Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China
Abstract
:1. Introduction
2. Literature Review
2.1. The Effects of COVID-19 on Transportation and Commute Mode
2.2. Discrete Models of Travel Behavior
3. Data and Methods
3.1. Data
3.2. Method
4. Results and Discussion
4.1. Results
- Wave 1
- Wave 2
4.2. Discussion
- Comparison within Two Waves
- Comparison between Two Waves
5. Conclusions and Future Research
- Age, income, household composition, and the frequency of use of travel modes are significant latent factors that impact the respondents’ attitude toward the mass transit nest and the auto nest under the impact of the COVID-19 pandemic. In wave 1, car owners were almost all car-oriented users and they showed less anxiety regarding public transport since they did not change their daily mobility pattern and the virus crisis from mass transit would not significantly affect their travel mode choice. Younger, middle- and low-income respondents living in a household with fewer family members, and public transport-dominated users might overlook the health risks on board and show less aversion emotion regarding mass transit. In wave 2, car owners may not be car-oriented commuters and they are more sensitive to both transit and auto nests, since they have substitutes for their current mode. Carless individuals usually take mass transit for work trips and keep a more positive attitude toward public transport, which is different from that in other developing countries [56]. In line with the reported findings in previous studies [57], older adults living in a big family are inclined to pay more attention to the risk of the infection, while it is inadequate to change their commute mode.
- Against the backdrop of controlling the spread of the disease, individuals’ trepidation regarding the infection risk gradually faded, but it was still a critical consideration in terms of travel mode choice. More individuals would prefer to take the health issue into account for commute mobility patterns and they are willing to pay more to improve the mass transit service in wave 1 than wave 2.
- The economic factor is a foundational base for the intention of car purchase. The pandemic enables researchers to stimulate the desire of buying a vehicle to some extent, but this is not the uppermost consideration in wave 2. On the contrary, due to the impact of the pandemic on the national economy and employment market, the individuals in wave 1 may not show a great demand for private cars.
- In light of economic reinvigoration and the increase in car ownership, urban traffic is faced with a great challenge but still remains in dynamic equilibrium. Since a large number of citizens are willing to take public transport to go to work even if they are car owners, mass transit is still the mainstream mode for commuters in the post-COVID-19 era.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Attributes | Code | Description | Levels | |
---|---|---|---|---|
