The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP) model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use.

Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

This paper aims to explore the factors influencing passengers’ satisfaction toward bus transit and develop the improvement strategy for the small-medium city. Data including individual attributes, travel activity attributes, and perceived service attributes were collected in the city of Weinan, China. The ordered logit (OL) model and ordered Probit (OP) model were employed to explore the significant factors associated with passengers’ satisfaction toward bus transit. The odds ratio (OR) was applied to quantitatively measure the effects of the significant factors. Improving strategies of bus transit service were proposed based on the model results using the three-factor theory. Results show that the OL model outperforms the OP model. The age, daily average waiting time, perceived waiting time, transferring convenience, the attitude of the driver, intelligent travel information service, hygienic environment inside the bus, ticket price, bus route setting, and bus stop setting significantly affect the passengers’ satisfaction. Among them, the ticket price, perceived waiting time, bus stop setting, intelligent travel information service, transferring convenience, and bus route setting were identified as exciting factors. It is recommended that optimization of bus route and bus stop setting, building bus dedicated lanes, optimizing dynamic charging system, and providing intelligent travel information service could be effective strategies to improve passengers’ satisfaction toward bus transit in Chinese small-medium cities.