Search Results (32)

The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts reductions in CO₂ and NO_x emissions resulting from BSS usage in Madrid by integrating real-time IoT sensor data with an advanced predictive model. The proposed framework effectively captures nonlinear and seasonal mobility and emission patterns, achieving high predictive accuracy while demonstrating significant energy savings. These findings confirm the environmental benefits of BSSs and provide urban planners and policymakers with a robust tool to extend and replicate this analysis in other cities, fostering sustainable urban mobility and improved air quality. Full article

This article aims to analyze the use of a bike-sharing system (BSS) during the flooding event caused by extreme rainfall that hit the municipality of Porto Alegre, Brazil, in May 2024. Public transport services were interrupted, prompting an investigation into the resilience of the BSS during the crisis. Considering data from the Tembici BSS company, a set of approximately 400,000 trips made between 104 stations in the municipality of Porto Alegre from January to May 2024 were analyzed. Daily rainfall data from the National Institute of Meteorology (INMET) were compared with the daily trip flow to identify the travel flow patterns on the days most affected by the flooding. The results indicate an abrupt drop in shared bicycle use during May 2024, but 7600 trips were recorded despite the crisis. Regarding the travel pattern between 1 May and 10 May, most trips were still for recreational purposes (73%), while trips for work and study accounted for 22% of the total, and only 5% were for delivery services. Overall, the resilience of the BSS during the extreme climate event in question points to the continuation of practical daily activities, although with more significant effects on economic-related activities and lesser effects on leisure-related activities. Full article

During public health emergencies, studying the travel characteristics and influencing factors of shared bicycles during different time periods on weekdays can provide valuable insights for urban transportation planning and offer recommendations for bike-sharing systems (BSS) affected by such events. Utilizing bike-sharing data, this study initiated the analysis by scrutinizing the spatial flow patterns in the core area of Beijing, employing network indicators within the framework of complex network theory. Subsequently, influencing factors associated with bike-sharing trips were pinpointed using the exponential random graph model (ERGM). Using COVID-19 as an example, it examines the impact of public health emergencies on bike-sharing during multiple time periods. Supported by the network analysis method, our findings revealed that the majority of travel activities occurred between adjacent areas. Throughout weekdays, a consistent level of travel activity was observed, exhibiting distinct patterns during daytime and nighttime. The period from 4:00 to 8:00 emerged as the peak time, characterized by heightened traffic and temperature changes. Morning commuting extended until 8:00–12:00, followed by a transition period from 12:00–16:00. The most active travel time, encompassing various purposes, was identified as 16:00–20:00. Additionally, the presence of hospitals and train stations amplified travel within the pandemic-affected area. Finally, variants of ERGMs were employed to assess the influence of finance, shopping, dining, education, transportation, roads, and COVID-19 on bike-sharing activities. The road network emerged as the most critical factor, exhibiting a significant negative impact. Conversely, COVID-19 had the most pronounced positive influence, with transportation stops and educational institutions also contributing significantly in a positive manner. This research provides valuable transportation planning insights for addressing public health emergencies and promotes the effective utilization of bike-sharing systems. Full article

In this study, we present a framework for optimizing pick-up and delivery operations in bike-sharing systems (BSSs), with particular emphasis on inventory rebalancing and vehicle routing to enhance operational efficiency. By employing a hybrid genetic algorithm (HGA), this study integrates sophisticated predictive models with multi-objective optimization techniques to strike a balance between operational efficiency and demand fulfillment in urban bike-share networks. For probabilistic demand forecasting, the DeepAR model is applied to a large number of bike stations clustered by geological proximity to enable stochastic inventory management. Our proposed HGA approach leverages both the genetic algorithm for generating feasible vehicle routes and mixed-integer programming for bike rebalancing to minimize travel distances while maintaining balanced inventory levels across all clustered stations. Through rigorous empirical evaluations, we demonstrate the effectiveness of our proposed methodology in improving service quality, thus making significant contributions to sustainable urban mobility. This study not only pushes the boundaries of theoretical knowledge in BSS logistics optimization but also offers managerial insights for practical implementation, particularly in densely populated urban settings. Full article

In recent years, numerous cities around the world have adopted bike sharing systems (BSSs). The increasing popularity of these transportation means is directly related to their eco-friendly and health advantages. Several factors affect how citizens make use of BSS, for instance, the size and configuration of a city, where docking stations are located, the associated prices, and others. Moreover, these systems have different usage patterns that vary according to the month, day, or hour but remain constant when compared yearly. However, the onset of the COVID-19 pandemic modified mobility behaviors as various governments around the world implemented mobility restrictions to avoid the spread of the virus. The objective of this investigation is to determine if the usage patterns of BSSs have changed permanently or if we have recovered pre-pandemic levels and usage patterns. Given the special characteristics of each BSS, this study focuses on Barcelona’s BSS, called Bicing. To understand the impact of other BSSs, the further analysis of each system’s unique characteristics is necessary. The study employs bike usage information from the public open data service maintained by Barcelona City Hall, namely, the Open Data BCN website, from January 2019 to December 2023, and it covers mechanical and electrical bikes with more than 4 million records per month. The results show that usage patterns were similar before and after the pandemic; the usage increased in 2021 and 2022 and stabilized in 2023, registering a rise of 17.5% in Bicing usage from 2021 to 2023. However, bike type preferences have changed for reasons unrelated to the pandemic restrictions. The main limitation of this investigation is the lack of continuity in the data due to a change in the company that provides the service in Barcelona. For future research, data from other transportation means can be used to analyze all communication behavior in this city. Additionally, if data are available, a study by gender and age can be performed and used to improve the system for certain groups. Full article

Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities worldwide strive to promote sustainable and efficient transportation options, BSS offer a flexible, eco-friendly alternative that complements traditional public transport systems. These systems, however, are complex and influenced by a myriad of endogenous and exogenous factors. This complexity poses challenges in predicting BSS activity and optimizing its usage and effectiveness. This study delves into the dynamics of the BSS in Hamburg, Germany, focusing on system stability and activity prediction. We propose an interpretable attention-based Temporal Fusion Transformer (TFT) model and compare its performance with the state-of-the-art Long Short-Term Memory (LSTM) model. The proposed TFT model outperforms the LSTM model with a 36.8% improvement in RMSE and overcomes current black-box models via interpretability. Via detailed analysis, key factors influencing bike-sharing activity, especially in terms of temporal and spatial contexts, are identified, examined, and evaluated. Based on the results, we propose interventions and a deployed TFT model that can improve the effectiveness of BSS. This research contributes to the evolving field of sustainable urban mobility via data analysis for data-informed decision-making. Full article

Decades ago, bicycles used to play an important role in urban transportation in Chinese cities, but they have been gradually replaced by private cars, metro, buses, and some other modes, owning to the fast-growing mobility demand as a result of urban expansion and motorization. However, in recent years, with the development of Information and Communication Technologies (ICT) and the initiative of the sharing economy, bike-sharing systems (BSSs) have been implemented extensively in Chinese cities. Their usage patterns can be revealed via system-generated data, yet less is known about users’ attitudes towards and preferences for these systems. In this study, we draw on two surveys conducted in Guangzhou and Beijing on the perceptions of travelers using BSSs to estimate the effect of demographic factors, bicycle ownership, and trip-level factors on the willingness and potential frequency of BSS usage. In addition, a latent class model is built to analyze the different aspects of theses systems concerned with different types of urban travelers. It is found that respondents’ age, occupation, income, mode combination, and the proximity of origin or destination to the docking station, etc., influence the willingness and frequency of using BSSs. In addition, respondents generally value features such as the proximity of docking stations to trip destinations, safety to ride, and appropriate level of fare. However, different latent classes show a different preference for other features of BSSs. According to the model results, proposals are given for the improvement of the existing systems in Chinese cities. Full article

Due to global ecological restrictions, cities, particularly urban transportation, must choose ecological solutions. Sustainable bike-sharing systems (BSS) have become an important element in the worldwide transportation infrastructure as an alternative to fossil-fuel-powered cars in metropolitan areas. Nevertheless, the placement of docks, which are the parking areas for bikes, depends on accessibility to bike paths, population density, difficulty in bike mobility, commuting cost, the spread of docks, and route imbalance. The purpose of this study is to compare the performance of various time series and machine learning algorithms for predicting bike demand using a two-year historical log from the Capital Bikeshare system in Washington, DC, USA. Specifically, the algorithms tested are LSTM, GRU, RF, ARIMA, and SARIMA, and their performance is then measured using the MSE, MAE, and RMSE metrics. The study found GRU performed the best, with RF also producing reasonably accurate predictions. ARIMA and SARIMA models produced less accurate predictions, likely due to their assumptions of linearity and stationarity in the data. In summary, this research offers significant insights into the efficacy of diverse algorithms in forecasting bike demand, thereby contributing to future research in the field. Full article

Recently, the rapid development of the bike-sharing system (BSS) has dramatically influenced passengers’ travel modes. However, whether the relationship between the BSS and public transit is competitive or complementary remains unclear. In this paper, a difference-in-differences (DID) model is proposed to figure out the impact of the dockless BSS (DBSS) on bus ridership. The data was collected from Shanghai, China, which includes data from automatic fare collection (AFC) systems, automatic vehicle location (AVL) systems, DBSS transaction data, and point-of-interest (POI) data. The research is based on the route-level, and the results indicate that shared bikes have a substitution impact on bus ridership. Regarding all the travel distance, each shared bike along the route leads to a 0.39 decrease in daily bus ridership on the weekdays, and a 0.17 decrease in daily bus ridership on the weekends, respectively, indicating that dockless shared bikes lead to a stronger decrease in bus ridership on weekends compared to weekdays. Additionally, the substitution effects of shared bikes on bus ridership gradually decays from 0.104 to 0.016 in daily bus ridership on weekends, respectively, with the increase in the travel distance within 0–3 km. This paper reveals that the travel distance of passengers greatly influences the relationship between the DBSS and public transit on the route level. Full article

