The goal of IJTTE is to broaden the knowledge of traffic and transport experts and academicians by promoting free access and provide valuable insight to transport related information, research and ideas. IJTTE will serve as a great resource for researchers and students across the globe.
Volume List  / Volume 10 (2)

Article

WILLINGNESS TO PAY FOR CONNECTED VEHICLES: AN ALTERNATIVE-SPECIFIC MIXED LOGIT REGRESSION APPROACH

DOI: 10.7708/ijtte.2020.10(2).07


10 / 2 / 215 - 228 Pages

Author(s)

Amirreza Nickkar - Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, USA -

Hyeon-Shic Shin - Department of City and Regional Planning, Morgan State University, Baltimore, USA -

Young-Jae Lee - Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, USA -


Abstract

The study of connected vehicles (CVs) has become a hot topic in recent years. Understanding the characteristics that lead consumers to relate to CVs motivates researchers to conduct market analysis studies. The current research investigated the socio-demographic attributes that may contribute to the individual preferences for purchasing CVs. Researchers constructed a series of Alternative-Specific Mixed Logit models to examine the associations between individual preferences of respondents and their willingness to pay for CV features in their future vehicle. The results indicate that hours spent driving play a privileged role among sociodemographic characteristics and driving behavior attributes of respondents. People who drive longer hours tended to purchase CV features. Also, the factor of age had a noticeable effect as the results showed that older people are more likely to purchase CV features.


Download Article

Number of downloads: 510

Keywords:
automated mobility; connected autonomous vehicle; willingness to pay; adoption; mixed Logit model;


Acknowledgements:

The authors thank the National Transportation Center at the Morgan State University for its support. This research was supported by the Connected Vehicle–Infrastructure University Transportation Center at Virginia Polytechnic and State University and the University Transportation Centers Program of the U.S. Department of Transportation. The authors declare no conflict of interest.


References:

Abraham, H.; Lee, C.; Brady, S.; Fitzgerald, C.; Mehler, B.; Reimer, B.; Coughlin, J. F. 2017. Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. In Proceedings of the Transportation Research Board 96th Annual Meeting (TRB'17).

 

Andorka, S.; Rambow-Hoeschele, K. 2020. Urban Landscape Revolution: The Potential of Connected Vehicles and Their Impact on the Mobility Ecosystem. Springer International Publishing, 157-166.

 

Arvin, R.; Kamrani, M.; Khattak, A. J. 2019. How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data, Accident Analysis & Prevention 127: 118-133.

 

Bansal, P.; Kockelman, K. M. 2017. Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies, Transportation Research Part A: Policy and Practice 95: 49-63.

 

Bansal, P.; Kockelman, K. M.; Singh, A. 2016. Assessing public opinions of and interest in new vehicle technologies: An Austin perspective, Transportation Research Part C: Emerging Technologies 67: 1-14.

 

Bertini, R.; Wang, H.; Knudson, T.; Carstens, K. 2016. Preparing a Roadmap for Connected Vehicle/Cooperative Systems Deployment Scenarios: Case Study of the State of Oregon, USA, Transportation Research Procedia 15: 447-458.

 

Bock, D. L.; Kettles, D.; Harrison, J. 2016. Automated, autonomous and connected vehicle technology assessment. Florida Solar Energy Center, FSEC Report Number: FSEC-CR-2020-16.

 

Choi, H.; Koo, Y. 2019. Do I have to buy it now? A vehicle replacement model considering strategic consumer behavior, Transportation Research Part D: Transport and Environment 73: 318-337.

 

Daziano, R. A.; Sarrias, M.; Leard, B. 2017. Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles, Transportation Research Part C: Emerging Technologies 78: 150-164.

 

Dowling, R.; Nevers, B.; Jia, A.; Skabardonis, A.; Krause, C.; Vasudevan, M. 2016. Performance Benefits of Connected Vehicles for Implementing Speed Harmonization, Transportation Research Procedia 15: 459-470.

