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Dockless shared micromobility services have grown substantially in recent years, but their impact on consumer demand has remained largely unstudied. The authors estimate how the largest and fastest-growing segment of this market--the dockless electric scooter ("e-scooter") sharing industry--impacts spending in one of the largest segments of the local economy, the restaurant industry. Using data covering 391 companies in 98 U.S. cities, the authors find that the introduction of e-scooters in a city significantly impacts restaurant spending, increasing spending by approximately 5.2% for e-scooter users, driving incremental spending of at least $11.3 million annually across all cities that first allowed e-scooters to operate over summer 2018. Impact varies by restaurant subcategory, with a stronger positive effect on fast-food restaurant spending, and a weaker effect on sit-down restaurant spending. E-scooter entry has a larger impact on companies with higher historical revenues selling at lower prices. It facilitates discovery of new restaurants from prospective customers and repeat business from already-acquired customers. [ABSTRACT FROM AUTHOR]

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei's official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters. [ABSTRACT FROM AUTHOR]

Despite many years of research and rapid changes in the field of bicycle route choice modelling, a review of factors associated with cyclists' route choice decisions is missing. This paper reviews 33 studies that employed GPS data and statistical modelling to explain the route choice behaviour of cyclists on utilitarian trips. Factors associated with bicycle route choice are divided into network, contextual, and individual factors. Findings from this review demonstrate that a refined categorisation of model attributes is essential, as the preferences vary greatly between different route attributes. Moreover, the analysis indicates a strong influence of the local context on the attribute definitions and route choice preferences. Although some commonalities between studies w.r.t. the network factors (e.g. a preference towards bicycle infrastructure or avoidance of slopes) can be determined, many results are contradictory or intertwined with other factors. Specifically, results related to intersections, land use characteristics, and interaction with other modes of transport are not always conclusive and are strongly dependent on the local context. However, the explanation of some of the unclear effects can be facilitated by incorporating contextual or individual information in the models. Based on the analysis of the existing studies, this review encourages a joint consideration of studies employing quantitative and qualitative methods to explain cyclists' behaviour. Moreover, reporting model results in an accessible form (e.g. through relatable values or visualisation) is essential to build bridges between bicycle research and policymaking. Future research is encouraged to study cyclists' route choice decisions from the perspective of objective well-being, quantifying and modelling the influence of factors such as safety, health, energy expenditure, or noise exposure. [ABSTRACT FROM AUTHOR]

Bicycle sharing systems are becoming highly popular and there have accumulated a large volume of users' trajectory data. In a bicycle sharing system, most users' borrowing and returning behaviors are arbitrary. Importantly, the bicycle sharing system is affected by weather, time as well as other dynamic factors under non-classical conditions. These factors make the scheduling of shared bicycles imbalanced, which is a typical machine learning problem under non-classical conditions. In addition, imbalanced bicycle scheduling impacts user experiences and causes huge economic losses. In this study, we propose a three-in-one dynamic shared bicycle demand forecasting model under non-classical conditions. The proposed model contains three essential and specific techniques: (1) Clustering stations based on the idea of data field. The activity degree of a station is calculated by constructing a bicycle transition network. The stations' geographical locations and the bicycle transition patterns are taken into consideration. Then, nearby stations with similar transition patterns are aggregated into a cluster based on data field. (2) Modeling dynamic factors under non-classical conditions. The Pearson correlation coefficient is used to choose the most relevant dynamic weather features from real data. These features are transformed into a three-dimensional vector by taking into account the historical demand for bicycles in the station cluster. (3) Predicting bicycle demand using a LSTM (Long Short-Term Memory) network with multiple features that learns the features from the three-dimensional vector. The bicycle demand in each cluster is forecasted in a real-time and dynamic fashion every ten minutes. These three techniques collectively achieve one goal: accuracy prediction with low errors in real-time response. Extensive experiments are conducted on real Citi Bicycle data to compare the proposed model with traditional machine learning algorithms as well as the state-of-the-art approaches. The results demonstrate the superior performance of the proposed model in different evaluation measurements. [ABSTRACT FROM AUTHOR]

