Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity
Abstract
:1. Introduction
2. Related Works
2.1. Mode Choice Model Considering Preference Heterogeneity
2.2. Psychological Factors
2.3. Research Gap
- The ICLV model incorporates the mixed logit model as a discrete choice model to account for various sources of heterogeneity among respondents. Through this approach, this study systematically explores the relationship between socio-demographic characteristics and SPT choice behavior, dividing it into two aspects: variance of alternative attributes and respondent travel and low-carbon attitudes.
- By analyzing changes in choice behavior resulting from improvements in alternative attributes and respondent group attributes, this study suggests policy implications for enhancing the acceptability of SPT alternatives.
3. Methodology
3.1. Study Site and Sample
3.2. Questionnaire Design
- Include five-point Likert scale questions about respondents’ psychological factors, such as travel attitudes and low-carbon attitudes.
- Experiment with respondents’ stated choices among alternatives—car (CAR), public transit (PT), and Sustainable Public Transportation (SPT) —in a given situation.
- Collect respondents’ socio-demographic characteristics, including gender, age, household members, income, etc.
3.2.1. Psychological Measurement Instruments
3.2.2. Stated Choice Experiment Design
3.3. Modelling Approach
3.3.1. Latent Variable Model
3.3.2. Discrete Choice Model
4. Results
4.1. Data and Prelimanary Analysis
4.1.1. Respondents Profile
4.1.2. Latent Variable Perception
4.2. Model Results
4.2.1. Estimated Results of the ICLV Model
4.2.2. Scenario Analysis
- Improvements in SPT attributes
- Changes in attitudes
5. Discussion and Implications
- Heterogeneity in Alternative Attributes: This study identifies significant heterogeneity among respondents in sensitivity to travel costs, with larger households showing greater sensitivity. In contrast, travel time and CO2 emissions were estimated as fixed parameters, with CO2 emissions not showing statistical significance.
- Influence of Latent Variables: The findings reveal that older age groups tend to resist transfers and exhibit lower levels of low-carbon knowledge and habits. Respondents with larger household sizes or monthly incomes of KRW 3 million or above demonstrate a lower importance on punctuality and higher levels of low-carbon-related attributes. Participants living with preschool-aged children and residing in larger households are more likely to choose SPT.
- CO2 Emissions Perception: This study finds no statistically significant link between commuters’ preferred travel mode and varying levels of CO2 emissions, suggesting that respondents may have difficulty perceiving CO2 emissions compared to travel time and cost. For instance, the study does not clarify the monetary value of reducing 100 g of CO2 emissions or its influence on transportation mode choice. This underscores the need to provide SPT users with comprehensive information to help them evaluate the value of CO2 emission reductions. Therefore, it is crucial to demonstrate how SPT can reduce CO2 emissions compared to existing car or public transit options and highlight the economic benefits to encourage a shift in travel mode.
- Expanding Eco-friendly Public Transport: Expanding eco-friendly public transport options, such as electric buses, and implementing a transfer discount system across various modes of transport may help reduce travel times and increase the mode share for SPT. Reducing SPT travel time by up to 85% could increase the mode share by approximately 18%, as shorter service intervals would make these options more attractive. Additionally, reducing travel costs to 85% of the current level could increase the mode share for SPT by 7.3%. Establishing governance between private sectors, such as shared mobility operators, and public transit authorities is recommended, along with a deliberative process regarding policy instruments like the intermodal transfer discount system. Government subsidies may also be necessary for implementation.
- Educational Programs on Low-carbon Knowledge: This study shows that low-carbon knowledge positively influences low-carbon habit attitudes, indicating a need for educational programs focused on low-carbon knowledge. Knowledge is essential for behavior [75], particularly in environmental awareness, which positively impacts eco-friendly behaviors like responsible actions and green product purchases [75,76,77]. Campaigns and events should be conducted to enhance low-carbon knowledge. It is important to tailor attitude change strategies to specific socio-demographic groups, as changes in attitudes lead to varying mode share adaptations for SPT based on group characteristics.