In-vehicle travel time (TR) | Log(ITT)TR | In-vehicle travel time using the transit mode | Scenario 1 | 20, 30, 40 (min) |
Scenario 2 | 30, 40, 50 (min) | |||
Scenario 3 | 45, 55, 65 (min) | |||
Travel cost (TR) | Log(TC)TR | Travel cost for the transit mode | Scenario 1 | Bus: 1, 2 (CNY) Metro: 2, 3 (CNY) |
Scenario 2 | Bus: 1, 2 Metro: 3, 5 (CNY) | |||
Scenario 3 | Bus: 1, 2 Metro: 4, 6 (CNY) | |||
Out-of-vehicle travel time (TR) | OTTTR | Out-of-vehicle travel time of the transit includes walking time from the origin to the bus or metro station and wait time at the bus/metro station | - | 5, 10, 15, 20 (min) |
In-vehicle travel time (AU) | Log(ITT)AU | In-vehicle travel time using the auto mode | Scenario 1 | 10, 15, 20 (min) |
Scenario 2 | 15, 20, 25 (min) | |||
Scenario 3 | 30, 35, 40 (min) | |||
Travel cost (AU) | Log(TC)AU | Travel time for the auto mode | Scenario 1 | 15, 20, 25 (CNY) |
Scenario 2 | 20, 25, 30 (CNY) | |||
Scenario 3 | 35, 40, 45 (CNY) | |||
Out-of-vehicle travel time (AU) | OTTAU | Out-of-vehicle travel time of the transit includes walking time from the origin to the garage or parking lot, or wait time for taxi/ride-hailing | - | 2, 6, 10, 14 (min) |
Percentage of the passenger-carrying capacity (PC) | PCTR | Percentage of the passenger-carrying capacity of the transit mode | - | 30%, 50%, 80% |
Variables | Category | Wave 1 | Wave 2 |
---|---|---|---|
Gender | Male | 46.67% | 56.29% |
Female | 53.33% | 43.71% | |
Age | 18–25 | 17.27% | 23.90% |
25–40 | 57.58% | 34.91% | |
40–55 | 12.73% | 31.13% | |
>55 | 12.42% | 10.06% | |
Educational level | High school, technical school, or below | 18.79% | 27.99% |
Junior college | 16.06% | 36.79% | |
Bachelor’s degree | 35.45% | 26.10% | |
Master’s degree or higher | 29.70% | 9.12% | |
Monthly income (CNY) | <¥3000 | 20.61% | 10.69% |
¥3001–¥5000 | 28.18% | 26.42% | |
¥5001–¥7000 | 19.39% | 34.59% | |
>¥7000 | 31.82% | 28.30% | |
Household composition | Live alone | 23.64% | 21.70% |
Couple | 28.79% | 38.68% | |
Two generations | 37.27% | 24.53% | |
Three generations | 10.30% | 15.09% | |
Car ownership | Yes | 57.58% | 53.77% |
No | 42.42% | 46.23% | |
Commute travel mode | Walk | 15.76% | 18.87% |
Bus | 10.61% | 23.58% | |
Metro | 8.18% | 22.64% | |
Taxi/ride-hailing | 5.15% | 14.47% | |
Private automobile | 45.15% | 12.58% | |
Bicycle/electric bike | 15.15% | 7.86% | |
Entertainment travel mode | Walk | 16.06% | 22.64% |
Bus | 9.09% | 29.87% | |
Metro | 10.00% | 13.52% | |
Taxi/ride-hailing | 11.52% | 12.58% | |
Private automobile | 40.91% | 9.12% | |
Bicycle/electric bike | 12.42% | 12.26% |
Classes | WAVE 1 | WAVE 2 | ||||||
---|---|---|---|---|---|---|---|---|
Number of Parameters | Log-Likelihood | AIC | BIC | Number of Parameters | Log-Likelihood | AIC | BIC | |
2 | 43 | −5049.00 | 10,184.02 | 10,471.63 | 43 | −7768.90 | 15,623.81 | 15,909.86 |
3 | 86 | −4619.90 | 9393.83 | 9908.84 | 86 | −7728.10 | 15,610.11 | 16,122.34 |
4 | 123 | −4281.10 | 8784.25 | 9526.67 | 123 | −7729.43 | 15,703.66 | 16,442.08 |
