Content uploaded by Linchuan Yang
Author content
All content in this area was uploaded by Linchuan Yang on Jan 30, 2023
Content may be subject to copyright.
Mode substitution and carbon emission impacts of electric bike sharing systems
Abstract and Figures
This paper presents a quantitative analysis of the mode substitution and usage-phase carbon emission impacts of the electric bike-sharing system (EBSS) on urban transportation using self-administrated survey data and EBSS operating data. The substitution probabilities of a given EBSS trip for different transport modes is determined using the Bayesian inference method. Emission impact is measured by comparing the current emissions to those generated by other transport modes without the EBSS. Remarkably, the partial-trip substitution impact of the EBSS on car travel is investigated, where the EBSS serves as a connection to public transit. The results show that the EBSS barely generates new trips; most short-distance EBSS trips were transferred from walking and conventional bike-sharing, and most long-distance trips were shifted from cars, buses, and the subway. Around 5% of EBSS trips less than 2 km were transferred from the car by integrating with public transit while accounting for over half of the emission reduction. Based on our method, the CO2 emissions per km of EBSS are 19.47 g, with 6.91 g generated by e-bikes due to electricity consumption and 12.56 g by trucks for battery swapping and bike relocation. The studied EBSS has saved 75.52% of CO2 emissions that other transport modes could have generated.
Figures - uploaded by Linchuan Yang
Author content
All figure content in this area was uploaded by Linchuan Yang
Content may be subject to copyright.
... Electric bike-sharing systems (EBSS) have recently gained popularity as a means of transportation because of the growth of the sharing economy and the promotion of sustainable development (Bieliński et al. 2021;Zhou et al. 2023). EBSS have been widely embraced by governments and consumers because they offer a cost-effective, green, and low-carbon form of transportation (Elmashhara et al. 2022). ...
... Among the studies focusing on EBSS, Wamburu et al. (2021) calculated the carbon emissions reduced by using EBSS as a substitution for taxis and ride-sharing vehicles for short trips in New York. Zhou et al. (2023) estimated the substitution probability of EBSS for other transport modes based on the Bayesian inference method, then calculated the carbon emissions of different transport modes, and obtained the reduction of carbon emissions. However, few studies have emphasized the exact carbon emissions of EBSS and characterized their spatial patterns. ...
... The approach could be applied to future research after adjusting the model parameters for calculating carbon emissions to fit particular scenarios. For example, Zhou et al. (2023) conducted a study on Hellobike in China, where the energy consumption per kilometer was 7.2 Wh/km (as opposed to 7.9 Wh/km in this study) and the national average emissions factor was 960 g/ kWh (as opposed to 433 g/kWh in this study). When analyzing the carbon emissions of e-bikes in China with Hellobike, the parameters could be set to the aforementioned values. ...
This study explored spatiotemporal patterns of e-bike usage. The carbon emissions of electric bike-sharing systems in Chicago were estimated, and their spatial distribution was characterized. Customers preferred e-bikes as a transportation mode for trips that took less than 20 min, indicating that the use of e-bikes for short trips could reduce traffic congestion. This finding has an important implication for urban planning studies. It would be more reasonable to calculate the potential reduction of carbon emissions from substituting e-bike rides for short trips by car or other transportation modes rather than for all trips. This study also identified hotspots and corresponding peak periods. Recommendations were made for strategically dispatching e-bikes around the central business district to meet customer needs during weekday peak commuting hours. E-bike trips produced the least amount of carbon in January. Emissions gradually climbed until April, when they almost tripled the January emissions. Throughout these 4 months, e-bike trips generated 1624.4 kg of carbon emissions, with weekday emissions accounting for the majority. The spatial patterns of carbon emissions were visualized based on street networks. The method used in this study for exploring carbon emissions can be applied to future research after adjusting the model parameters to fit particular scenarios.
... The substitution probability refers to the likelihood that a shared micro-mobility trip has replaced another mode of transportation. Zhou et al. (2023) used a survey question 'which mode of transportation would you choose if there were no e-bike?' to quantify the transfer probability because the answer to this question can be explained as the mode that was replaced when SEB became available. It is worth noting that the actual reduction of GHG emissions is determined by the mode with the highest probability. ...
