The rapid adoption of electric bikes (e-bikes) (~150 million in ten years) has come with debate over their role in China's urban transportation system. While there has been some research quantifying impacts of e-bikes on the transportation system, there has been little work tracking e-bike use patterns over time. This paper investigates e-bike use over a six-year period. Four biannual travel diary surveys of e-bike users were conducted between 2006 and 2012 in Kunming, China. Choice models were developed to investigate factors influencing mode-transition and motorization pathways. As expected, income and vehicle ownership strongly influence carbased transitions. Younger and female respondents were more likely to choose car-based modes. Systematic and unobserved changes over time (time-dynamics) favor car-based modes, with the exception of previous car users who already shifted away from cars being less likely to revert to cars over time. E-bikes act as an intermediate mode, interrupting the transition from bicycle to bus and from bus to car. Over six years, e-bikes are displacing prospective bus (6555%), car/taxi (1524%) and bicycle (197%) trips. Over 40% of e-bike riders now have household car access so e-bikes are effectively replacing many urban car trips.

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This study examines the factors that influence the use of carsharing systems in Beijing, as well as the potential for carsharing systems that integrate electric vehicles. Investigated variables include weather, air quality, price, vehicle attributes and "status" indicators. Additionally, we explore how the impacts of these factors differ when carsharing is utilized for one-way trips as compared with round-trips. The study relies on a pen-and-paper survey (1,010 completed survey forms with 2,023 reported trips) which uses a stated-preference pivoting design to build hypothetical choice sets around actual trips. The data are used to estimate binomial logit regression models for one-way and round-trip carsharing. Results of the analysis indicate that age, car ownership, shelter mode, the original cost for taxi users, perceived parking availability, cold weather, and relative cost differences are significant for one-way carsharing. For round-trip carsharing, significant factors include car ownership, income, gender, environmental concern, and relative cost differences. The most statistically significant factors to attract carsharing customers are the cost gap (defined as cost of original mode-cost of carshare) for both one-way and round-trip carsharing services, and car ownership, which has a positive significant effect for one-way trips and a negative significant effect for round-trips. This paper contributes to the literature by further examining the determinants that affect the use of carsharing, distinguishing between one-way trips and round-trips, and developing models that can be applied in urban environments like Beijing.

Household car ownership has risen dramatically in China over the past decade. At the same time a disruptive transportation technology emerged, the electric bike (e-bike). Most studies investigating motorization in China focus on macro-level economic indicators like GDP, with few focusing on household, city-level, environmental, or geographic indicators, and none in the context of high e-bike ownership. This study examines household vehicle purchase decisions across 59 cities in China with broad geographic, environmental, and socioeconomic characteristics. We focus on a subset of households who own e-bikes and rely on a telephone survey from an industry customer database. From these responses, we estimate two three-level hierarchical choice models to assess attributes that contribute to 1) recent car purchases and 2) the intention to buy a car in the near future. The results show that the models are dominated by household characteristics including household income, household size, household vehicle ownership, number of licensed drivers and duration of car ownership. Some geographic, environmental and socioeconomic factors have significant influences on car purchase decisions. Only two city-level transportation variable have an effecthigher taxi density and higher bus density reducing car purchase. Cold weather, population density gross domestic product per capita positively influence car purchase, while urbanization rate reduces car purchase. Because of supply heterogeneity in the data set, described by publicly available urban transportation data, this is the first study that can include geographic and urban infrastructure differences that influence purchase choice and suggests potential region-specific policy approaches to managing car purchase may be necessary.

This study examines workers" mode-choice responses to a typical job decentralization policy implemented in China"s urban developmentgovernment job relocation (GJR) to new towns in the urban periphery. Broadly, the literature suggests that job decentralization tends to increase car commuting; however, little is known about the effects of China"s GJR initiatives on individuals" commuting mode choices. Using Kunming as a case study, this study examines how workers" commuting mode choices have shifted in response to GJR policy. Our study analyzes two travel survey datasets that span the job relocation process: 1) stated preference (SP) data on workers" anticipated mode choices after a move of workplace to a planned new town; and 2) revealed preference (RP) data on workers" actual choices of commuting mode after their jobs were moved. The findings suggest that after job relocation, workers" actual commuting modes shift from more sustainable modes towards cars. The determinants of workers" mode choices differ substantially between the hypothetical and actual setting of job relocation. The anticipated mode choices are largely determined by socio-demographic characteristics whereas the actual mode choices are strongly influenced by travel time and housing locations. The evidence from this study offers two important implications for future planning practice of job decentralization.First, planners and policy-makers should be skeptical about the transportation benefits of job decentralization. Second, while SP surveys can assist planners to predict individuals" mode-choice responses, the robustness of SP results should be carefully assessed before translating into the evidence base for informing job decentralization policy-makings.