Bicycle-sharing services have received worldwide attention as a sustainable form of transportation. This study explores a priori acceptance of public bicycle-sharing system (PBSS) in India, which is still in the early phases of adopting PBSS. The effects of psychological factors, such as perceived usefulness (PU), perceived ease of use (PEoU), perceived fun (PF), health value (HV), and environment values (EV), on intention to use PBSS are studied, utilizing an extended technology acceptance model. 747 samples were collected from online questionnaires in Dehradun, India. Results of Structural Equation Modelling revealed that intention to use PBSS is strongly predicted by PU and PF together, while PF mediates the influence of PEoU. Furthermore, EV and HV generate positive behavior intention to use PBSS through PU, PF, and PEoU. The study suggests promoting PBSS as a green and active transportation mode offering high PU and enjoyment, with theoretical and practical implications discussed. [ABSTRACT FROM AUTHOR]

A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times. [ABSTRACT FROM AUTHOR]

This case asks students to assume the role of a newly hired (data) analyst for Divvy, Chicago's bike share program. In this role, they use data analytics software to perform data analysis and data visualization techniques. This case can be implemented in multiple parts, and it is scalable for the unique needs of instructors and their students. Introductory topics include assessing the properties of the data, describing data validation techniques, creating calculated variables, and constructing basic data visualizations. Advanced topics include joining additional data sources, developing and investigating research questions, and creating dashboards to tell a meaningful story about the data. The case is unique because it enables students to interact with a large, real-world dataset, and it is ideal for accounting information systems or data analytics courses. [ABSTRACT FROM AUTHOR]

Our research presents a methodological framework for analyzing bicycle-sharing systems, using the self- service bike operations of JCDecaux in Toulouse as a case study. The objective was to identify a method for obtaining a cleansed and structured attribute list that could be useful in evaluating and optimizing the placement of bicycle rental docks. Utilizing open data, our approach involves developing a Python script within QGIS to create new layers around each of the 288 studied bicycle rental stations, based on a selected 100-meter buffer. This buffer size is chosen to reduce data overlap in dense urban settings. The script is designed to collect urban features within these buffers that register as multipolygons (mainly buildings) or points (amenities, transportation features), moreover it applies categorization of data, such as identifying and marking the different building types. The method includes a basic visualization of potential data in QGIS using OpenStreetMap. [ABSTRACT FROM AUTHOR]

Purpose: Product-service systems (PSS) are regarded as highly sustainable solutions. However, studies identifying and comparing the sustainable potential of product-service offerings by considering the three sustainability dimensions are still scarce. This paper aims to benchmark and analyse the sustainable potential of a use-oriented PSS, showing the influence of the context of implementation on the sustainable potential of the solutions. Design/methodology/approach: By adopting a competitive benchmarking approach, six bicycle-sharing systems from different countries were selected for analysis. The main sustainability-related aspects in use-oriented PSS (the systems investigated) were identified through a literature review. Multiple secondary sources were used to collect data about the analysed PSS. A qualitative analysis was conducted through triangulation of the sources to identify and compare the systems by considering the selected sustainability aspects. Findings: The main results show that use-oriented PSS provide a range of economic, social, and environmental benefits, confirming the sustainable potential of such solutions. Several similarities between the systems have been identified, along with some differences, especially regarding their integration with other transport systems and the use of renewable energy, which can affect users' acceptance, operation efficacy, and overall sustainable potential of the solutions. Practical implications: This study identifies best practices that can be considered by other bike-sharing businesses to improve their sustainability potential. Originality/value: This study identifies and explores the sustainable potential of bicycle-sharing solutions using a benchmark approach. It augments existing empirical knowledge on sustainable PSS and business models by revealing best practices, including the context that may enhance the sustainability potential of the solutions regarding environmental, economic, and social benefits. [ABSTRACT FROM AUTHOR]

