Emerging electric carsharing (EC) systems have demonstrated their advantages and attracted public attention. The number of EC systems is growing throughout the world, especially in metropolitan areas in developing countries. For successful implementation, developers need to understand the public acceptance of EC services. In this study, we sought to determine the factors that affect EC acceptance in the context of developing countries. The study involved 437 individuals, aged between 18 and 65 years, who were randomly sampled from an EC service area in China. The determinants of EC acceptance were investigated based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) and tested by using Structural Equation Modeling. The results indicated that hedonic motivation (HM) has a powerful effect on behavioral intention (BI) to use the EC service in the future. Performance expectancy (PE), effort expectancy (EE), and familiarity with the carsharing concept (FM) also influenced EC’s acceptance. However, the impact of social influence (SI) did not emerge from this study. The results also revealed that gender moderates the effects of EE and FM on BI. Age moderated the effect of FM on BI and unexpectedly moderated the impact of HM on BI. The present study confirmed the validity of the UTAUT research model in predicting the intention to use an EC system in developing countries. Implications and recommendations for government and EC developers are also discussed.

Understanding travelers’ acceptance of Advanced Traveler Information Systems (ATIS) is crucial to the implementation of Intelligent Transportation Systems (ITS) capable of mitigating traffic congestion and improving network performance. This paper adopted an extended Technology Acceptance Model (TAM) to predict and explain road users’ intention to use Variable Message Sign (VMS) information. In addition to the traditional parsimonious TAM constructs (perceived usefulness, perceived ease of use and behavioral intention), the model examined the effects of attitude towards route diversion, familiarity with road network and information quality on road users’ acceptance of VMS. 762 drivers were interviewed and the obtained data were processed using Structural Equation Modeling. The results showed that travelers’ attitude towards route diversion had a positive effect on perceived usefulness and intention to use VMS. Information quality had a positive direct effect on perceived usefulness, perceived ease of use and attitude towards route diversion. Familiarity with the network had a positive effect on attitude towards route diversion and a negative effect on the perceived usefulness of VMS information. Perceived ease of use significantly and positively affected perceived usefulness and intention to use VMS. Perceived usefulness also had a positive effect on intention. Several academic and practical implications were also discussed.

This study aims at reviewing whether regional and generational differences exist in behavior intention to adopt low-carbon travel modes. Based on 759 questionnaires collected from three cities (Zhenjiang, Suzhou, and Shanghai) with different population sizes in China, we develop a modified theory of planned behavior (MTPB) model framework integrating low-carbon transport policies, psychological aspects, personal norms, and travel habits. A more advanced partial least-square method of structural equation model (PLS-SEM) and a multiple-group analysis (MGA) model are applied to estimate the effects and heterogeneities of these factors on low-carbon travel behavior intention among three cities and four age groups. The results show that the roles of low-carbon policies, subjective norms, and personal norms on behavior intention of adopting low-carbon travel modes are more salient. The effect of low-carbon policy on behavior is much weaker than it is on intention, and it does not follow that such intention will often be followed up with action. There is regional and generational heterogeneity in terms of the influence on low-carbon travel behavior intention. In particular, the benefits of low-carbon policies are more remarkable in the middle-sized city, young adult group, and pre-older adult group. The low-carbon travel behavior intention in the large-sized city, junior-middle adult group, and senior-middle adult group are affected by subjective norms more easily. The large-sized city and young adult group have better personal norms in favor of low-carbon travel. The findings could provide helpful insights into developing heterogeneous transport policies to encourage different travelers to switch from auto to low-carbon travel modes.

Understanding the commuting patterns of long-duration commuters and the possible changes in these patterns can help policymakers adopt the more reasonable land use and transportation policies. With Kunming in China as a case study, the determinants of long-duration commuting trips were identified based on logistic regression model. The results indicated that age, education level, number of workers, presence of retirees, and residential location have a significant impact on the occurrence of long-duration commuting trips. The ideal commuting times and tolerance thresholds of commuting time of long-duration commuters were also investigated. The statistical results revealed the distributions of ideal commuting times and tolerance thresholds of commuting time of both short- and long-duration commuters. The average tolerance threshold of commuting time and the average ideal commuting time of long-duration commuters were greater than those of short-duration commuters. For 97.2% of the long-duration commuters, their actual commuting time was longer than the ideal commuting time; this finding indicates that most long-duration commuters are dissatisfied with their commuting time. The actual commuting time of 40.1% long-duration commuters exceeded their tolerance thresholds; these commuters are eager to reduce their commuting time

Drivers’ behaviors to look for a parking space are affected by numerous influence factors, and there are differences between motorcycle drivers and other drivers, such as car drivers and truck drivers. In many developing countries, motorcycles dominate urban transportation, and it is essential to assess the impact of motorcycle drivers’ parking choice behavior as a solution to reduce the effect on traffic flow. This study identified the influence factors of motorcycle drivers’ parking lot choice models in a developing country, Viet Nam. Data were collected in a motorcycle dependent city, Ho Chi Minh (HCM) City, typically. A stated preference (SP) survey was designed and collected 318 answers from motorcycle drivers. Various discrete choice models under the assumption of random utility maximizations (RUM), which included the mixed logit model, multinomial logit model, and nested logit model, were employed to evaluate the influence factors on motorcycle drivers’ parking choice behavior models. The results showed that the mixed logit model fit with the data. Parking fee, walking distance, the capacity of the parking lot, and queuing time have significant effects on parking lot choice modeling. However, navigation and street sign variables showed a lesser effect on the choices of motorcycle users. This study towards parking planning solution for motorcycles and the author expects that it would be helpful to further study on the parking lot in developing countries.