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This research aims to estimate the purposes for which bike-share users utilize shared bicycles from bike-sharing companies using selected predictors through multinomial logistic regression in Slovakia. The study seeks to provide a novel perspective on alternative transportation by addressing a gap in existing research, which has not previously focused on modeling the specific purposes of bicycle use. The final sample comprises 162 bike-share users out of more than 300 respondents. The results show that social status, bike-sharing company, and average distance are statistically significant input variables. Finally, we find that using shared bikes for recreation is more typical for employees with a traveled distance of up to 3 km from one of the two bike-sharing companies compared to the reference group (commuting to school). The paper contributes to better planning and management of bike-sharing systems. [ABSTRACT FROM AUTHOR]

This paper complements the existing literature on Bike Share Schemes (BSS) by investigating their use from a spatiotemporal perspective to assess their relationship with public transport (PT). We address these questions through the case of Brussels and its long-standing docked BSS 'Villo!'. Our study analyses comprehensive (consecutive 12 months) and disaggregated (station level) data on rentals and returns and finds that Villo! is used mostly in dense (although not all) districts also well served by PT. However, temporal structures suggest Villo! overtakes PT at night in vibrant districts and possibly in selected districts with lower PT services over weekends. In addition, Villo! stations at key PT hubs usually do not show specific temporal patterns, which suggests intermodality may work at all times during PT operations. There could be an evening peak effect combined with the Brussels' topography, but this needs to be confirmed by on-site surveys. [ABSTRACT FROM AUTHOR]

Abstract Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

Bike-sharing systems can definitely contribute to the achievement of sustainable urban mobility. In spite of this potential, their planning and operation are not free of difficulties. The main operational problem of bike-sharing systems is the unbalanced distribution of bicycles over the service region, resulting in zones where bicycles are scarce and zones where bicycles accumulate. In order to provide an acceptable level of service, the operator needs to carry out repositioning movements, which are costly. Bike-sharing repositioning optimization solutions have been developed that rely on the estimation of the expected number of requests and returns at each location. Errors in this prediction are directly transferred to suboptimal repositioning solutions. For this reason, the development of methodologies able to accurately forecast bike-sharing usage is an issue of great concern. This paper deals with this problem using machine learning regression methods, which yield usage predictions from inputs such as historical usage and meteorological data. Three different machine learning regression techniques have been analyzed (i.e., random forest, gradient boosting, and artificial neural networks) and applied to a case study based on the New York City bike-sharing system. This paper describes the variables of the models and their calibration processes. Results are analyzed and compared in order to determine which one of the three techniques and under what conditions is the most adequate. Comparisons are not only made in terms of accuracy but also with respect to the applicability of the algorithms. Results indicate that, given the similar accuracy of all methods, the simpler calibration process of the random forest technique makes it advisable for most applications. [ABSTRACT FROM AUTHOR]

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security. [ABSTRACT FROM AUTHOR]

The objective of this research is to explore the determinants of shared bike route choice in both urban and rural settings in Germany. To this end, a stated preference (SP) survey on route choice was conducted across several German cities. Participants were drawn from the service area of a regional bike-sharing system (BSS) in Germany. This area included five major cities and several mid-sized and smaller municipalities. The survey includes responses from both users and non-users of the BSS. Mixed multinomial logit models were used in analysis of the SP data. The study also calculated the willingness to pay (WTP) values for selected attributes. Significant influences on route choice included access and egress time, ride time, and ride cost. Significant random heterogeneity was found, especially for ride cost. Further heterogeneity was reported for interactions with sociodemographic attributes. Street type, surface, and bike infrastructure also had significant effects on route choice. Linkages between infrastructural preferences and respondents’ ages were evident. Although WTP values were strongly influenced by random heterogeneity in ride cost perception, most WTP values were comparable to the values obtained in other studies.