... According to the methods in Section 3.3, basic calculation parameters are determined based on the sources listed in Table 2. Zhou et al. (2023) conducted a survey to investigate the distribution of daily travel among people, their preferred transportation modes for different distances, and the likelihood of switching to an SEB from their original transportation mode. This survey can be interpreted as alternative choices made by users when shared micro-mobility services are unavailable. ...
... It should be noted that the probabilities of mode choices listed in Table 3 may be subject to various external factors, such as weather conditions, terrain, and time of day. The emission factors of other transportation modes refer to Zhu and Lu (2023) and Zhou et al. (2023). Five simultaneous experiments take approximately 432 minutes. ...
Shared micro-mobility systems (SMMS) have the potential to reduce CO 2 emissions of urban transportation. However, the reduction varies with fleet sizes (i.e., the number of different types of bikes in the market) and layouts. The excessive commercialization of SMMS has resulted in a decline in carbon benefits. In this study, we propose an agent-based model integrated with a lifecycle assessment (LCA) approach to evaluating the carbon benefits of unknown shared bike scale scenarios by establishing a brand-new SMMS. The proportion of satisfied actual trip demand and bike utilization rates also be analyzed for exploring the inherent mechanisms of SMMS. Take San Francisco, California, as an example, the evaluation of free-floating shared electric bikes and station-based shared-bikes analyzes five different layouts, each consisting of 121 different combinations. The results indicate that, for an area of approximately 100 km 2 with a daily travel demand of over ten thousand, a fleet size of 4500-7500 bikes is suggested, potentially leading to a weekly carbon reduction of 10,000-11,000 kg CO 2-eq. This study will provide insights for launch plan and scale management of SMMS from a sustainable perspective.
... However, today, e-bikes have become common among different user groups regardless of age or gender, because of their various mobility, health, and environmental benefits. Consequently, many e-bike-sharing schemes (EBSSs) are utilizing these features of e-bikes and successfully facilitating last-mile connectivity for daily commuters and tourists, especially during and after the COVID-19 pandemic (21)(22)(23)(24)(25). ...
... In contrast, abundant evidence is available from past research that prioritizes safe cycling infrastructure over charging infrastructure in influencing e-bike adoption (8,18,32,46). However, the studies that focused on shared e-bike adoption identified the location and number of charging stations as the strong indicators influencing EBSS use in addition to the cycling infrastructure (24,25,31,45). Given the limited range (25-50 km per charge) and high charging time (4-5 h for a full charge) of e-bikes, and longer trip lengths of the commuters (more than 15 km), there can be an exigency for a destinationbased charging infrastructure. ...
Electric bicycles (e-bikes) are an emerging mode of sustainable transportation well-known for their individual and environmental benefits. Past research suggests factors for e-bike adoption from new and experienced e-bike users, but little is known about prospective users’ attitudes. Understanding the standpoint of non-users would reveal practical barriers impeding e-bike adoption in developing markets. We identify important drivers and barriers from a representative city-level sample of prospective e-bike users in India. The study employs exploratory factor analysis integrated with a multi-criteria decision-making model to identify latent components and prioritize their variables. The results revealed five factors: user-perceived benefit-specific motivators, travel quality-specific motivators, e-bike mobility-specific motivators, perceived social and economy-specific barriers, and e-bike infrastructure-specific barriers. Attributes such as monetary savings, reduced congestion, and last-mile connectivity were identified as the most important benefits, while fear of battery explosion and lack of cycling and charging infrastructure were perceived as the key barriers. Comparison by age shows “purchase cost” as the most influencing perceived social and economy-specific barrier among young male commuters. Comparison by income underlines the diminishing importance of “purchase cost” with increasing income among males. Regardless of age, income, and trip length, females prioritized “range anxiety” over “purchase cost.” The trip length-based comparison reveals the significance of “risk of theft” for males with longer trip lengths. In general, males of all groups preferred using e-bikes for “short non-commuting trips” substituting motorized transport, while females preferred using them for “last-mile connectivity.” These findings offer insights for designing effective e-bike promotion campaigns for the mass adoption of e-bikes.