Previous literature has shown mixed findings on whether bike or e-scooter share programs, collectively called shared micromobility, play a role in reducing vehicle travel. In this study, we focused on two types of shared micromobility (i.e., bikes—both regular and e-bikes—and escooters). We examined their impact on vehicle travel in urbanized areas in the United States between 2012 and 2019. Employing the difference-in-differences (DID) approach, we quantified the collective effect of shared micromobility on daily vehicle miles of travel (VMT) per capita at the urbanized area level. Our findings suggest that bike shares could reduce VMT while e-scooter share alone might not have a significant impact on lowering vehicle travel. Our results also indicate that there may be a synergistic effect of bike and scooter shares in VMT reduction when both types of shared micromobility are available in an urbanized area. Our findings also demonstrate regional variations in the impact of shared micromobility on vehicle travel. [ABSTRACT FROM AUTHOR]

The objective of a hop-on-hop-off sightseeing bus is to provide the highest accessibility to the most desirable attractions of the place. Four tourism cities worldwide, London, Vienna, Edinburgh, and Singapore, were studied to benchmark the system's spatial and non-spatial criteria. The head/tail breaks principle sets up the threshold to measure the popularity, and the space syntax measures to evaluate the accessibility. The primary goals for a sightseeing bus system should consider the benefits of the bus takers, the revenue and costs of system providers, and the marketing and competitiveness of local authorities. Hence the length and number of bus stops per route should be limited to no more than 1.5–2 hours per loop. The four accessibility measures for attractions were considered higher than the city's second-rank (M2) mean value. The results showed that in the optimizing process using the benchmarked criteria, the coverage ratio of Grade 1, 2, and 3 attractions was enhanced by 25%, 42%, and 20% higher, respectively, than the existing sightseeing bus system in Taipei City. This optimizing accessibility process should be transferable to other means by planners to increase the tourist visits, such as the bicycle system, shared scooters, and walking areas. [ABSTRACT FROM AUTHOR]

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing's urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development. [ABSTRACT FROM AUTHOR]

Facing the Bicycle Rebalancing Problem (BRP), we established a Rebalancing Incentive System (BRIS). In BRIS, the bicycle operator proposes the method of financial compensation to encourage cylclists to detour some specific stations where the number of bikes is excessive or insufficient and access suitable sations. BRIS mainly includes two objects: the Bike Gym imitating the bicycle environment, and the Spatiotemporal Rebalancing Pricing Algorithm (STRPA) determining the amount of money which is given to the cyclist depending on time. STRPA is a deep reinforcement learning model based on the actor–critic structure, which is the core concept of this paper. In STRPA, the hierarchical principle is introduced to solve the dimensional disaster, and the graph matrix A is introduced to solve the complex node relationship. In addition, the traffic data including the bicycle have strong temporal and spatial characteristics. The gated recurrent unit (GRU), the sub-module of STRPA, can extract the temporal characteristics well, and the graph convolution network (GCN), also a sub-module of STRPA, can extract the spatial characteristic. Finally, our model is superior to the baseline model when verified on the the public bicycle data of Nanjing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Sport & Health Research is the property of Journal of Sport & Health Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bicycle parking is a critical piece of cycling infrastructure and yet there is little known about who and how often these facilities are used. This study investigates the use patterns, occupancy, duration times, and rotation rates of on-street bike parking in Barcelona, Spain. We sampled all city districts with rectangular plots that included 163 bicycle parking sites. We visited them 4–15 times during two field work campaigns in spring and summer 2021. We measured total occupancy, bicycle type (bike-share, children, abandoned, private), and developed a method to estimate rotation rates, turnover and service levels. We estimate that Barcelona has approximately 18,730 bicycles parked on the street every day, of which 8% are dockless shared bikes, 5% are children bikes and 3% are abandoned bikes or bike parts. We find that 10% of bike parking sites were saturated (average occupancy 90%+) and 30% reached saturation in at least one measurement. Contrary to our hypothesis, we do not find strong diurnal temporal variations, as occupancy rates are remarkably stable over the day across the city. We estimate that 35% of bicycles parked on the streets are for long term parking (1 week+) or storage, while only 12% of bicycles are short-term day-users. To improve the management of on-street bicycle parking, cities must maintain an updated spatial data set of bike parking locations and understand the patterns of who and when they are used. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An observational study is presented through video recordings, with the aim to identify the factors that motivate the cyclist’s decision to not respect the red light in a traffic light intersection. The results of the chi-square statistical analysis shown the following factors: “men aged 15-30 years”, the use of “visual search” and the “size group”, influence the decision to cross in red light. While the use of a cycling helmet, cell phone, face-mask, bicycle of sharing system and reflective vest are associated to the respect of the stop sign while the red-light indication is on at the traffic light. [ABSTRACT FROM AUTHOR]