The field of transportation has undergone changes due to the advancements in technology and the pressing need for mobility solutions. As cities worldwide struggle with the challenges posed by population growth and environmental sustainability, it becomes imperative to introduce adaptive transportation options. The increasing traffic congestion in areas and concerns regarding air quality and carbon emissions emphasize the importance of finding sustainable solutions. In this regard we present the ''On demand shared use bike sharing system (OSABS)'' as an eco-friendly on-demand mobility alternative that has the potential to revolutionize transportation from its core. Self-driving bikes provide a solution by reducing traffic congestion and promoting sustainable modes of transport. Customers can easily reserve a bike through their smartphones and it comes directly to their location within the given time. The user will ride it manually and release it after usage. The bike then drives autonomously to the next destination. This service allows an environment-friendly door-to-door mobility solution.This scientific paper focuses on examining the viability of autonomous cargo bike-sharing as a solution for urban transportation. A simulation model has been built to reproduce the OSABS operation using a case study for the city of Magdeburg. Through a set of experiments with different demand scenarios, we analysed and discussed the profitability and feasibility of this new service. These findings would be valuable, in the conversation about the future of transportation through providing important insights into how autonomous bike-sharing systems could be effectively implemented.

The data provide information from a face-to-face survey of 370 potential dock-based e-bike-sharing users in Santander, Spain. The survey was designed to collect information about the current and future use of the city's bike-sharing service, including frequency of use, trip purpose, and Willingness to Pay (WTP) for a future e-bike-sharing service. The questionnaire is structured in two sections. The first section relates to the characterisation through socio-economic data and information on bike-sharing services. The characterisation questions were intended to clearly distinguish between potential recurrent users (locals) and occasional users (visitors). The second section corresponds to a stated preference (SP) exercise that reveals the WTP for using e-bike-sharing considering different payment structures: fee per use, annualised registration, and a combination of the above. The data provide a valuable reference for municipal managers, private bike-sharing service providers, and the scientific community about users' preferences and intentions to use bike-sharing and e-bike-sharing according to sociodemographic characteristics and pricing scenarios. In addition, the questionnaire can be used as a reference in related research.

Bike-sharing networks have achieved considerable success in many cities worldwide, gaining a growing number of supporters for this mode of transportation. While the existing literature covers various aspects related to bike-sharing, the exploration of the relationship between these networks and the financial benefits for residents when transitioning to cycling has been somewhat limited. Therefore, the main objective of our article is to identify the factors influencing urban residents’ decisions to switch to cycling and to understand the significance of financial considerations in shaping changes in travel behaviour. We assessed the perceived affordability of bike-sharing services by measuring respondent satisfaction (via Computer Assisted Personal Interviews – CAPI) with the rental prices of city bicycles. To examine the relationships between variables, we employed statistical tests, including the Fisher test, the chi-square test of independence, and the Mann-Whitney test. Our research findings confirmed that replacing public transportation with bicycles has the most substantial impact, while substituting car trips has a relatively minor effect. Furthermore, our analysis revealed statistically significant associations between price satisfaction and the decision to abandon car travel in favour of cycling, as well as the motivation to save costs and substituting walking and public transport with bicycle travel.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch. [ABSTRACT FROM AUTHOR]

Although the sharing economy in tourism has garnered much positive attention, perennial ethical gray areas have been ignored. Drawing upon the ecological systems theory, we investigate the influence of different systemic factors, classified as individual differences and environmental context, on the ethical behavior of travelers (i.e., reporting malfunctions in a bike‐sharing context and saving water/electricity in a peer‐to‐peer accommodation context). PLS‐SEM and fsQCA are applied to analyze and examine proposed hypotheses. The results complement the factor configuration differences between the two behaviors. Moreover, this study provides new insights into the regulatory guidelines for shared service operators in the tourism industry. [ABSTRACT FROM AUTHOR]