5 | 145 | −4355.15 | 8932.56 | 9768.15 | 145 | −7738.40 | 15,766.71 | 16,731.31 |
6 | 179 | −4708.01 | 9214.015 | 10,276.27 | 179 | −7737.10 | 15,832.11 | 17,022.89 |
7 | 213 | −4722.46 | 9678.926 | 10,461.59 | 213 | −7742.40 | 15,910.89 | 17,327.85 |
Parameters | Class 1 | Class 2 | Class 3 | Class 4 | ||||
---|---|---|---|---|---|---|---|---|
Class-Membership Model | Value | t-Stat. | Value | t-Stat. | Value | t-Stat. | Value | t-Stat. |
ASC_Class | 3.883 | 16.128 | 3.236 | 12.867 | 2.483 | 9.907 | ||
Male | 0.227 | 2.334 | 0.219 | 2.231 | 0.161 | 1.500 | ||
Female | −0.227 | −0.219 | −0.161 | |||||
Age (18–25) | 0.366 | 1.752 | −0.028 | −0.130 | −0.204 | −0.850 | ||
Age (25–40) | 0.234 | 2.397 | 0.399 | 2.382 | 0.247 | 1.347 | ||
Age (40–55) | −0.813 | −4.652 | −0.940 | −5.288 | −0.357 | −1.756 | ||
Age (>55) | 0.214 | 0.568 | 0.314 | |||||
Education (High school, technical school, or below) | −1.012 | −5.466 | −0.476 | −2.547 | −2.120 | −8.410 | ||
Education (Junior college) | 0.108 | 0.599 | 0.190 | 1.085 | 0.642 | 3.143 | ||
Education (Bachelor’s) | −0.758 | −4.639 | −1.048 | −6.351 | −0.236 | −2.303 | ||
Education (Master’s or higher) | 1.662 | 1.334 | 1.715 | |||||
Income (<3000) | −0.708 | −4.765 | −0.989 | −6.414 | −1.142 | −6.527 | ||
Income (3001–5000) | 0.186 | 1.262 | −0.062 | −0.414 | −0.288 | −1.773 | ||
Income (5001–7000) | 0.521 | 2.628 | 0.933 | 4.671 | 1.159 | 5.523 | ||
Income (>7000) | 0.002 | 0.118 | 0.270 | |||||
Household (live alone) | −0.606 | −3.283 | −0.705 | −3.787 | −0.735 | −3.682 | ||
Household (couple) | 0.789 | 4.566 | 0.568 | 3.251 | 0.293 | 1.530 | ||
Household (two generations) | 0.670 | 4.358 | 0.484 | 3.100 | 0.415 | 2.490 | ||
Household (three generations) | −0.852 | −0.346 | 0.027 | |||||
Car ownership (Yes) | −0.488 | 4.977 | 0.899 | 6.705 | −0.668 | 3.359 | ||
Car ownership (No) | 0.488 | −0.899 | 0.668 | |||||
Commute mode (Walk) | −0.879 | −3.829 | −0.378 | −1.599 | 0.215 | 0.893 | ||
Commute mode (Bus) | 0.988 | 1.591 | −0.652 | −2.191 | −0.908 | −2.943 | ||
Commute mode (Metro) | 1.001 | 2.739 | 0.665 | 1.064 | 1.427 | 2.427 | ||
Commute mode (Taxi/ride-hailing) | 0.417 | 1.287 | −0.928 | −1.042 | 2.332 | 1.730 | ||
Commute mode (Private car) | −2.341 | −2.692 | 3.128 | 2.973 | −0.658 | −2.253 | ||
Commute mode (Bike/electric bike) | 0.814 | −1.835 | −2.408 | |||||
Entertainment mode (Walk) | −2.101 | −7.775 | −0.378 | −9.093 | 0.615 | −8.276 | ||
Entertainment mode (Bus) | 1.029 | −4.422 | −0.652 | −6.328 | −0.508 | −2.931 | ||
Entertainment mode (Metro) | 1.034 | −0.123 | −0.665 | −3.785 | 1.427 | −4.625 | ||
Entertainment mode (Taxi/ride-hailing) | 1.536 | 3.818 | −0.928 | −3.230 | 1.332 | 6.442 | ||
Entertainment mode (Private car) | −2.366 | −1.333 | 4.258 | 3.128 | −0.458 | −2.248 | ||
Entertainment mode (Bike/electric bike) | 0.868 | −1.635 | −2.408 | |||||
Class-specific model | ||||||||
Constant (metro) | 0.035 | 0.023 | 0.662 | 4.003 | −0.073 | −3.193 | 0.725 | 2.088 |