Bicycling is an important form of active transport that contributes to sustainability mobility as a result of its role in personal and public health and emissions reduction. The significance of which has grown since the COVID‐19 pandemic outbreak. However, biking studies have neglected, in theoretical terms, developing an understanding of why consumers bike. Therefore, this research designs and verifies an extended theory of planned behavior adding personal and public health and a moderator of perceived smart application usage to help explain such consumer behavior. This study is based on a digital survey of South Koreans who biked for leisure, tourism, and/or work, utilizing partial least squares‐structural equation modeling with multi‐group analysis and Fuzzy‐set Qualitative Comparative Analysis. Results revealed that personal health is most important to cyclists, followed by public health, attitude, and subjective norm. Interestingly, people with perceived high usage of smart applications for biking show stronger relationships between public health and attitude and perceived behavioral control and behavioral intention than low users. In contrast, individuals with perceived low usage of smart applications for biking reveal a stronger relationship between attitude and behavioral intention than high users. The high and low user groups of smart applications also distinctively differ in levels of cycling behavior. Consequently, this work offers several theoretical and managerial implications for research and practice. [ABSTRACT FROM AUTHOR]

Background: Individuals with serious mental illness (SMI) die 10–25 years earlier than the general population, partially due to cardiovascular disease. Those with SMI have poor cardiovascular disease behavioral risk profiles, including low physical activity engagement. This study examined the feasibility of providing city bike-share access to individuals with SMI. Methods: Participants were outpatients with SMI in a psychiatric rehabilitation program and were provided with a complimentary bike-share program membership (Nice Ride). Demographic and clinical variables were extracted from medical records. Lab values, body mass index, 10-year cardiovascular disease risk score, and tobacco use were collected at intake. Nice Ride provided ride frequency and duration data. Results: Of 22 SMI patients enrolled, 72.7% completed ≥1 ride. Among users, median ride length was 9 minutes (interquartile range = 49.5) and 56.3% averaged ≥10 minutes of riding per week. Individuals living <0.5 miles from a station were more likely to be users. Riding ≥10 minutes per week was associated with lower body mass index and male gender. Conclusion: Further work is warranted to determine if bike-share programs offer a feasible, scalable, and cost-effective method for increasing physical activity in those with SMI. These findings are currently being used for program adaptation to increase uptake. [ABSTRACT FROM AUTHOR]

Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial- and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems. [ABSTRACT FROM AUTHOR]

Free-floating bike share (FBS) represents a new generation of bike share schemes and provides mobility opportunities that influence people's daily travel. Understanding the determinants of FBS use can provide a basis for the further development of bike share programs and support related policymaking. Previous studies have used survey data with rather small samples and have focused only on FBS users while ignoring nonusers. The influences of information and communication technology (ICT) use and social context are under-researched. By using a dataset that is nationally representative of the potential users of FBS in China, this study applied a two-stage Bayesian multilevel hurdle model to investigate participation in and the corresponding usage of FBS. The independent variables include sociodemographics, ICT use, travel characteristics, physical environment, and social context. We found that ICT use has a significant effect on both participation in and the usage of FBS, social context only has a significant effect on participation in FBS, and age and annual individual income have nonlinear effects on the usage of FBS. This study provides policymakers and FBS operators with suggestions for promoting FBS use. [ABSTRACT FROM AUTHOR]