Assessments of the bike-sharing industry traditionally focus on its effects on other markets, municipalities, or general well-being. This paper deviates from this on how the market is organised. Using information on the aggregate number of firms in cities and greater cities across Europe, we found non-proportional changes in market size with respect to changes in market structure. This is crucial information inferring changes in profits, costs, or degree of product differentiation. To distinguish between these three sources, we utilised additional firm-level data on capacity and type of service provided. Our results suggest that the non-proportional increase in market size after an entry is most likely associated with increased intensity of competition and new forms of offered services, i.e. product differentiation. We did not find evidence that newcomers have been entering with substantially larger capacities per capita compared to incumbents. From a policy perspective, entry into the bike-sharing industry has benefited consumers through market expansion and caused a potential decrease in profits. • A non-proportional market size increase after the entry of the second and third firms. • No association for entry barriers; no entries with larger capacity. • Increases likely associated with differentiation: entrants likely dockless or e-bikes. • Entry and competition benefited consumers through market expansion and may decrease profits. [ABSTRACT FROM AUTHOR]

Bike-sharing systems (BSS) are gaining popularity in cities worldwide, as a part of a generalized strategy to mitigate the problems derived from motorized transportation (congestion, pollution, noise, etc.). These systems have proved to have positive effects on cities, moreover, many of them have reached a performance peak and require improvements to attract/retain users and compete against emerging soft mobility alternatives. Whilst there are broad studies evaluating BSS demand and design, less attention has been paid to user satisfaction and the complexities underlying the relationships between the system attributes importance. This study proposes a novel combination of two methodologies for satisfaction assessment that allows decision-makers to identify the most influential system attributes on user satisfaction. The combined methods are the direct, explicit, Importance Performance Analysis (IPA), and the nonlinear, implicit Three-Factor Theory (3FT), which combined generate a three-dimensional scheme that facilitates the comprehension of the results. The combination was applied to a dataset of Madrid´s BSS to identify the attributes that perform poorly, and then assess their implicit influence on satisfaction to establish improvement priorities. The results suggest that station occupancy and bicycle availability and totem functioning are key service attributes to enhance satisfaction. Also, the combination of the two methodologies makes it possible to differentiate that maintenance is a priority for subscribers and network extension for occasional users. The pedelec system is a key attribute that might help overcome Madrid´s unfavorable cycling environment. This user-centric evaluation is a valuable tool that guides precise measure implementation, service operation, future design, and planning oriented to increase ridership. [ABSTRACT FROM AUTHOR]

Mobility hubs facilitate the comfortable transfer between different means of transport. However, the optimal location of mobility hubs challenges municipalities due to the complexity of urban structures and the need to anticipate changes in travel behaviour. This study proposes an interdisciplinary approach to establish a more sustainable transport network by determining optimal locations for mobility hubs from a municipality’s perspective. Based on a pre-selection using a transport planning approach, we use a bilevel optimisation model to find optimal locations. The model minimizes external costs at the upper level while considering users’ route and mode choices at the lower level. We validate our approach in a case study of Aachen, Germany. Our findings indicate that implementing bike-sharing stations can significantly contribute to developing a more sustainable transportation network, while one-way car-sharing stations do not contribute to a more sustainable transport network within the model as they primarily draw away users from more sustainable modes.

The pricing problem for demand-responsive feeder transit (RFT) in competitive relationships with public bicycles in bicycle-sharing systems is investigated from a game-theoretic perspective to establish a feeder program for passengers traveling to and from transport hubs. First, a revenue model including the base fare and passenger travel delay penalty is constructed. In this model, the base fares of responsive feeder buses and public bicycles are functions of the distance from the demand point to the metro station and the rental period, respectively. Then, the pricing game process under mutual competition between responsive feeder buses and public bicycles is analyzed. Moreover, a nested two-layer planning pricing model is constructed. Finally, a solution algorithm based on sensitivity analyses and a genetic algorithm is designed. The results show that this method can be used to effectively determine the unit fares and passenger flow sharing rates of RFT and public bicycles. Compared with that under the single-vehicle RFT operation mode, the RFT fare under the two-vehicle RFT operation mode is lower, and the passenger flow sharing rate is higher, which can significantly increase the social benefits. However, the total revenue of the operation is significantly reduced and thus requires greater financial input from the government. Under the dual-vehicle operation mode, the fares of public bicycles are greater than those of RFT, and the passenger flow sharing rate is low, which significantly reduces both the social benefits and the total benefits of the operation. With the improvement of the operation mode of the RFT system, the competitiveness of using public bicycles decreases.