Constant (taxi/ride-hailing) | −0.842 | 0.085 | −0.887 | 5.125 | −2.545 | 3.312 | 1.101 | 1.988 |
Constant (private car) | 0.745 | 1.243 | −0.161 | −2.112 | 2.575 | 3.112 | −0.249 | 2.105 |
Log(ITT)AU | −1.249 | −0.850 | −0.114 | −5.525 | −0.117 | −3.047 | −0.091 | −2.249 |
Log(ITT)TR | 0.155 | −0.977 | −0.141 | −4.971 | −0.166 | −2.100 | −1.190 | −2.460 |
Log(TC)AU | −1.246 | −1.882 | −0.127 | −1.825 | −0.141 | −3.110 | −0.119 | −2.246 |
Log(TC)TR | −0.120 | −0.770 | −0.107 | −1.961 | −0.093 | −2.984 | −0.080 | −2.418 |
OTTTR = 5 | 0.369 | 0.864 | 0.327 | 2.484 | 1.538 | 1.862 | 0.283 | 3.186 |
OTTTR = 10 | 0.004 | 1.569 | 0.144 | 1.982 | −0.237 | 1.977 | 0.095 | 1.874 |
OTTTR = 15 | −0.310 | −1.255 | −0.122 | 2.103 | −0.485 | −2.107 | −0.105 | −2.362 |
OTTTR = 20 | −0.464 | −0.350 | −0.815 | −0.273 | ||||
OTTAU = 2 | 0.441 | 0.711 | −3.516 | 1.517 | 5.141 | 0.583 | 1.987 | |
OTTAU = 6 | −0.115 | −0.219 | 1.968 | 0.980 | 3.121 | 0.148 | 2.336 | |
OTTAU = 10 | −0.142 | −0.228 | −2.361 | −0.672 | −3.100 | −0.336 | −3.155 | |
OTTAU = 14 | −0.184 | −0.264 | −1.824 | −0.394 | ||||
PCTR = 30% | 0.160 | 0.366 | 0.575 | 2.001 | 0.388 | 1.644 | 0.553 | 1.743 |
PCTR = 50% | −0.035 | −0.156 | 0.034 | 2.211 | −0.058 | 1.821 | −0.081 | 1.781 |
PCTR = 80% | −0.125 | −0.608 | −0.332 | −0.472 | ||||
Model statistics | ||||||||
Class size | 4.26% | 38.60% | 44.68% | 12.46% | ||||
Number of observations | 5934 | |||||||
Covergent log-likelihood | −4281.1 | |||||||
Pseudo R-squared | 0.2878 |
Parameters | Class 1 | Class 2 | ||
---|---|---|---|---|
Class-Membership Model | Value | t-Stat. | Value | t-Stat. |
ASC_Class | −0.1827 | −1.984 | ||
Male | −0.253 | −2.991 | ||
Female | 0.253 | |||
Age (18–25) | 0.002 | 4.759 | ||
Age (25–40) | −0.027 | −2.276 | ||
Age (40–55) | −0.330 | −2.504 | ||
Age (>55) | 0.354 | |||
Education (High school, technical school, or below) | −0.617 | −4.003 | ||
Education (Junior college) | −0.150 | −0.464 | ||
Education (Bachelor) | −0.010 | −0.057 | ||
Education (Master’s or higher) | 0.778 | |||
Income (<3000) | −0.410 | −1.168 | ||
Income (3001–5000) | 0.406 | 0.386 | ||
Income (5001–7000) | −0.045 | 1.977 | ||
Income (>7000) | 0.049 | |||
Household (live alone) | −0.218 | −1.963 | ||
Household (couple) | −0.213 | −2.643 | ||
Household (Two generations) | −0.391 | 1.751 | ||
Household (Three generations) | 0.822 | |||
Car ownership (Yes) | −0.754 | −2.134 | ||
Car ownership (No) | 0.754 | |||
Commute mode (Walk) | −0.052 | −1.042 | ||
Commute mode (Bus) | −0.212 | −3.163 | ||
Commute mode (Metro) | 0.387 | −1.758 | ||
Commute mode (Taxi/ride-hailing) | 0.308 | 0.016 | ||
Commute mode (Private car) | −0.229 | 0.444 | ||
Commute mode (Bike/electric bike) | −0.202 | |||
Entertainment mode (Walk) | 0.196 | 0.687 | ||
Entertainment mode (Bus) | 0.752 | 1.025 | ||
Entertainment mode (Metro) | −0.185 | −2.874 | ||
Entertainment mode (Taxi/ride-hailing) | −0.172 | 0.534 | ||