Demand for various bicycles and sharing systems has constantly been growing worldwide as they improve the quality of life and promote eco-friendly transportation. Accordingly, it is inevitable that bicycle roads should be expanded. As bicycle roads have a relatively lower load applied than automobile roads, adopting a design method that uses a high reclaimed asphalt pavement (RAP) content can be beneficial. However, much uncertainty still exists about the relation between the mixing method and application in field sites, without appropriately considering the quality control of the rejuvenator. Therefore, this study aims to demonstrate the effect of base oil as a rejuvenator on aged binders, considering the use of a high RAP content for bicycle roads. To prepare the aged binder, a rolling thin-film oven (RTFO) and pressure aging vessel (PAV) were used to imitate the life cycle of asphalt pavement from production to service life, and then three contents of aged binder (0%, 50%, and 100%) were added and mixed with fresh PG 64-22 base binder. Finally, each type of prepared aged asphalt binder was blended with three different base oil contents (0%, 5%, and 10%). The results indicated that (1) the addition of base oil effectively decreased the viscosity of aged binders, (2) aged binders containing base oil showed less G*/sin δ compared to originally aged binders, and (3) the application of base oil improves the cracking properties of the aged binder by decreasing stiffness. In conclusion, the most striking observation from the data analysis from the Superpave test and statistical results was the effect of reducing the asphaltene portion based on the use of base oil in the aged binder. Therefore, using base oil in RAP can enable the application of a high RAP content to the bicycle road. [ABSTRACT FROM AUTHOR]

The last two decades have exhibited a profound transformation of traditional urban mobility patterns partly due to the exponential growth in both number and popularity of public bicycle sharing systems (BSS). Analysis and visualization of the data generated by BSSs have become of special interest to municipalities to evaluate the effect of their mobility programs and offer integrated urban mobility solutions. In this paper, we present a visualization system that aims to assist city officials from small and medium cities in their decision-making process with an intuitive representation of BSS' data. It has been developed, tested, and evaluated together with officials and domain experts from the city of Logroño (Spain). Our tool presents usage information with different time granularities (yearly, monthly, weekly, or seasonally), shows traffic flows between stations, and provides an in-depth breakdown of users' data such as their registered address, traveled distance, or gender-based patterns. [Display omitted] • Web-based exploratory tool to analyze usage trends of bike sharing systems. • Analysis tool to support mobility policies planning. • A report-mode that enables generation of data-based detailed reports on demand. • Scalability to other bike sharing systems with similar needs. [ABSTRACT FROM AUTHOR]

Purpose -- What motivates Sao Paulo citizens to use bicycles as a mode of transportation? To answer this question, we conceptually rely on the Goal-Framing Theory (Lindenberg, 2000), which seeks to understand how individual choices are made, and exogeneous variables such as climate and temperature which might influence in this decision. Design/methodology/approach -- Empirically, we conduct a quantitative study, relying on primary data from 252 respondents and analyzed by means of logistic regression models, through which we obtained the likelihood of using bikes and the relative weights of each of the variables defined in our theoretical section. Findings -- What motivates Sao Paulo residents are not economic reasons, but rather normative (doing what is right) or hedonic (the pursuit of personal satisfaction) aspects. In other words, we demonstrate that cyclists make this choice to comply with a norm, seeking for benefits to the environment and directly or indirectly to society, as opposed to benefitting individually from this decision. Practical implications -- The research has implication for public policies. Originality/value -- The study is one of the first to empirically test the Goal Framing Theory (Lindenberg, 2000) and, as far as we could tell, the first to assess the motivations for the use of bicycles as a means of transportation in Brazil and in Latin America. [ABSTRACT FROM AUTHOR]

The bicycle sharing systems (BSSs) have been deployed extensively in Chinese cities. Would bicycle sharing helps to mitigate vehicle emission in Chinese cities? In this study, we attempt to investigate this question by estimating the emission reduction due to BSSs based on the mode shifts analysis in two largest cities in China, Beijing and Shanghai. The characteristics of the bikesharing trips are extracted from sample Mobike data. In order to examine whether the dockless BSSs can promote the transit ridership, the Difference-in-Difference (DID) analysis was conducted using the big data for BSSs and transit trips in Shanghai. The aggregated changes in mode shares suggest that BSSs in Beijing had a more significant impact on other modes, especially car trips, than the counterparts in Shanghai. However, the Mobike sample trips for Beijing was mostly very short in distance, which indicates most BSSs trips were unlikely to directly substitute car trips if not accounting for the changes in destination choices. Although BSSs provides an alternative connection option for urban transit system, the result of DID analysis shows the large scale deployment of the dockless BSSs in Shanghai hadn't appeared to evidently stimulate the usage of the metro system. This finding doesn't support the claim that BSSs could contribute to the emission reduction by complementing urban transit system. More empirical studies are needed in order to assist policy makers to perform more neutral assessment when regulating, subsidizing BSSs or integrating BSSs with other transit systems and policies. [ABSTRACT FROM AUTHOR]