During recent years, bike sharing systems (BSS) have been adopted in many large cities around the world. Thanks to their environmental and health benefits, BSS’ popularity as a green transportation mode is exponentially increasing and many small cities are also adopting them. However, few of these small cities have the resources to manage and analyze the massive amount of data produced by these systems in order to optimize them and promote their use among citizens. This manuscript analyzes BiciLog (Logroño, Spain) data and studies customers’ usage patterns, disaggregated by gender and age. The t-test is the inferential statistic test employed to compare the equality of the means among different groups. Results show differences in how women and men are using the BiciLog system. Women use the system less but ride for longer than men. There are also differences between age groups. Most of the users are between 20 and 29 years old. However, customers between 60 and 69 years old are also extensively using BSS. In fact, they not only make more trips but also their rides are around three times longer than customers in other age groups. These results can be used by BiciLog operators to create and evaluate campaigns to motivate BSS use among target groups and improve the system based on customers’ preferences. The main limitation of this investigation is the lack of data available to calculate additional information such as the real distance covered by customers when riding, or their preferred routes. For future research, a longer data period can be considered to compare usage patterns across different years. Additionally, customer surveys can help us to understand their motivations to use the system and corroborate the results found in this study. Full article

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier. Full article

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model. Full article

An e-bike sharing system (e-BSS) solves many of the shortcomings of BSS but requires high financial investments compared to BSS. This article proposes a sustainable and targeted extension of the existing BSS with e-bikes and charging piles. The existing BSS in the selected city area is divided into sub-areas using the Voronoi diagram and reference points (landmarks). Then, the integrated approach of the Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) is used to assess the adequacy of the existing bike-sharing stations for updating with e-bikes and charging piles. The joint approach allows decision-makers to look at the whole process and highlight the link between the criteria assessment and user preferences in the context of the chosen reference point. This can encourage future users to use e-BSSs. Based on a thorough literature review, the defined system of criteria takes into account all dimensions of sustainability: the requirements of most stakeholders and the structural features and needs of e-BSS. Finally, the super-efficiency DEA is used to classify the suitable candidates for bike-sharing so that only the most suitable stations are updated. The test of the proposed algorithm in Ljubljana city centre confirms several suitable options for updating the BSS, depending on the reference point. Full article

Bicycle-sharing systems (BSSs) are an effective solution to reduce private car usage in most cities and are an influential factor in encouraging citizens to shift to more sustainable transport modes. In this sense, the location of BSS stations has a critical impact on the system’s efficiency. This study proposed an integrated geographic information system–multi-criteria decision-making (GIS-MCDM) framework that includes the analytic hierarchy process (AHP), technique for order preference by similarity to the ideal solution (TOPSIS), and spatial data processing in GIS to determine a ranking of potential locations for BSS stations. The results of the proposed GIS-MCDM method can be used for both planning a new BSS or expanding one that is currently under operation. The framework was applied to a case study for expanding GIRA, the BSS of Lisbon, Portugal. In it, location criteria were selected in four categories, including criteria from the literature and extracted from available transaction data; in addition, we also suggested some criteria. The rebalancing operator’s staff were the decision makers in this study via their responses to the AHP questionnaire. The rebalancing staff believed that the main criterion of “city infrastructure” with the two sub-criteria of “population density” and “slope” were the most important. Furthermore, the proximity to the “bike network” with the sub-criterion of “proximity to the current bike stations” had less importance. Each criterion’s weight and inconsistency rate were obtained using the Expert Choice software. The geographic values of each criterion were created utilizing the ArcGIS software, and its network analyst module was employed for applying location techniques. Based on the created suitability map, the city’s center was the main suitable area for establishing new stations. Forty-five new bike stations were identified in those areas and ranked using the TOPSIS technique. Full article

Bike-sharing systems (BSS) are offered in many cities and urban municipalities and urban areas without such systems are thinking about their introduction. In addition, many studies on BSS are available; however, neither mode nor route choice parameters are available for station-based BSS, which are required for the implementation of BSS in local and regional transport demand models. As a result, this makes it impossible to simulate demand model-based effects of these systems on other transport modes and e.g., calculate scenario-guided modal shifts. The paper presents results obtained from a survey study, which aims to estimate BSS-related choice parameters. The study combined computer-assisted telephone interviews (CATI) for a collection of revealed preferences (RP) on the use of BSS with a follow-up paper-and-pencil survey on stated preferences (SP) of 220 BSS users and non-users from the Rhine-Neckar area in mid-west Germany. Considering the three transport modes BSS, public transport (PT), and private motorized transport (PMT), results from this choice experiment and, according to behavioural parameters, allow integration of BSS in transport demand models and a simulation of modal shifts. Survey design, mode-choice experiment, and choice models are presented in this paper. Full article