... The common limitation of this type of study is that it only provides descriptive statistics (X% of shared EV/e-bike trips would have been conducted using Y mode). Zhou et al. (2023) categorized trips by different distances and calculated a distinct substitution pattern for each distance. Fukushige et al. (2021) went a step further by estimating a statistical model: it uses variables including trip attributes and individual characteristics to predict the substituted mode of each shared e-bike trip. ...
Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found that users of different modes not only have varied general preferences for different shared modes but also have different sensitivity for attributes such as travel time and cost. Public transport users are more likely to switch to eHUBS modes than car users. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).
... Research efforts on ebike-sharing systems (also hybrid bike-sharing systems) are still insufficient, especially for the impact of introducing ebikes to conventional bike-sharing systems. Scholars mostly are concerned with user behavior related to shared ebikes and their environmental benefits (Campbell et al., 2016;Liu et al., 2022;Reck et al., 2021;Zhou et al., 2022). By numerical simulation, Schnieder and West (2019) evaluated the performance of three charging strategies including charging at midnight, charging during dispatch, and charging at the station. ...
The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.
The coronavirus pandemic may provoke an increase on our overreliance on private car usage due to a permanent loss of confidence on public transport (PT), threatening current decarbonization efforts of the transport sector. Thus, alternative modes like bike sharing systems (BSS) must be considered.
In this study, through conducting 16 semi-structured interviews and by employing thematic analysis, we explore the users' perceptions of using Lisbon's BSS during this pandemic. Our findings show that the observed decrease on BSS usage during the COVID-19 lockdowns was mostly due to mandatory teleworking than to a perceived infection risk. Even during the height of the pandemic, users still turned to BSS to fulfil their essential trip needs. Users considered bike sharing to have a lower infection risk comparatively to PT, with some users joining BSS during the pandemic to specifically avoid using PT. Furthermore, users associate riding a shared bicycle with a pleasant activity that reduces their travel times and costs, while also providing health and environmental benefits. Consequently, bike sharing contributes to the resilience of transport systems by providing its users with a transport alternative perceived to have a low infection risk, ensuring their mobility needs during disruptive events.
Findings from this research provide evidence that support policies, such as, expanding BSS coverage areas, optimizing rebalancing operations, introducing shared e-bikes, and implementing segregated cycling lanes alongside BSS. These policies may be particularly effective at increasing the competitiveness of BSS as an alternative mode during disruptive public health crises and beyond.
To enhance the last-mile connectivity of metros, transport managers are experimenting with the integration of shared mobility modes, such as dockless bike sharing (DBS) and ridesourcing. To provide theoretical guidance for transport managers, it is imperative to deepen knowledge of the differences between the integration of DBS and ridesourcing with metros. A three-level structure was developed in this study to identify integrated DBS and ridesourcing trips using observed data from DiDi and Mobike in Shanghai, China. The travel characteristics of two identified integrated shared mobility modes were then compared. Finally, negative binomial regression models were built to investigate the factors influencing these two integrated uses. Comparative analysis shows that the travel distance intervals with the highest proportions are 0.5–1.0 km for DBS and 1.0–1.5 km for ridesourcing. In terms of temporal distribution, morning peaks of integrated DBS trips are higher and earlier than those of ridesourcing. In the spatial dimension, integrated DBS and ridesourcing trips are mainly concentrated in the central urban area and suburban areas, respectively. Further examination indicates that the built environment has different impacts on these two integrated uses. These results provide an improved understanding of the seamless integration of shared mobility modes with metros.