This article addresses the one-commodity pickup and delivery traveling salesman problem (1-PDTSP), which is a generalization of the well-known traveling salesman problem. The 1-PDTSP aims to find a Hamiltonian tour in which a set of supply points (pickup locations), demand points (delivery locations) are visited and, the total traveled distance is minimized. We propose a hybrid metaheuristic based on multi-start evolutionary local search and variable neighborhood descent to solve the 1-PDTSP. To test the performance of our algorithm, we solve instances with up to 500 nodes available in the literature and we demonstrate that our approach is able to provide competitive results when comparing to other existing strategies. Since a direct application of the 1-PDTSP arises as the bicycle repositioning problem, we also use our metaheuristic algorithm to solve a set of real-case instances based on EnCicla, the bicycle sharing system in the Aburrá Valley (Antioquia, Colombia). [ABSTRACT FROM AUTHOR]

A resilient transport network, which is significant for urban sustainability and security, is characterized by its ability to recover from disruptions subject to natural and man-made disasters. Bike sharing could act as a viable alternative in the case of public transit disruptions given its flexibility and various social, environmental, and economic benefits. This study aims to estimate quantitatively the potential of bike sharing to promote transport resilience, by using autoregressive negative binomial time series model to investigate the effects of public transit closures on bike sharing demand in Washington, D.C. area during 2015–2017. We find that (1) bike sharing can act as a supplementary mode to enhance urban transport resilience in the case of complete transit closure; (2) the proximity of bike sharing docks to metro stations has a powerful effect on propensity to use a bike sharing program; and (3) extreme weather is one of major barriers to bicycling. Planners can enhance resilience of urban transport networks by fully considering the capacity and usage of bike sharing docks, as well as the coherence of metro stations and bike sharing docks, in distributing and rebalancing activities. [ABSTRACT FROM AUTHOR]

This research looks at how spatial factors of public transportation influence the use of bike-sharing in an urban context. Based on a grid-cell-based method, ordinary least square regression and geographically weighted regression (GWR) are developed to reveal the link between the spatial distribution of bus, tram, rail stops, and bike-sharing trips. The public transportation coverage in Budapest City is rated as excellent, with all parts of the city covered within a 5 km radius. We find that areas with a high proportion of public transportation stops have a significantly higher number of bike-sharing trips. Bike-sharing trips are concentrated near regional railway stations, the central business district, and surrounding zones. The connection between bike-sharing and trams/rails appears to be stronger than the connection between bike-sharing and buses. According to the findings, nearly one-third of public transportation stops have accessible bike docks within 125 m walking distance. In GWR analysis, the coefficients of bus stops are increasing towards the center of the city, while the coefficients of tram/rail stops are decreasing. Finally, by examining the priority zones for establishing more bike-sharing facilities, it is discovered that the eastern side of the city requires more development than the western side because it has a high number of bike-sharing trips but no adequate facilities near public transportation facilities. [ABSTRACT FROM AUTHOR]

Tourist destinations are often inaccessible due to mobility problems. The purpose of this paper is to identify the mobility problems of tourist destinations in remote areas of natural value. The research was carried out in the following two tourist destinations with the above-mentioned values: in the Zaoneshye Region in Russia and the Hajnówka Poviat, which includes the priceless resources of the Polish part of the Białowieża Forest. The research was conducted using a survey method. Respondents could download the survey questionnaire onto their mobile devices (smartphone, tablet) by scanning a QR code or provide their answers to the questionnaire on paper or to an interviewer, who recorded them in an electronic version. The respondent group consisted of tourists visiting both regions for tourism purposes. The survey was carried out between 2019 and 2021. The results showed that the car is the preferred means of transport in both regions, and that road works are somewhat or completely necessary. Additionally, tourists in the Hajnowka Poviat travel a lot on foot or by bicycle, as there are more cycling and pedestrian paths available. In contrast, tourists visiting the Zaoneshye Region suggest providing more facilities for tourism and better and more efficient communication. [ABSTRACT FROM AUTHOR]

