Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors?
Abstract
:1. Introduction
- -
- How are individuals’ perceptions and attitudes towards ADRT influenced by gender, age, education, employment, income, household size, residential location, and having a driver’s license?
2. Literature Background
3. Research Method
4. Descriptive Statistics
4.1. Socio-Demographic Characteristics
4.2. Attitudinal Characteristics
5. Findings and Implications for Transition to ADRT
5.1. Associations between AV Exposure and Socio-Demographic Predictor Variables
5.2. Associations between Attitudinal Characteristics and Socio-Demographic Predictor Variables
- Education and Awareness: Policymakers should implement education campaigns that explain how ADRT works, its benefits, and the safety measures put in place. Transparency about technology can help alleviate fears.
- Regulation and Standards: Policymakers should establish stringent standards and regulations for ADRT systems. This would not only ensure safety but also promote public confidence in the technology.
- Demonstrations and Trials: Public demonstrations or pilot programs can also help to increase public trust in ADRT. By seeing the technology in action and understanding its benefits first-hand, people might be more likely to trust and adopt it.
- Addressing Equity Concerns: A significant subset of the population that might be sceptical about ADRT could be those who worry about access and equity, particularly if they live in underserved areas or have limited mobility. Policymakers need to assure these communities that ADRT will be accessible and affordable to all, not just a privileged few.
- Stakeholder Involvement: Involving different stakeholders in the policymaking process can also build trust. This could include public forums or consultations where citizens can express their views and contribute to decision-making about ADRT.
- Data Privacy and Security Measures: Given the digital nature of ADRT, data privacy and cybersecurity are crucial. Policymakers should define clear guidelines to protect user data and ensure that robust cybersecurity measures are in place.
6. Conclusions
7. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Response Variable Model | Omnibus Sig. | Predictor Variable | Std. Error | Wald Sig. | OR | OR 95% Wald C.I. | |
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
AV Knowledge | 0.038 | Employment | 0.140 | 0.038 | 1.335 | 1.015 | 1.755 |
AV Experience | 0.002 | Gender (male) | 0.408 | 0.018 | 2.635 | 1.184 | 5.860 |
Household Inc. | 0.142 | 0.015 | 1.411 | 1.069 | 1.861 |
Response Variable Model | Omnibus Sig. | Parallel Lines Sig. | Predictor Variable | Std. Error | Wald Sig. | OR | OR 95% Wald C.I. | |
---|---|---|---|---|---|---|---|---|
Lower | Upper | |||||||
Perceived opportunities of ASBs | ||||||||
More efficient | 0.000 | 0.541 | Age | 0.087 | 0.001 | 0.742 | 0.626 | 0.881 |
Household Inc. | 0.066 | 0.032 | 1.153 | 1.011 | 1.315 | |||
Household Inc. | 0.068 | 0.001 | 1.263 | 1.106 | 1.442 | |||
Less congestion and emissions | 0.001 | 0.493 | Age | 0.088 | 0.001 | 0.757 | 0.637 | 0.899 |
Fewer driver errors | 0.003 | 0.153 | Age | 0.087 | 0.004 | 0.780 | 0.658 | 0.924 |
Easy to learn how to interact/travel | 0.000 | 0.783 | Age | 0.088 | 0.003 | 0.770 | 0.647 | 0.915 |
Household Inc. | 0.068 | 0.001 | 1.263 | 1.106 | 1.442 | |||
Safer | 0.007 | 0.052 | Age | 0.086 | 0.008 | 0.796 | 0.674 | 0.939 |
More attractive | 0.000 | 0.131 | Age | 0.098 | 0.000 | 0.709 | 0.584 | 0.861 |
Employment | 0.138 | 0.036 | 1.337 | 1.014 | 1.762 | |||
Drivers Lic (yes) | 0.331 | 0.009 | 0.423 | 0.221 | 0.809 | |||
Age | 0.098 | 0.000 | 0.690 | 0.570 | 0.834 | |||
Household Inc. | 0.068 | 0.024 | 1.165 | 1.020 | 1.331 | |||
More positive attitude | 0.000 | 0.124 | Gender (male) | 0.247 | 0.040 | 1.660 | 1.018 | 2.708 |
Drivers Lic (yes) | 0.331 | 0.009 | 0.423 | 0.221 | 0.809 | |||
Age | 0.098 | 0.000 | 0.690 | 0.570 | 0.834 | |||
Household Inc. | 0.068 | 0.024 | 1.165 | 1.020 | 1.331 | |||
Perceived challenges of ASBs | ||||||||
Higher fare | 0.008 | 0.529 | Res Location (peri-urban) | 0.228 | 0.044 | 1.584 | 1.012 | 2.479 |
Education | 0.136 | 0.031 | 0.746 | 0.571 | 0.973 | |||
Traffic accidents | 0.003 | 0.472 | Gender (male) | 0.227 | 0.028 | 1.647 | 1.055 | 2.570 |
Drivers Lic (yes) | 0.327 | 0.037 | 0.506 | 0.267 | 0.959 |
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Golbabaei, F.; Yigitcanlar, T.; Paz, A.; Bunker, J. Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors? Sustainability 2023, 15, 11839. https://doi.org/10.3390/su151511839
Golbabaei F, Yigitcanlar T, Paz A, Bunker J. Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors? Sustainability. 2023; 15(15):11839. https://doi.org/10.3390/su151511839
Chicago/Turabian StyleGolbabaei, Fahimeh, Tan Yigitcanlar, Alexander Paz, and Jonathan Bunker. 2023. "Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors?" Sustainability 15, no. 15: 11839. https://doi.org/10.3390/su151511839