Abstract:E-bikes are bicycles that provide pedal-assistance to aid people in cycling. Because of the potential of promoting sustainable transportation, more attention has been focused on the e-bike market. This paper investigates the differences of the cycling experience and perceptions between e-bike and conventional bicycle users, using samples drawn from independent bicycle dealer customers. A total of 806 respondents in the United States took the on-line survey, including 363 e-bike-owning respondents. The results … Show more
“…The similarities between motorcycle and bicycle may cause a little promotive relation between them. Finally, it is interesting to see that e-bicycle and bicycle are also mutually promotive, as shown by the positive correlation (0.0273), although the correlation is very small, possibly due to the similarity between e-bicycle and bicycle on the aspect of flexibility and sustainability [50].…”
Section: Discussion On Error Correlation Matrixmentioning
With the rapid increase of motorization in China, transitions have taken place in regards to traditional private transportation modes. This paper aims to understand four types of vehicle ownership within a household, including automobile, motorcycle, electric bicycle and human-powered bicycle. This study presents a cross-sectional multivariate ordered probit model, with a composite marginal likelihood estimation approach that accommodates the effects of explanatory variables, and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, in 2015, which can analyze the significant effects of sociodemographic attributes and built environment attributes. Interestingly, the major findings suggest that: (1) The households with higher income tend to own more automobiles, yet the effect is not obvious with a small value of elasticity, which is similar to developed countries. (2) The household education level, which takes a positive effect on automobile ownership, is a more elastic factor than income. (3) The higher population density contributes to less ownership of automobiles and motorcycles, due to traffic congestions and parking challenges. (4) There is a large substitutive relation between automobile and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.
“…The similarities between motorcycle and bicycle may cause a little promotive relation between them. Finally, it is interesting to see that e-bicycle and bicycle are also mutually promotive, as shown by the positive correlation (0.0273), although the correlation is very small, possibly due to the similarity between e-bicycle and bicycle on the aspect of flexibility and sustainability [50].…”
Section: Discussion On Error Correlation Matrixmentioning
With the rapid increase of motorization in China, transitions have taken place in regards to traditional private transportation modes. This paper aims to understand four types of vehicle ownership within a household, including automobile, motorcycle, electric bicycle and human-powered bicycle. This study presents a cross-sectional multivariate ordered probit model, with a composite marginal likelihood estimation approach that accommodates the effects of explanatory variables, and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, in 2015, which can analyze the significant effects of sociodemographic attributes and built environment attributes. Interestingly, the major findings suggest that: (1) The households with higher income tend to own more automobiles, yet the effect is not obvious with a small value of elasticity, which is similar to developed countries. (2) The household education level, which takes a positive effect on automobile ownership, is a more elastic factor than income. (3) The higher population density contributes to less ownership of automobiles and motorcycles, due to traffic congestions and parking challenges. (4) There is a large substitutive relation between automobile and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.
“…Fishman and Cherry [13] provided a comprehensive review on e-bike-related literature, whose focus is transport instead of recreational e-bike research. E-bikes were shown to reduce the fatigue level [14][15][16][17] and increase the cyclist's mode satisfaction level [18]. It was also found that e-bikes can increase the ridership or travel distance in comparison with traditional bikes [4,15,19,20].…”
Section: Related Workmentioning
confidence: 96%
“…Equations (13) and (14) ensure that the same fleet deployment plan is applied at the initial period of planning horizon for different demand realisations. Constraints (15) and (16) are balancing constraints for the number of bikes and e-bikes at the beginning of each period t. Constraint (17) ensures that the number of bikes at each station can never exceed the capacity of bike station; constraint (18) ensures that the number of e-bikes at each e-bike station cannot exceed the total number of charging piles. Equations (19) and (20) are fleet size constraints to ensure that the total fleet size in each demand scenario is the same, and the fleet size constraint can also serve as a budget constraint.…”
Following the bike-sharing system, the shared e-bike becomes increasingly popular due to the advantage in speed, trip distance, and so forth. However, limited research has investigated the impact of the introduction of shared e-bikes on the existing bike-sharing systems. This paper aims to study the effect of shared e-bikes on the traditional bike-sharing system and determine the optimal fleet deployment strategy under a bimodal transportation system. A stochastic multiperiod optimisation model is formulated to capture the demand uncertainty of travelers. The branch-and-bound algorithm is applied to solve problem. A 15-station numerical example is applied to examine the validity of the model and the effectiveness of the solution algorithm. The performance of integrated e-bike and bike-sharing system has been compared with the traditional bike-sharing system. The impacts of the charging efficiency, fleet size, and pricing strategy of e-bike-sharing system on the traditional bike-sharing system have been examined.
“…Hence, the space of the investigated problem is equal to 8. Based on [69][70][71][72][73][74][75][76][77][78][79][80], the following criteria are specified: [74,79].…”
The problem of sustainable city transport is a growing field of study, and will be addressed in this paper. With the rising significance of present transportation systems’ negative externalities on the environment, such as the unavoidable increase of air pollution levels, cities seek sustainable means of transport and reduction of combustion cars’ utilization. Moreover, improvements in the area of renewable energy sources have led to rising trends in sustainability, driving the usage and production of electric vehicles. Currently, there is an increasing tendency of looking for more sustainable transport solutions, especially in highly congested urban areas. It seems that in that case, electric bicycles can be a good option, as they yield more benefits in comparison to cars, especially combustion cars. In this paper, we identify an assessment model for the selection of the best electric bicycle for sustainable city transport by using incomplete knowledge. For this purpose, the Characteristic Objects METhod (COMET) is used. The COMET method, proven effective in the assessment of sustainable challenges, is a modern approach, utterly free of the rank reversal phenomenon. The evaluated model considers investigated multiple criteria and is independent of chosen alternatives in the criteria domain. Hence, it can be easily modified and extended for diverse sets of decisional variants. Moreover, the presented approach allows assessing alternatives under conditions of incomplete knowledge, where some data are presented as possible interval numbers.
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