Compared to motorized modes, bike-sharing systems (BSSs) are generally recognized as an environmentally friendly mode of transport and mobility. Due to the advantages and benefits of BSSs, they have spread globally in the past decade. The mapping knowledge domain (MKD) technique is an important tool for bibliometric analysis that can directly reflect the development status and trends in a research field and has been extensively applied in natural science, medical science, engineering and technology, humanities and the social sciences. In this paper, we conduct a systematic analysis of the development trend in bike-sharing studies taken from Web of Science (WoS) Core Collection articles published between 2010 and 2020 using the MKD software tool VOSviewer and CiteSpace. The results show that the topics of the cited documents can be divided into three categories: (a) development, operation mode and lessons learned; (b) BSS static rebalancing problem; (c) spatiotemporal characteristics and demand prediction. Next, we conduct document co-citation analysis and keyword co-occurrence analysis to visually explore the research trends in bike-sharing studies. Our results also find that, (a) bicycle rebalancing problem, (b) travel behavioral movements and barrier, (c) impact factors and characteristics of the internal usage demand, (d) innovation and sustainability for BSSs in the future, and (e) built environment and land-use, are the five major research areas and interests for bike-sharing studies. Finally, examining the trends in BSSs studies by identifying keyword bursts allows an on-demand characteristics analysis based on multisource data fusion technology. This study expands the application field of the MKD analysis method and promotes the development of BSSs against a background of new technology innovation. [ABSTRACT FROM AUTHOR]

Introduction: Urban mobility (such as walking or biking) and public Bike-Sharing Systems (BSS) have begun to emerge through programs and services that drive change from the strong culture of dependence on motor vehicles in most developed countries. At the international level, the 2030 Agenda of the United Nations (UN) opted to establish objectives for sustainable development, addressing the need to establish changes in urban mobility. The World Health Organization (WHO) has concluded that physical inactivity and being overweight / obesity are the fourth and fifth risk factors for non-communicable diseases in the world, respectively. The objective of this study is to know if a BSS has been able to serve as a means of promoting Physical Activity (PA) in the urban environment. Material and Methods: A quantitative and longitudinal study has been designed with data collection and analysis from July 2009 to January 2012. The sample consisted of a total of 3,268 users and a total of 59998 observations were recorded (Men: n = 42,411; Women: n = 17,587). SPSS 21.0 and ANOVA were used for statistical analysis. Results: Users between the ages of 20 and 44 have spent an average of 30.7 minutes, between 45 and 64 years an average of 36 minutes and between 65 and 79 years an average of 51.1 minutes. Conclusions: The data suggest that the WHO (2010) PA recommendations may be being met. BSS can favor the practice of PA in the urban environment, and thus, be able to promote less polluted and traffic-congested environments. Men from 65 to 79 years old have had the most average minutes of use. The routes that took the most minutes were those that go between the same stations and those that pass between stations on the urban outskirts. [ABSTRACT FROM AUTHOR]

The main objective of the current research is to identify and prioritise the obstacles to using bicycle sharing systems (BSSs) in the Tehran metropolis. The methodology of this research is analytical-descriptive and it aims to achieve applied goals. To do so, firstly the obstacles are identified through studying the theoretical and practical foundations of the issue and then by delving into factors associated with BSSs in the Tehran metropolis, extracting them through the content validity method. Then, the interrelations among the specified obstacles and their impact and effectiveness are determined through structural and MICMAC modelling. The data collection tool employed in the research assumes the forms of a survey and a face-to-face interview. According to the results, the following variables are among the ones associated with obstacles having the maximum impact on other obstacles: the financial problems and the disparity within the municipal management, culturalisation, educational shortage and negligence in using the participatory capacities of civilians. Before executing and taking any other strategy to overcome the obstacles, the forthcoming model supports Tehran metropolis municipal managers to identify the existent obstacles of the field in order to enable them to utilise a suitable approach as to lodging BSSs. [ABSTRACT FROM AUTHOR]