This research aims to estimate the purposes for which bike-share users utilize shared bicycles from bike-sharing companies using selected predictors through multinomial logistic regression in Slovakia. The study seeks to provide a novel perspective on alternative transportation by addressing a gap in existing research, which has not previously focused on modeling the specific purposes of bicycle use. The final sample comprises 162 bike-share users out of more than 300 respondents. The results show that social status, bike-sharing company, and average distance are statistically significant input variables. Finally, we find that using shared bikes for recreation is more typical for employees with a traveled distance of up to 3 km from one of the two bike-sharing companies compared to the reference group (commuting to school). The paper contributes to better planning and management of bike-sharing systems.

E-scooters have exploded in popularity across the United States in recent years. Understanding the effects of shared micromobility on society, as well as understanding how one shared micromobility mode can affect other travel modes is valuable for decision making and evaluation. In this study, the effects of introducing E-scooters on a preexisting bike-sharing system were evaluated using real-world data. A no-ride zone policy for E-scooters around the University of Arizona (UA) effectively created policy-based control and treatment groups to observe the effect of E-scooters on the bike-sharing system. A difference-in-differences regression model was used to estimate the change in the number of bike-sharing trips after the E-scooters were introduced for nonmembers and members, finding that after the introduction of E-scooters, member bike-sharing system trips were decreased by 1.5 trips per station per day in the area where E-scooters were introduced. Furthermore, a temporal and spatial analysis was conducted to evaluate the behavioral change exhibited by bike-sharing users after the introduction of E-scooters. E-scooter data was used to compare the similarity between bike-sharing and E-scooter usage patterns, indicating that E-scooters reduced the number of spontaneous trips taken by bike-sharing users. The number of nonmember bike-sharing trips decreased for all locations, indicating that E-scooters became the more popular option for leisure activities. Furthermore, member bike-sharing system users were found to travel less on weekends than nonmembers. [ABSTRACT FROM AUTHOR]

Bike-sharing networks have achieved considerable success in many cities worldwide, gaining a growing number of supporters for this mode of transportation. While the existing literature covers various aspects related to bike-sharing, the exploration of the relationship between these networks and the financial benefits for residents when transitioning to cycling has been somewhat limited. Therefore, the main objective of our article is to identify the factors influencing urban residents' decisions to switch to cycling and to understand the significance of financial considerations in shaping changes in travel behaviour. We assessed the perceived affordability of bike-sharing services by measuring respondent satisfaction (via Computer Assisted Personal Interviews - CAPI) with the rental prices of city bicycles. To examine the relationships between variables, we employed statistical tests, including the Fisher test, the chi-square test of independence, and the Mann-Whitney test. Our research findings confirmed that replacing public transportation with bicycles has the most substantial impact, while substituting car trips has a relatively minor effect. Furthermore, our analysis revealed statistically significant associations between price satisfaction and the decision to abandon car travel in favour of cycling, as well as the motivation to save costs and substituting walking and public transport with bicycle travel. [ABSTRACT FROM AUTHOR]