Bike sharing has the potential to benefit the environment with less energy consumption and CO2 emissions. Most previous studies on this topic assumed that bikeshare trips mainly substitute cars. However, bikeshare also replaces ecologically friendly transportation modes like public transit (PT). Therefore, it is vital to examine bikeshare's modal shift from PT to fully assess its environmental impacts. This paper examines the bikeshare and PT relationships in Chengdu,and environmental effects of bike sharing under two cases based on this relationship. The first case only considers the substitution of bikeshare for car and walking, and 19,964.31 L of fuel and 4125.13 kg of CO2 can be saved; while the second case also considers its substitution for buses and subways, and 13,198.68 L of fuel and 2564.31 kg of CO2 can be saved. Results indicate that the environmental benefits of bike sharing may be overestimated if not considering its substitution for PT.
With the increasing demand for liveable cities, efforts to promote high degrees of street vitality have become prevalent in contemporary urban design. Nevertheless, exploring the relationship between intangible street vitality and built environment features remains a challenge. With the help of multi-sourced urban data, a systematic analysis of 14 Chinese cities with both top-down and human-scale viewpoints was developed focusing on morphological, functional, and human-scale features. The hierarchical regression analysis indicates that functional features have the highest impact, followed by morphological and human-scale features. Among functional features, spatial proximity to a primary school and functional mixture have the strongest impact, while high street store consumption levels tend to have negative effects. In terms of morphological features, a high-density built-up environment has the strongest impact. Many human-scale features have positive effects on street vitality; nevertheless, the visible vegetation and sidewalk, which are important indicators of streetscape quality, point to a negative correlation that reflects the mismatch between vitality and quality issues. In short, this data-informed approach provides new insights into street vitality. This study responds to the growing scholarly interest in introducing systemic and scientific thinking into the previously qualitative and intuitive fields of urban design.
Dockless bikesharing (DBS) has been considered as a solution to the first and last mile problem of metro connectivity. Leveraging data covering all DBS programs in Shanghai, China, this study evaluated bike-and-ride (BnR) activities in DBS-metro systems via four metrics: BnR trip count, BnR rate, shared-bike utilization rate, and catchment size (85th percentile transfer distance). A set of generalized additive models considering marginal nonlinear interactions was fitted to examine associations between the four metrics and external environment, including land use, socio-demographics, roadway designs, transportation facilities, metro station features, and DBS operator features. Different buffer sizes measured by network distance were tested to check model robustness and find optimal buffers. Results showed that: 1) metro stations near the city center exhibited greater BnR trip count, higher BnR rate, lower shared-bike utilization rate, and smaller catchment size; 2) proportion of public and residential land suggested positive relationships with BnR trip count but lose their significances after offsetting metro ridership; 3) numbers of colleges, shopping malls, and carsharing stations presented positive relationships with both BnR trip count and BnR rate; 4) land use mix was significantly positively associated with BnR trip count only when buffer size was larger than 1.5 km; 5) regions with higher population density went from less BnR activities in the city center to more BnR activities in the suburbs; 6) Large DBS operators outperformed small ones in BnR trip count but not in bike utilization rate. Taken together, this study uncovers a spatially disproportionate and supply-demand unbalanced distribution of DBS resources, which could attenuate the efficiency and attractiveness of using DBS to BnR. DBS operators and local governments should evaluate DBS systems from multiple perspectives to avoid an oversupplied and over-competing market.
We estimate the maximum capability to reduce CO2 by substituting private car travel for e-bike. We use spatial microsimulation (population-synthesis) to stimulate the adult population within every small area in England, taking account of area type and geodemographic circumstances of the population. By estimating for individuals the distance they are capable of travelling by e-bike and the extent to which they are capable of replacing private car travel, we find the upper limit on the capability to reduce CO2 by substituting car travel for e-bike use is 24.4 MTCO2 p.a. (per annum) in England. CO2 saving capability per person and per small area are highest (over 750 kg CO2 per person p.a.) for residents of rural areas and the rural urban fringe. e-bikes offer major conurbations more modest CO2 saving capability per person. We identify areas which are vulnerable to car related economic stress and also have high capability to replace car km with e-bikes, which if supported appropriately could contribute to equitable carbon reduction. Though capable of a very significant contribution to transport carbon reduction, other changes in technology and reduction in demand would also be necessary to reach zero emissions.