 

Ebnali, M.; Hulme, K.; Ebnali-Heidari, A.; Mazloumi, A. 2019. How does training effect users’ attitudes and skills needed for highly automated driving?, Transportation Research Part F: Traffic Psychology and Behaviour 66: 184-195.

 

Fernandes, S. C. F.; Esteves, J. L.; Simoes, R. 2017. Characteristics and human factors of older drivers: improvement opportunities in automotive interior design, International Journal of Vehicle Design 74: 167-203.

 

Ghiasi, A.; Li, X.; Ma, J. 2019. A mixed traffic speed harmonization model with connected autonomous vehicles, Transportation Research Part C: Emerging Technologies 104: 210-233.

 

Gkartzonikas, C.; Gkritza, K. 2019. What have we learned? A review of stated preference and choice studies on autonomous vehicles, Transportation Research Part C: Emerging Technologies 98: 323-337.

 

Guerrero-Ibanez, J. A.; Zeadally, S.; Contreras-Castillo, J. 2015. Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies, IEEE Wireless Communications 22: 122-128.

 

Haboucha, C. J.; Ishaq, R.; Shiftan, Y. 2017. User preferences regarding autonomous vehicles, Transportation Research Part C: Emerging Technologies 78: 37-49.

 

Jaffry, S.; Apostolakis, A. 2011. Evaluating individual preferences for the British Museum, Journal of Cultural Economics 35(1): 49-75.

 

Jiang, Y.; Zhang, J.; Wang, Y.; Wang, W. 2018. Capturing ownership behavior of autonomous vehicles in Japan based on a stated preference survey and a mixed Logit model with repeated choices, International Journal of Sustainable Transportation 13(10): 788-801.

 

Kidando, E.; Moses, R.; Ghorbanzadeh, M.; Ozguven, E. E. 2018. Traffic Operation and Safety Analysis on an Arterial Highway: Implications for Connected Vehicle Applications. In Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), 2753-2758.

 

Kopelias, P.; Elissavet, D.; Vogiatzis, K.; Skabardonis, A.; Zafiropoulou, V. 2020. Connected & Autonomous Vehicles – Environmental Impacts – A review, Science of The Total Environment 712: 135237.

 

Kyriakidis, M.; Happee, R.; De Winter, J. C. F. 2015. Public opinion on automated driving: Results of an international questionnaire among 5000 respondents, Transportation Research Part F: Traffic Psychology and Behaviour 32: 127-140.

 

Li, L.; Liu, Y.; Wang, J.; Deng, W.; Oh, H. 2016. Vehicle mobility driven by traditional drivers versus connected drivers, Wireless Networks 22: 1891-1900.

 

Liu, P.; Guo, Q.; Ren, F.; Wang, L.; Xu, Z. 2019a. Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors, Transportation Research Part C: Emerging Technologies 100: 306-317.

 

Liu, P.; Yang, R.; Xu, Z. 2019b. Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions, Risk Analysis 39: 326-341.

 

Medenica, Z. 2019. Human Machine Interaction. In: MIUCIC, R. (ed.) Connected Vehicles: Intelligent Transportation Systems. Cham: Springer International Publishing.

 

Motamedi, S.; Wang, P.; Chan, C. Y. 2018. User Acceptance and Public Policy Implications for Deployment of Automated Driving Systems. Institute of Transportation Studies, Berkeley.

 

Narla, S. R. 2013. The evolution of connected vehicle technology: From smart drivers to smart cars to self-driving cars, Ite Journal 83: 22-26.

 

National Highway Traffic Safety Administration. 2018. Automated Vehicles for Safety. United States Department of Transportation.

 

Outay, F.; Kammoun, F.; Kaisser, F.; Atiquzzaman, M. 2017. Towards Safer Roads through Cooperative Hazard Awareness and Avoidance in Connected Vehicles. In Proceedings of the 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), 208-215.

 

Payre, W.; Cestac, J.; Delhomme, P. 2014. Intention to use a fully automated car: Attitudes and a priori acceptability, Transportation Research Part F: Traffic Psychology and Behaviour 27: 252-263.