The emerging demand for electric bicycles in recent years has prompted several Bike-Sharing Systems around the world to adapt their service to a new wave of commuters. Many of these systems have incorporated electric bikes into their network while still maintaining the use of regular mechanical bicycles. However, the presence of two types of bikes in a Bike-Sharing network may impact how rebalancing operations should be conducted in the system. Regular and electric bikes may exhibit distinct demand patterns throughout the day, which can hinder efficient planning of such operations. In this paper, we propose a new model that provides rebalancing recommendations based on the demand prediction for each type of bike. Additionally, we simulate the performance of our model under different scenarios, considering commuters’ varying inclination to substitute their preferred bike with one of a different type. Our empirical experiments indicate the potential of our model to improve user satisfaction, reducing the total lost demand by approximately 10%, while reducing the lost demand for electric bikes by around 30%, on average, when compared to the existing rebalancing strategy used by the real-world Bike-Sharing System under study. Remarkably, this was accomplished while maintaining an almost identical average hourly count of rebalancing operations.

The United States faces urban issues like congestion and pollution due to heavy car dependency, with over 91% owning cars and 87% driving to work. In response to these challenges, various shared mobility solutions have been integrated into transportation systems, encompassing bike-sharing, e-scooter sharing, and ride-hailing, but have not seen widespread adoption. To this end, this study investigates the perception of these services in the context of benefits and barriers. A survey was conducted in Indianapolis, Indiana and distributed online, gathering 424 responses. Latent class analysis (LCA) was used to determine groups of individuals with similar perceptions. The LCA identified three classes regarding perceptions of shared mobility benefits, and three others in relation to barriers. Regarding benefits, the classes include “Casual observers of benefits,” predominantly comprising older, less active females; “Benefits proponents,” featuring a majority of young, well-educated, higher-income males who prioritize health; and “Non-believers in benefits,” primarily consisting of older individuals, often identifying as black, with lower incomes and less active commuting. In terms of barriers, the classes are “Indifferent about barriers,” mainly comprising younger, lower-income individuals; “Shared mobility bystanders,” primarily characterized by older individuals with infrequent work commutes; and “Barrier conscious,” mostly including younger, well-educated, racially diverse individuals with complex commuting patterns. In both the benefits and barriers categories, the largest classes consist of individuals with neutral perspectives on shared mobility, signifying that a substantial portion of the population has not fully embraced these services. The study recommends policies to promote shared mobility to this significant population segment.

Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

We study the effects of bike-sharing usage on urban air quality in China. Our Difference-in-Differences strategy exploits the staggered entries by two leading bike-sharing companies, ofo and Mobike, and utilizes city-day panel data. We find that the entries of the two bike-sharing platforms can reduce PM2.5 by 3.832 ​μg/m3. Further analyses imply that the effects are more conspicuous on pollutants from the exhaust emission of vehicles, increase with the usage intensity of bike-sharing service, and are more pronounced in cities with well-developed rail transit or those located in the Beijing-Tianjin-Hebei Air Pollution Transmission Channel.

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires highly service planning methods based on bike-sharing user behavior. Negative Binomial, Poisson Regression, and Time Series models were elaborated considering the weather to reveal the differences between the members, occasional users, and visitor bike-sharing user groups. The negative Binomial approach is found to be superior to Poisson. Weather effects were varied in their influence on bike-sharing user classifications. In general, good weather conditions lead to more usage of bike-sharing. Weekends attract more occasional users and visitors than weekdays. In time series models, the seasonal trend of bike-sharing trips conducted by members was predicted without weather impact. According to the comparison, Random Forest performed better than SARIMA when the number of observations was low. Visitors are more influenced by temperature, wind and type of day. Occasional users are more subjected to precipitation. For members, it is found that the temperature, type of day are the most significant factors. The least factors for all are varied as well: precipitation for visitors, humidity for occasional users, precipitation and wind for members. The results help decision-makers predict the daily usage of bike-sharing for various user groups.

