analysis of UberPool and Chicago Transit Authority services in Chicago, Illinois. Res. Transp. Econ. 71, 9–16 (2018)

    Article  Google Scholar 

  60. Amirkiaee, S.Y., Evangelopoulos, N.: Why do people rideshare? An experimental study. Transport. Res. F: Traffic Psychol. Behav. 55, 9–24 (2018)

    Article  Google Scholar 

  61. Sarriera, J.M., et al.: To share or not to share: Investigating the social aspects of dynamic ridesharing. Transp. Res. Rec. 2605(1), 109–117 (2017)

    Article  Google Scholar 

  62. Tahmasseby, S., Kattan, L., Barbour, B.: Propensity to participate in a peer-to-peer social-network-based carpooling system. J. Adv. Transp. 50(2), 240–254 (2016)

    Article  Google Scholar 

  63. Smith, S.: Determining Sample Size: How to Ensure You Get the Correct Sample Size, E-Book (c) Qualtrics Online Sample. (2013)

  64. Haan, P., Uhlendorff, A.: Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood. Stata J. 6(2), 229–245 (2006)

    Article  Google Scholar 

  65. Krueger, R., Bierlaire, M., Daziano, R.A., Rashidi, T.H., Bansal, P.: Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity. J Choice Model. 41, 100323 (2021)

    Article  Google Scholar 

  66. Dias, F.F., Lavieri, P.S., Garikapati, V.M., Astroza, S., Pendyala, R.M., Bhat, C.R.: A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation. 44(6), 1307–1323 (2017)

    Article  Google Scholar 

  67. Clewlow, R., Mishra, G.S.: Shared mobility: Current adoption, use, and potential impacts on travel behavior. (2017)

  68. Winter, K., Cats, O., Martens, K., van Arem, B.: Identifying user classes for shared and automated mobility services. Eur. Transp. Res. Rev. 12(1), 1–11 (2020)

    Article  Google Scholar 

  69. Islam, M.R., Abdel-Aty, M., Lee, J., Wu, Y., Yue, L., Cai, Q.: Perception of people from educational institution regarding autonomous vehicles. Transp Res Interdiscip Perspect. 14, 100620 (2022)

    Google Scholar 

  70. Wang, Z., Safdar, M., Zhong, S., Liu, J., Xiao, F.: Public Preferences of Shared Autonomous Vehicles in Developing Countries: A Cross-National Study of Pakistan and China. J. Adv. Transp. 2021, 5141798 (2021)

    Article  Google Scholar 

  71. Lécureux, B., Bonnet, A., Manout, O., Berrada, J., Bouzouina, L.: Acceptance of Shared Autonomous Vehicles: A Literature Review of stated choice experiments. (2022)

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Acknowledgments

The authors would like to thank the Sawtooth Software, Inc. for providing an academic grant to conduct this study. The authors also appreciate the Urban Mobility & Equity Center at Morgan State University for their support. The findings of this research do not necessarily represent the views or positions of the authors’ affiliated institutions.

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Authors and Affiliations

  1. Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, USA

    Alireza Ansariyar, Amirreza Nickkar & Young-Jae Lee

  2. Department of City and Regional Planning, Morgan State University, Baltimore, USA

    Hyeon-Shic Shin

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  1. Alireza Ansariyar
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  2. Amirreza Nickkar
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Correspondence to Amirreza Nickkar.

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Ansariyar, A., Nickkar, A., Lee, YJ. et al. User Preferences for Automated Shared Mobility Services: An Alternative-Specific Mixed Logit Regression Approach. Int. J. ITS Res. 21, 331–348 (2023). https://doi.org/10.1007/s13177-023-00358-0

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