Research Article
Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model
Table 6
Marginal effects for the BP model.
| | Variable | E-bike involved crash | E-bike license plate use |
| | Gender (male versus female) | 0.177 | −0.285 | | Age group (Young versus older) | 0.124 | −0.442 | | Education level (high versus low) | −0.169 | 0.479 | | Holding automobile driver license (yes versus no) | −0.225 | 0.243 | | Type of e-bike (E-scooter versus e-bike) | 0.156 | — | | Car in household (yes versus no) | −0.165 | 0.137 | | Frequency of using e-bike (frequently versus occasionally) | 0.182 | — | | Experiences in using e-bike (3–5 years versus <1 year) | −0.203 | 0.267 | | Experiences in using e-bike (>5 years versus <1 year) | −0.164 | 0.221 | | Law compliance (strong versus no) | −0.425 | 0.433 | | Aggressive driving behaviors (frequently versus no) | 0.163 | −0.025 | | Aggressive driving behaviors (generally versus no) | 0.152 | — | | Impulse behavior (frequently versus no) | 0.144 | — | | Degree of riding experience (skilled versus new driver) | −0.107 | — | | Risk perception scale (high versus low) | −0.089 | — | | Registration fee (low versus high) | — | 0.146 | | Time for license registration (30 min versus >30 min) | — | 0.179 |
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— represents that the variable is not significant.
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