 

Razaob, A.; Mansor, M.; Khamis, N.; Kassim, K. 2019. Willing of public to purchase and understanding of pedestrian AEB system in Malaysia. IOP Conference Series: Materials Science and Engineering, IOP Publishing, 012062.

 

Sahebi, S.; Nassiri, H. 2017. Assessing Public Acceptance of Connected Vehicle Systems in a New Scheme of Usage-Based Insurance, Transportation Research Record 2625: 62-69.

 

Sawtooth Software. 2009. ACBC Technical Paper. Sawtooth Software Technical Paper Series, 1-21.

 

Sayer, J. R.; Leblanc, D. J.; Mefford, M. L.; Devonshire, J. 2007. Field test results of a road departure crash warning system: driver acceptance, perceived utility and willingness to purchase. In Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 77-83.

 

Schoettle, B.; Sivak, M. 2014.A survey of public opinion about connected vehicles in the U.S., the U.K. and Australia. In Proceedings of the International Conference on Connected Vehicles and Expo (ICCVE), 687-692.

 

Sener, I. N.; Eluru, N.; Bhat, C. R. 2009. An analysis of bicycle route choice preferences in Texas, US. Transportation 36: 511-539.

 

Shabanpour, R.; Golshani, N.; Shamshiripour, A.; Mohammadian, A. 2018. Eliciting preferences for adoption of fully automated vehicles using best-worst analysis, Transportation Research Part C: Emerging Technologies 93: 463-478.

 

Shabanpour, R.; Mousavi, S. N. D.; Golshani, N.; Auld, J.; Mohammadian, A. 2017. Consumer preferences of electric and automated vehicles. In Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 716-720.

 

Shin, H. S.; Callow, M.; Dadvar, S.; Lee, Y. J.; Farkas, Z. A. 2015. User Acceptance and Willingness to Pay for Connected Vehicle Technologies: Adaptive Choice-Based Conjoint Analysis, Transportation Research Record 2531: 54-62.

 

Shin, H. S.; Callow, M.; Farkas, Z. A.; Lee, Y.J.; Dadvar, S. 2016. Measuring User Acceptance of and Willingness-to-Pay for CVI Technology. Connected Vehicle/Infrastructure University Transportation Center (CVI UTC).

 

Shin, H. S.; Farkas, Z. A.; Nickkar, A. 2019. An Analysis of Attributes of Electric Vehicle Owners' Travel and Purchasing Behavior: The Case of Maryland, International Conference on Transportation and Development, June 9–12, 2019, Alexandria, Virginia.

 

United States Census Bureau. 2017. U.S. Census Data and Statistics. Available from internet: https://data.census.gov/cedsci/.

 

Van Themsche, S. 2016. E-Mobility likely winners and losers. The Advent of Unmanned Electric Vehicles. Springer.

 

Wang, H.; You, F.; Chu, X.; Li, X.; Sun, X. 2019. Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City, IEEE Access 7: 20938-20949.

 

Zhang, Y.; Wu, C.; Qiao, C.; Hou, Y. 2019. The effects of warning characteristics on driver behavior in connected vehicles systems with missed warnings, Accident Analysis & Prevention 124: 138-145.

 

Zmud, J.; Sener, I. N.; Wagner, J. 2016a. Consumer acceptance and travel behavior: impacts of automated vehicles. Texas A&M Transportation Institute.

 

Zmud, J.; Sener, I. N.; Wagner, J. 2016b. Self-Driving Vehicles: Determinants of Adoption and Conditions of Usage, Transportation Research Record 2565: 57-64.


Quoted IJTTE Works

There is no quoted studies.



Related Keywords

ESTIMATION OF VALUE OF TIME OF TOLL ROAD USERS IN INDIA

FACTORS INFLUENCING THE ADOPTION OF ELECTRIC VEHICLE: THE CASE OF ELECTRIC MOTORCYCLE IN NORTHERN GHANA