Our results are directly relevant to policy actors internationally who require evidence on place-based decarbonisation capability, particularly where car dependence is high. The results highlight how context is important in any attempt to design policy for equitable carbon reduction both to influence discussion on what is possible, as well as practical identification of areas for targeted intervention. Digital indicators covering all zones in a country's geography such as this are also useful because of the rapid digitalisation of policy making. We provide code so that others can produce similar analyses in other countries (https://github.com/DrIanPhilips/e-bikeCarbonReductionCapability).
Free-floating bike sharing (FFBS) is clearly becoming a global phenomenon. However, the actual environmental impact of FFBS remain controversial. Using life cycle assessment (LCA) and bottom-up method, this study quantitatively estimates the impact of Beijing large scale FFBS program on greenhouse gas (GHG) emissions from transportation system based on the real-world transportation big data. The results show that FFBS can obtain desirable environmental benefits, but the premise is reasonable and effective market supervision. In the period of explosive growth (2017∼2018), due to oversupply and short service life of the shared bike, FFBS has become the transportation mode with the highest GHG emission-intensity. Compared with before the advent of FFBS (i.e., 2016), the total GHG emissions and per capita GHG emissions increased by about 0.33 million tons CO2-eq and 25.63 kg CO2-eq respectively. Subsequently, in the orderly development stage, the operational efficiency of FFBS has been significantly improved under the supervision and governance. The total GHG emissions and per capita GHG emissions in 2019 decreased by about 0.33∼0.41 million tons CO2-eq and about 11.58∼15.85 kg CO2-eq respectively, compared with 2016. This study helps to better understand the environment impact of FFBS and constitutes a valuable reference for improving urban transportation sustainability.
Bike-sharing systems provide the public with more transport choices. To elaborate more effectively and comprehensively bike sharing's prospective contribution to urban sustainable development, a quantitative estimation of bike sharing's environmental benefits is performed through a case study of New York City's bike-sharing systems. Using a long-term series of big data, the environmental benefits of bike sharing in New York City are evaluated from a spatiotemporal perspective. Data on a total of 48 million bicycle trips between January 2014 and December 2017 are analysed. During 2014-2017, bike travellers saved 13,370 t of oil equivalent, and decreased 30,070 t of carbon emissions and 80 t of nitrogen oxides. Evaluation of gender dynamics reveals that men produced greater environmental benefits through the bike-sharing initiative.
The rapid growth of dockless bike-sharing (DBS) systems has attracted increased academic attention in the solutions to first- and last-mile problems. However, only a few studies have examined how the synergy between DBS and metro transit is affected by objective and perceived measures of built environment collectively. This study intends to fill this research gap by focusing on the effects of objective and perceived measures of built environment on DBS–metro integrated use for commuting trips. Results reveal that low agreement between the two measures of built environment and that the perceived measure is more likely to be directly associated with DBS–metro integration than the objective measure. Different built environment attributes may affect DBS–metro integration by unique paths. Moreover, individual characteristics (i.e., gender, age, and income) and location factor moderate the association between the built environment and DBS–metro integration. Particularly, built environment attributes related to transportation service are easier to be moderated than land use and cycling condition attributes. We conclude that the understanding of and interventions for the built environment as objectively measured are necessary but not sufficient for DBS–metro integration. Promoting the perception of the built environment among different population groups is also important for interventions.
In hopes of reducing traffic congestion, air and noise pollution, and energy consumption, bike sharing is becoming increasingly popular around the world. Underlying many of the benefits attributed to bike sharing is an assumption that bike share journeys replace a significant proportion of trips previously made by car. This paper examines factors correlated with the use of an electric bike-sharing system in Tricity, Poland. We use the double hurdle estimation approach on data from two matched surveys: before and after the implementation of the system. Our analyses indicate that electric bike rides did not act as a substitute for car trips. Shared e-bikes were used by residents as a substitute for public transportation or as a first/last mile of transport to/from public transportation stops. We examined the use of e-bikes for various types of trips and identified factors encouraging and discouraging people from usage of bike-sharing and cycling in general.