One of the critical issues in the operation of vehicle-sharing systems is the optimization of the fleet repositioning movements. Repositioning implies the artificial movement of vehicles from places where they accumulate to others in which they are scarce. This yields a higher vehicle availability, without over dimensioning the vehicle fleet and while increasing the vehicle utilization rates. In the particular case of bike-sharing systems, repositioning implies to deploy a fleet of small trucks or vans able to move groups of bicycles from one location to another, with the purpose of maximizing the users’ level of service while minimizing the operating agency costs. This repositioning optimization problem has been previously addressed in the operations research field through Mixed Integer Programing (MIP) and its variants, generally facing two limitations. First, its high computational cost, which prevents achieving direct solutions in realistically large systems. So, it has been necessary to develop heuristics and approximations. And second, its reliance and sensitivity to demand forecasts, with its inherent level of uncertainty. Aiming to overcome these weaknesses, this paper presents a strategy based on a real-time pairwise assignment between repositioning trucks and tasks, in order to optimize the bike-sharing repositioning operations. The proposed method is conceptually simple, less dependent on demand predictions, easily implementable in any coding language and applicable to large systems at a low computational cost. These properties make the method appealing to address the repositioning task assignment in any vehicle-sharing system. On a simulated case study, based on Bicing, the bicycle-sharing system in Barcelona, the proposed strategy has been implemented and compared to the MIP-based routing approach. Results show that the proposed real-time pairwise assignment method is able to significantly improve the performance of the repositioning operations, especially in scenarios where the demand forecast is not accurate. Being based on real-time information, the proposed strategy is flexible enough to solve unpredictable situations. So, the proposed strategy can be implemented as an alternative to MIP-based solutions, or as a complementary strategy for dynamic real-time adaptation of static long-term solutions.

Bike-sharing systems allow occasional and regular users to travel by replacing other transport modes for the same trip or generating a new journey. Our research assesses the demand for Lisbon's public dock-based bike-sharing system (BSS), named GIRA. This paper aims to identify the determinant factors that influence the potential of the BSS to generate new trips or replace previous modes using a conditional logit model based on a survey of 3112 BSS users. The survey results indicate that GIRA generated approximately 20% of the BSS trips, i.e., they would not have been realized if GIRA did not exist. The remaining BSS trips replaced other motorized (55%) and non-motorized (25%) trips. The main determinants explaining a higher likelihood of replacing different modes are having a yearly GIRA pass and a bike-sharing station within a 5-min walk. In contrast, regular car users are more likely to generate new trips, suggesting they use bike-sharing for recreational purposes. The findings provide policymakers with an assessment of determinants which may influence bike-sharing users to generate or replace trips from other modes and, consequently, define policies to potentially increase bike-sharing. [ABSTRACT FROM AUTHOR]

This study explores the relationship between bike-sharing and public transit trips before and after COVID-19 lockdowns, focusing on Columbus, Ohio. Using CoGo Bike Share trip data together with Automatic Passenger Counter (APC) data from the Central Ohio Transit Authority (COTA), we first identify bike-share trips and public transit ridership patterns during this period and classify bike-share trips as substitutive and complementary to public transit trips. We then employ binary logit models to analyze the determinants of substitutive trips. Some key findings are as follows. First, the COVID-19 outbreak caused decreases in bus ridership while increases in bike-sharing trips. Second, bike-sharing may compete with public transit for short distance trips. Third, bike-sharing trips are likely to substitute public transit within more intensive and congested public transit networks after controlling for various other factors. Fourth, trips made in different neighborhoods and land use reveal significant relationships with modal types. Lastly, lockdowns due to the COVID-19 pandemic led to substitutive trips substantially, particularly on weekdays. [ABSTRACT FROM AUTHOR]

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing's six central districts. Subsequent to this, a comprehensive analysis of the network's structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities. [ABSTRACT FROM AUTHOR]

One important factor in determining whether commuters will use public transport is spatial accessibility rooted in the first-mile problem. This study explores commuter behaviour in terms of how they utilize bike-sharing to manage the first-mile accessibility of a public transportation station. Historical data from Taipei Metro smart cards were analyzed using RFM (recency, frequency, and monetary) segmentation models to identify commuter segments. This study reveals two significant findings: comprehensive spatiotemporal characteristics and homogeneous behavioural patterns are derived from clustering algorithms. The city's penetration pricing strategy for bike-sharing motivates modal splitting transfer between bike-sharing and transit (MSTBT). In addition, we observed a supplementary and utilitarian relationship between bike-sharing and the metro. A convenient transportation network improves first-mile accessibility, thus the frequency of MSTBT usage is a key metric for measuring engagement. The findings provide a useful reference for urban planners promoting the design and development of sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers.

