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Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This...
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... goal was to make Lisbon city less polluted and more accessible to shared mobility, promoting the use of active modes over the private car. It is composed by conventional and electric bikes (26 kg, Li-ion batteries of 12.5 Ah 36 V, electric motor 250 W, see Figure 1). ...
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... are typically the months of July and August, where no pre-university school takes place, and these are also the months selected by workers to take vacations. As observed in Figure 10, on the weekdays, the two first peaks are slightly lower, but the afternoon peak and 7 pm to 1 am period have higher percentages of trips, which may indicate that the system is used more for night-outs in the holiday period comparing to the non-holiday period, and there is a decrease in the morning and middle day peaks, indicating commuters for work decrease due to holidays. However, these results should be more carefully examined with a survey conducted for the users of the system. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 The climate parameters, temperature, precipitation, and wind speed were correlated with the number of trips ( Table 5). ...
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... events can be classified as "weak" for values less than 0.5 mm/h, "moderate" for values between 0.5 and 4 mm/h, and "strong" for values above 4 mm/h. These categorized events were correlated with the number of trips considering the entire year of 2018, and the outcome is shown in Figure 11 for the hourly trips. Running an ANOVA null hypothesis on "the hourly trips on the weekends during three different rain events are statistically equivalent" and "the hourly trips on the weekdays during three different rain events are statistically equivalent", the p-value is less than 0.05 (0.006) for the first and higher than 0.05 (0.25) for the second. ...
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... the distinction between e-bike and bike is no longer possible, and the trip duration and distance are not available. Figure 12 shows the busiest station in the system in 2020, where the reduction in the April lockdown and partial lockdown in May can be observed. The climate parameter precipitation was very low in May (on average less than 0.8 mm/day), where the minimum number of trips was observed. ...
Citations
... E-bike sharing systems, in comparison to traditional bike sharing, have a relatively shorter history , resulting in a scarcity of research on this emerging form of shared mobility (Bieliński, Kwapisz, & Ważna, 2021). Existing studies on e-bike sharing have covered several topics , including concept design and operational pilot studies (Cherry et al., 2011;Ji et al., 2014;Langford, Cherry, Yoon, Worley, & Smith, 2013), demand analysis and user behavior (Campbell, Cherry, Ryerson, & Yang, 2016;Choi, Kim, & Seo, 2023;Guidon et al., 2019;He, Song, Liu, & Sze, 2019), mode substitution and environmental impact (Bieliński et al., 2021;Fukushige, Fitch, & Handy, 2021;Fukushige et al., 2023;Raposo & Silva, 2022;Zhou, Yu, et al., 2023), and operations strategies such as optimal fleet deployment (Zhu, 2021), rebalancing (Fukushige, Fitch, & Handy, 2022), as well as battery swapping Yang et al., 2022Yang et al., , 2021Zheng et al., 2023;Zhou et al., 2022;Zhou, Wang, et al., 2023). Additionally, facility planning aspects have also been explored, including e-bike sharing station planning (Chen, Hu, Li, & Wu, 2020), parking location planning (Zhou, Wang, Yang, & Wei, 2020), and charging pile planning (Zhong, Xu, Chen, Gao, & Duan, 2019). ...
E-bike sharing has been embraced as a sustainable transportation mode, and its success hinges on efficient recharging facility planning. While battery-swapping technology has emerged as a promising solution for refueling shared e-bikes, the efficient recharging of swapped batteries remains understudied. This study explores two battery-swapping recharging modes, namely, centralized charging (CC) and decentralized swapping (DS). Specifically, a multi-decentralized swapping (M-DS) mode is introduced to incorporate multiple recharging sites within each service area, aiming to enhance recharging efficiency. A general modeling framework is proposed to simulate battery-swapping demand based on available bike-sharing data and optimize facility planning and routes across various recharging modes. The optimization model is formulated into a location-routing problem, addressed by a customized meta-heuristic algorithm. A case study conducted in Shenzhen, China, demonstrates the model’s ability to jointly determine optimal facility locations, facility types, and operational strategies. The results indicate significant efficiency enhancement achieved by M-DS, offering both cost savings and improved e-bike availability. Further scenario analysis reveals the flexibility and cost-effectiveness of M-DS, thereby supporting small-scale e-bike sharing services and facilitating the expansion of access to environment-friendly transportation. This study contributes to facility planning for electric micromobility and provides practical insights for industry practitioners.