6. Conclusions and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Attributes | Alternatives | ||
---|---|---|---|
Car | PT | SPT | |
Travel time (minutes) | 43/48/53 | 61/68/75 | 61/68/75 |
Travel cost (KRW) | 6669/7410/8151 | 1450/1550/1650 | 1450/2350/3250 |
CO2 emissions(g) | 3330/3700/4070 | 945/1050/1155 | 0/467/934 |
Characteristics | Frequency | Percentage | |
---|---|---|---|
Gender | Male | 66 | 55.5% |
Female | 53 | 44.5% | |
Age | 20–29 | 28 | 23.5% |
30–39 | 27 | 22.7% | |
40–49 | 34 | 28.6% | |
Above 50 | 30 | 25.2% | |
Education | Middle school or below | 1 | 0.8% |
High school or below | 13 | 10.9% | |
Undergraduate | 11 | 9.2% | |
Graduate | 94 | 79.0% | |
Monthly Income | Less than KRW 1 million | 3 | 2.5% |
KRW 1~3 million | 38 | 31.9% | |
KRW 3~6 million | 51 | 42.9% | |
KRW 6~10 million | 23 | 19.3% | |
More than KRW 10 million | 4 | 3.4% | |
Household-size | 1 | 21 | 17.6% |
2 | 21 | 17.6% | |
3 | 35 | 29.4% | |
4 | 37 | 31.1% | |
More than 5 | 5 | 4.2% | |
Preschooler in household | Yes | 34 | 28.6% |
No | 85 | 71.4% |
Characteristics | Indicators | Mean | S.D. |
---|---|---|---|
Preference of transfer between travel mode (PR) | PR1. I am willing to choose a route that combines multiple travel modes to optimize my route | 3.73 | 1.01 |
PR2. It is not a burden for me to transfer between travel modes | 3.35 | 1.16 | |
Importance of punctuality (IP) | IP1. It is important that I arrive on time to appointments | 4.41 | 0.74 |
IP2. I try to avoid travel modes that may involve delays | 4.30 | 0.78 | |
IP3. I leave home in advance to ensure I will arrive on time and as comfortable as possible | 4.23 | 0.76 | |
Low-carbon knowledge (LK) | LK1. Promoting the use of new energy vehicles can save resources and reduce carbon emissions | 3.79 | 0.78 |
LK2. Saving energy and reducing carbon emissions are necessary to improve the environment | 4.03 | 0.74 | |
LK3. Vehicle emissions are an important cause of environmental pollution | 4.01 | 0.83 | |
Low-carbon habits (LH) | LH1. I am willing to save energy in everyday life, reducing carbon emissions | 3.66 | 0.86 |
LH2. I am willing to purchase products from companies that strive to protect the environment | 3.38 | 0.84 | |
LH3. I am willing to pay extra for low-carbon transport services | 3.15 | 1.01 | |
LH4. I am willing to change my habits to reduce carbon emissions | 3.71 | 0.82 |
Latent Variables | Indicators | Standardized Factor Loading | Cronbach’s |
---|---|---|---|
PT | PT 1 | 0.622 | 0.752 |
PT 2 | 0.938 | ||
IP | IP 1 | 0.753 | 0.818 |
IP 2 | 0.753 | ||
IP 3 | 0.751 | ||
LK | LK 1 | 0.746 | 0.800 |
LK 2 | 0.735 | ||
LK 3 | 0.666 | ||
LH | LH 1 | 0.518 | 0.787 |
LH 2 | 0.773 | ||
LH 3 | 0.721 | ||
LH 4 | 0.579 | ||
KMO measure of sampling adequacy | 0.784 |
Latent Variables | Estimate | t-Value | |||
---|---|---|---|---|---|
TR | Structural | Aged 40 and over | −0.976 | * | −1.835 |
Preschooler | −0.978 | −0.809 | |||
Household size | 0.937 | ** | 2.638 | ||
Income KRW 3 million and over | −0.602 | −1.070 | |||
2.370 | ** | 2.189 | |||
Measurement | TR 1 | 1.000 | - | ||
TR 2 | 0.807 | ** | 5.294 | ||
IP | Structural | Aged 40 and over | −0.015 | −0.055 | |
Preschooler | −0.797 | * | −1.895 | ||
Household size | 0.544 | ** | 3.454 | ||
Income KRW 3 million and over | 0.872 | * | 1.751 | ||