In recent decades, shared mobility has gained prominence as a sustainable alternative in transport, yet a comprehensive understanding of its effects on travel behaviour remains limited. This paper provides a narrative review of quantitative empirical studies, focusing on car-sharing and bike-sharing, and revisits the magnitude of the effects on four indicators: public transport use, active transport use, auto dependence, and auto ownership. Both cross-sectional and longitudinal perspectives are considered, examining variances in trip characteristics. Shared mobility users tend to rely less on private vehicles and increase cycling, with varying effects on transit use and walking. Car-sharing typically replaces private vehicles for non-commuting trips, while bike-sharing mainly competes with rather than complements public transport, especially for shorter commutes. The longitudinal effects of shared mobility appear more limited than those observed in cross-sectional analyses, indicating that shared mobility can potentially lead to a positive trend in travel mode shifts over time, albeit slowly. Additionally, this study highlights differences in shared mobility outcomes between Australia and other global contexts, exploring potential reasons for these discrepancies. Integrating shared mobility and other transport paradigms requires long-term strategies to shape travel behaviour towards multimodality, offering a continuum of choices covering most daily trips without private vehicles. [ABSTRACT FROM AUTHOR]

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires highly service planning methods based on bike-sharing user behavior. Negative Binomial, Poisson Regression, and Time Series models were elaborated considering the weather to reveal the differences between the members, occasional users, and visitor bike-sharing user groups. The negative Binomial approach is found to be superior to Poisson. Weather effects were varied in their influence on bike-sharing user classifications. In general, good weather conditions lead to more usage of bike-sharing. Weekends attract more occasional users and visitors than weekdays. In time series models, the seasonal trend of bike-sharing trips conducted by members was predicted without weather impact. According to the comparison, Random Forest performed better than SARIMA when the number of observations was low. Visitors are more influenced by temperature, wind and type of day. Occasional users are more subjected to precipitation. For members, it is found that the temperature, type of day are the most significant factors. The least factors for all are varied as well: precipitation for visitors, humidity for occasional users, precipitation and wind for members. The results help decision-makers predict the daily usage of bike-sharing for various user groups. [ABSTRACT FROM AUTHOR]

Dockless bike-sharing systems have grown rapidly in China in recent years. A large number of shared bikes have been put into use, which results in a serious problem of uncollected faulty bikes. However, since the exact number of faulty shared bikes cannot be known a priori, a reverse logistic network must be designed strategically to reduce the total costs of the collection process. This paper studies a robust reverse logistic network design problem of shared bikes that integrates the locations of recycling centres and the planning of collection routes in uncertain collection demand situations. A two-stage robust location routing model is presented to capture the uncertainty of collection demands, and then a set-partition reformulation is presented to address the computationally intractable model. We develop a column-and-constraint-generation-based algorithm combining a tailored column generation algorithm to efficiently solve the proposed model. Numerical experiments and parameter analyses, which are based on real data from bike-sharing companies, are conducted to evaluate the performance of the proposed algorithm. A case study is presented to further analyse the spatial features of the reverse logistic network of shared bikes. Benefits of applying robust optimization are demonstrated experimentally in terms of total cost under uncertain collection demand. • A robust reverse logistic network design problem in bike sharing is studied. • A column-and-constraint-generation based algorithm is presented. • A case study demonstrates the benefits of applying robust optimization. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatial regression performs better than count models and random forest. This study helps decision-makers in predicting the origin-destination matrix of bike-sharing trips based on the transportation network and land use.