... Among the growing demand and price fluctuation of fuel the world needs potential fuel to alternative as well as new vehicle types [8,9]. One of the promising solutions for a new vehicle type is an electric vehicle including electric bicycle, which is favorable to decrease toxic emission as well as reduce dependent on consumption of fossil fuel [10,11]. The electric bicycle is widely used due to its environmental friendly nature, as well as it help in the healthy and leisure need [12,13]. ...
A method integrating the artificial neural network with genetic algorithm is applied. • The MATLAB-Simulink model of electric assisted bicycle is employed for data generation and validation. • The required power and electric consumption are optimized by combining artificial neural network and genetic algorithm (ANN-GA). • The effective performance area of electric assisted bicycle under various a structure and operating parameters is identified. • Deep-learning results reflect major physical mechanism of electric assisted bicycle operation. A R T I C L E I N F O Keywords: Electric assisted bicycle performance Artificial neural network Genetic algorithm Required power Electric consumption A B S T R A C T The purpose of this research is to study how the operating and structure parameter affect the dynamic, require power and electric consumption of electric assisted bicycle (EAB). The paper applied a combined an artificial neural network (ANN) with genetic algorithm (GA) method to predict the required power, electric consumption of EAB and find an effective performance area. The MATLAB-Simulink simulation model is established to create 1000 data point, that is applied in an ANN for training, testing and verifying. The ANN-GA method is applied to identify the optimized required power and electric consumption under four typical slope grades in the area of (0-0.65%), four typical wheel radius in the area of (0.3-0.0.39 m), four typical frontal areas in the range of (0.423-1.323 m 2), four typical speed levels from speed level 1 to speed level 4. After the ANN is trained, it is applied into the genetic algorithm to identify the effective performance. The prediction results reflect the major physical mechanism that governs ES performance and are validated against the MATLAB-Simulink simulation model. Additionally, the electric assisted bicycle can reach an effective performance area at 27.16 km/h with the optimized required power of 545.5 W at slope grade of 0%, wheel radius of 0.39 m, speed level 4, frontal area of 0.423 m 2. The results show that the ANN-GA method is suitable to identifying structure and operating parameters including various slope grades, frontal areas, wheel radius, speed level for optimizing effective performance of EAB, which contributes a helpful method for EAB design and control. Beside that, the experimental test was conducted on real road test at Taehwa river to verify the simulated results. The experiment results and simulation results have the same trend at the same conditions.
... The European Committee of the Regions, in the EU Cycling Action Plan [41], emphasizes the need to push for greater accessibility of public transport stops for pedestrians and cyclists, the creation of safe, universally accessible rober parking at interchange points, and service-oriented, bike-sharing, as part of territorial programming instruments [49]. Raposo and Silva (2022) conducted a study in Portugal that found that the implementation of such measures can potentially prevent 36 tons of greenhouse gas emissions and reduce energy consumption by 451 GWh annually as a result of these measures [50]. ...
The study estimates the amount of emissions resulting from linear sources. There were calculations for a model rural municipality, composed of national, provincial, country, and municipal roads that run through the area. In this study, the following categories of vehicles were assumed to travel along this route: motorcycles, passenger cars, light trucks (vans), heavy trucks without trailers, trucks with trailers, and buses. The analysis used data on the average volume of traffic (SDR) on selected roads on the territory of the sample municipality, based on the frequency of participation in traffic by each mode of transportation on selected road sections. To estimate the emission rates of each pollutant, for each vehicle category separately, the calculations were made based on the emission factor rates for each type of fuel. According to the adopted methodology and based on the adopted assumptions scenarios, pollutants' emissions were estimated. The implementation of the scenarios offered for reducing CO 2 emissions has been proposed, and it is estimated that, depending on the variant adopted, the reductions will be between 13 and 21% in variant I, between 3 and 8% in variant II, and between 18 and 34% in combining these variants. The variant with a reduction in private car transportation in favor of bicycle transportation in combination with public transportation showed the most favorable effects on the environment.