0.532 | 0.541 | ||||
Measurement | IP 1 | 1.000 | - | ||
IP 2 | 0.775 | ** | 5.725 | ||
IP 3 | 0.819 | ** | 5.744 | ||
LK | Structural | Aged 40 and over | −0.172 | * | −1.924 |
Preschooler | −0.473 | −0.799 | |||
Household size | 0.468 | ** | 3.320 | ||
Income KRW 3 million and over | 0.687 | ** | 1.850 | ||
0.093 | 0.564 | ||||
Measurement | LK 1 | 1.000 | - | ||
LK 2 | 0.928 | ** | 6.013 | ||
LK 3 | 1.000 | ** | 5.897 | ||
LH | Structural | Aged 40 and over | −0.456 | ** | −2.158 |
Preschooler | −0.565 | −0.643 | |||
Household size | 0.704 | ** | 2.990 | ||
Income KRW 3 million and over | 0.815 | ** | 2.090 | ||
LK | 0.351 | ** | 3.103 | ||
0.717 | ** | 3.164 | |||
Measurement | LH 1 | 1.000 | - | ||
LH 2 | 1.224 | ** | 4.379 | ||
LH 3 | 1.171 | ** | 4.362 | ||
LH 4 | 1.247 | ** | 4.566 |
Latent Variables | Public Transit | Sustainable Public Transit | ||||||
---|---|---|---|---|---|---|---|---|
Estimate | t-Value | Estimate | t-Value | |||||
Constant (ASC) | −1.224 | * | −1.827 | −0.976 | * | −1.835 | ||
Alternative specific variables | Travel time | −0.139 | ** | −4.288 | −0.139 | ** | −4.288 | |
Travel costRN | mean | −6.620 | ** | −16.889 | −6.620 | ** | −16.889 | |
std. dev. | 1.190 | ** | 4.367 | 1.190 | ** | 4.367 | ||
CO2 emissions | −2 × 10−4 | −0.379 | −2 × 10−4 | −0.379 | ||||
Context variables | Walking time to nearest subway station from home | −0.041 | ** | −2.470 | −0.024 | ** | −2.131 | |
Latent variables | PR | 0.224 | * | 1.931 | 0.353 | ** | 2.272 | |
IP | −0.776 | ** | −2.303 | −1.342 | ** | −1.997 | ||
LK | 0.084 | * | 1.784 | 0.222 | ** | 2.047 | ||
LH | 0.240 | * | 1.708 | 0.470 | * | 1.969 | ||
) | −1.118 | ** | −6.609 | −1.118 | ** | −6.609 | ||
Random parameter heterogeneity | Aged 40 and over | 0.086 | 0.870 | 0.086 | 0.870 | |||
Preschooler | −1.582 | ** | −2.040 | −1.582 | ** | −2.040 | ||
Household size | 0.243 | * | 1.735 | 0.243 | * | 1.735 | ||
Income KRW 3 million and over | −0.618 | * | −1.687 | −0.618 | * | −1.687 | ||
LL(Initial) | −3584.799 | |||||||
LL(Final) | −1593.135 | |||||||
0.556 | ||||||||
0.535 |
Variables | PR | IP | LK | LH | Latent Variables | Travel Cost | Total | |
---|---|---|---|---|---|---|---|---|
Aged 40 and over | PT | −0.219 | - | −0.014 | −0.124 | −0.357 | - | −0.357 |
SPT | −0.344 | - | −0.038 | −0.243 | −0.625 | - | −0.625 | |
Preschooler | PT | - | 0.618 | - | - | 0.618 | −0.206 | 0.413 |
SPT | - | 1.070 | - | - | 1.070 | −0.206 | 0.865 | |
Household size | PT | 0.210 | −0.422 | 0.039 | 0.208 | 0.036 | −1.275 | −1.240 |
SPT | 0.330 | −0.730 | 0.104 | 0.408 | 0.112 | −1.275 | −1.163 | |
Income KRW 3 million and over | PT | - | −0.676 | 0.058 | 0.254 | −0.365 | −0.539 | −0.904 |
SPT | - | −1.170 | 0.152 | 0.496 | −0.522 | −0.539 | −1.061 |
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Lee, G.; Kim, S.; Koo, J.; Choo, S. Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity. Sustainability 2024, 16, 7924. https://doi.org/10.3390/su16187924
Lee G, Kim S, Koo J, Choo S. Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity. Sustainability. 2024; 16(18):7924. https://doi.org/10.3390/su16187924
Chicago/Turabian StyleLee, Gyeongjae, Sujae Kim, Jahun Koo, and Sangho Choo. 2024. "Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity" Sustainability 16, no. 18: 7924. https://doi.org/10.3390/su16187924