Entertainment mode (Private car) | −0.328 | −2.387 | ||
Entertainment mode (Bike/electric bike) | −0.263 | |||
Class-specific model | ||||
Constant (metro) | 0.235 | 2.679 | 0.384 | 2.986 |
Constant (taxi/ride-hailing) | 0.283 | 1.969 | −0.004 | 1.961 |
Constant (private car) | −0.374 | −2.652 | −0.053 | 2.661 |
Log(ITT)AU | −0.435 | −1.987 | −0.131 | −2.512 |
Log(ITT)TR | −0.271 | −2.330 | −0.025 | 3.174 |
Log(TC)AU | −0.329 | −2.089 | −0.260 | −2.016 |
Log(TC)TR | −1.945 | −1.981 | −0.701 | −2.025 |
OTTTR =5 | 2.079 | 2.661 | 2.190 | −1.841 |
OTTTR = 10 | 0.162 | 2.256 | 0.801 | −1.996 |
OTTTR = 15 | 0.046 | 2.455 | −1.490 | −2.103 |
OTTTR = 20 | −2.286 | −1.501 | ||
OTTAU = 2 | 0.522 | 1.997 | 1.844 | 3.254 |
OTTAU = 6 | 0.426 | 2.164 | 0.624 | 3.111 |
OTTAU = 10 | −0.340 | −3.127 | 0.919 | 2.630 |
OTTAU = 14 | −0.608 | −3.387 | ||
PCTR = 30% | 1.411 | 2.365 | 0.942 | 2.001 |
PCTR = 50% | −0.504 | −3.682 | 0.138 | 2.211 |
PCTR = 80% | −0.907 | −1.081 | ||
Model statistics | ||||
Class size | 60.69% | 39.31% | ||
Number of observations | 5724 | |||
Convergent log-likelihood | −7768.9 | |||
Pseudo R-squared | 0.267 |
Gender | Age | Educational Level | ||
Class 1 | ||||
Class 2 | ||||
Car ownership | Income | Household composition | ||
Class 1 | ||||
Class 2 | ||||
Commute travel mode | Entertainment travel mode | |||
Class 1 | ||||
Class 2 |
Wave 1 | Wave 2 | ||||
---|---|---|---|---|---|
Transit | Class 2 | Class 3 | Class 4 | Class 1 | Class 2 |
ITT | −1.318 | −1.785 | −2.388 | −0.139 | −0.036 |
PC = 30% | 5.374 | 4.172 | 6.913 | 0.725 | 1.344 |
PC = 50% | 0.318 | −0.624 | −1.013 | −0.259 | 0.197 |
PC = 80% | −5.682 | −3.570 | −5.900 | −0.466 | −1.542 |
OTT = 5 min | 3.056 | 16.538 | 3.538 | 1.069 | 3.124 |
OTT = 10 min | 1.346 | −2.548 | 1.188 | 0.083 | 1.143 |
OTT = 15 min | −1.140 | −5.215 | −1.313 | 0.024 | −2.126 |
OTT = 20 min | −3.271 | 3.516 | −3.413 | −1.175 | −2.141 |
Auto | Class 2 | Class 3 | Class 4 | Class 1 | Class 2 |
ITT | −0.898 | −0.830 | −0.765 | −1.322 | −0.504 |
OTT = 2 min | 5.598 | 10.759 | 4.899 | 1.587 | 7.092 |
OTT = 6 min | −1.724 | 6.950 | 1.244 | 1.295 | 2.400 |
OTT = 10 min | −1.795 | −4.766 | −2.824 | −1.033 | 3.535 |
OTT = 14 min | −2.079 | −12.936 | −3.311 | −1.848 | −13.027 |
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Luan, S.; Yang, Q.; Jiang, Z.; Zhou, H.; Meng, F. Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China. Int. J. Environ. Res. Public Health 2022, 19, 5076. https://doi.org/10.3390/ijerph19095076
Luan S, Yang Q, Jiang Z, Zhou H, Meng F. Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China. International Journal of Environmental Research and Public Health. 2022; 19(9):5076. https://doi.org/10.3390/ijerph19095076
Chicago/Turabian StyleLuan, Siliang, Qingfang Yang, Zhongtai Jiang, Huxing Zhou, and Fanyun Meng. 2022. "Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China" International Journal of Environmental Research and Public Health 19, no. 9: 5076. https://doi.org/10.3390/ijerph19095076