... This is all the more important given that in most countries, outside of major cities, the basis of mobility is the highly energy-inefficient mode of transport that is the individual car. Attempts are being made to address this by raising the cost of private car use in cities and by promoting public transport and developing alternative systems to provide mobility [2]. ...
Mobility is one of the basic needs for modern people. The transport system is one of the largest consumers of energy. The largest dimension of mobility activity is concentrated in metropolitan areas, which also shows energy consumption by transport. The research looked at the potential for improving the energy efficiency of a functional and spatial structure, using the example of a medium-sized city and its functional area. The study refers to the idea of the pervasiveness of spatial structure and to the criteria of New Urbanism, as a multifunctional and sustainable urban form. The gravity fields concept was also used. This article presents simulation modeling that has made it possible to model the potential for optimizing an urbanized area towards a reduction in energy consumption in the mobility sphere and to compare the scale of the potential in this respect of its segments in relation to the movement relationships of the destinations (residence, services, workplaces, and leisure). Results show the greatest energy-saving potential located in the peripheral areas with longer distances from centers and the worst equipment of services and infrastructure. The analytic model presented in the article, based on the concept of pervasiveness, could be used for the evaluation of the multifunctionality and sustainability of urban structures.
... Thus far, we have only witnessed small pilot projects around the world. The only major projects are BiciMAD in Madrid (Spain), which has 2000 e-bikes at 165 stations, and the e-bike-sharing system GIRA in Lisbon (Portugal), which has 600 conventional bikes and 700 e-bikes at 164 stations [2,7]. ...
... The final criteria hierarchical structure of the AHP is presented in Table 1. Proximity to a bike lane/(meter) 22 Proximity to the subway network/(meter) 23 Proximity to the bus transport network/(meter) 24 Proximity to the tramway network/(meter) 25 Proximity to transit hubs and intersections/(meter) 26 Proximity to road networks/(meter) 27 Proximity to ferry ports/(meter) 28 Proximity to high traffic density roads/(meter) 29 Proximity to the parking area/(meter) 3 Terrain-Related Criteria I 31 The slope of the terrain/(maximum slope of a hill in per cent) 32 Possibility of expansion in the future/(per cent) 4 Environment-Related Criteria O+ 41 Emission reduction due to e-bike-sharing system (e-BSS)/(kg CO2eq) 42 Proximity to repositioning trucks depot location/(meter) 5 Battery-Related Criteria O+ 51 Number of batteries at the station/(number of batteries) 52 Number of charging piles at the station/(piles) 53 E-bike battery autonomy under regular use/(minutes) 6 E-bike-Related Criteria O+ 61 Number of e-bikes at the station/(number of e-bikes) 62 Number of e-bikes slots/(number of e-bikes) 7 Economic Criteria O− ...
An e-bike sharing system (e-BSS) solves many of the shortcomings of BSS but requires high financial investments compared to BSS. This article proposes a sustainable and targeted extension of the existing BSS with e-bikes and charging piles. The existing BSS in the selected city area is divided into sub-areas using the Voronoi diagram and reference points (landmarks). Then, the integrated approach of the Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) is used to assess the adequacy of the existing bike-sharing stations for updating with e-bikes and charging piles. The joint approach allows decision-makers to look at the whole process and highlight the link between the criteria assessment and user preferences in the context of the chosen reference point. This can encourage future users to use e-BSSs. Based on a thorough literature review, the defined system of criteria takes into account all dimensions of sustainability: the requirements of most stakeholders and the structural features and needs of e-BSS. Finally, the super-efficiency DEA is used to classify the suitable candidates for bike-sharing so that only the most suitable stations are updated. The test of the proposed algorithm in Ljubljana city centre confirms several suitable options for updating the BSS, depending on the reference point.
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.
