Search Results (26)

The rapid expansion of bike-sharing systems has introduced significant management challenges related to spatial-temporal demand fluctuations and inefficient e-fence capacity allocation. This study proposes a Spatio-Temporal Graph Attention Transformer Network (STGATN), a novel hybrid deep learning framework for dynamic demand forecasting in bike-sharing e-fence systems. The model integrates Graph Convolutional Networks to capture complex spatial dependencies among urban functional zones, Bi-LSTM networks to model temporal patterns with periodic variations, and attention mechanisms to dynamically incorporate weather impacts. By constructing a city-level graph based on POI-derived e-fences and implementing multi-source feature fusion through Transformer architecture, the STGATN effectively addresses the limitations of static capacity allocation strategies. The experimental results from Shenzhen’s Nanshan District demonstrate the performance, with the STGATN model achieving an overall Mean Absolute Error (MAE) of 0.0992 and a Coefficient of Determination (R²) of 0.8426. This significantly outperforms baseline models such as LSTM (R²: 0.6215) and a GCN (R²: 0.5488). Ablation studies confirm the model’s key components are critical; removing the GCN module decreased R² by 12 percentage points to 0.7411, while removing the weather attention mechanism reduced R² by nearly 5 percentage points to 0.8034. The framework provides a scientific basis for dynamic e-fence capacity management, advancing spatio-temporal prediction methodologies for sustainable transportation. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Climate change is an urgent issue, and the current mindset of private ownership, particularly of private vehicles, needs to shift. Shared mobility is rapidly emerging as a key part of the solution to contemporary transportation challenges, driven by technological advancements and the growing demand for more sustainable travel options. This paper provides a comprehensive analysis of shared mobility operational models in Europe, focusing on carsharing and its current research on fleet optimization, bikesharing, and scooter sharing. The study draws on three scientific literature databases, with searches centered on keywords relevant to Shared Mobility. This study contributes to the literature by defining each Shared Mobility modality and examining the different operational models. Full article

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. Full article

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking. Full article

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility. Full article

Dockless Bike-Sharing (DBS) is an eco-friendly, convenient, and popular form of ride-sharing. Metro-oriented DBS systems have the potential to promote sustainable transportation. However, the availability of DBS near metro stations often suffers from either scarcity or overabundance. To investigate the factors contributing to this imbalance, this paper examines the nonlinear influences and interactions that impact the DBS system near metro stations, with Shenzhen, China serving as a case study. An ensemble learning approach is employed to predict the imbalance state. Then, the machine learning interpretation method (i.e., SHapley Additive exPlanations) is used to quantify the contribution of effects, discover the strength of interactions between factors and uncover their underlying interactive connections. The results indicate the influence of external factors and the relations between pairwise variables (e.g., road density and the day of the week) for each imbalanced state. Provide two quantized sets of factors that can result in the supply-demand imbalance and support future transport planning decisions to enhance the accessibility and sustainability of Metro-oriented DBS systems. Full article

E-scooters as a new form of mobility are gaining more and more popularity. This popularity results from the flexibility of this mode of transport, but above all from the positive impact on the natural environment through the much higher energy efficiency of an e-scooter compared to a motor vehicle (according to the literature the rate is 2 km per kWh equivalent for a motor vehicle and the range is 90–100 km per kWh in the case of an e-scooter). This paper introduces a discussion on the future development of an energy-efficient electric scooter sharing system based on stakeholder analysis methods. The implementation of the e-scooter sharing system involves linking several areas of human activity, including social activity. This, in turn, relates to the interactions and building of relationships with entities, particularly those influencing the provision of services and their effects. The large number of entities and the complexity of the relations between them make it a challenge both to identify stakeholders in the development of the public e-scooter system and to indicate their roles in shaping the sustainable development strategy for urban mobility. The following study was based on the methodological foundations of stakeholder theory and social network analyses. The main research objective of the article is to identify and assign to different groups the stakeholders influencing the sustainable development of energy-efficient e-scooter sharing systems based on Polish cities. An evaluation was carried out using expert methods with a stakeholder analysis, based on matrix and mapping methods, and with the MACTOR application. Relationships and cooperation suggestions were established for each of the stakeholder groups, which could become an important part of the strategic approach to supporting public transport service providers and organizers, as well as allowing for further reductions in energy consumption in the city by introducing such services on a large scale. The cooperation of the entities participating in the implementation of bike-sharing services can contribute to their greater sustainable development and assurance using the new mobility modes, which consume less energy and at the same time make the city energy-efficient. 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

Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This research focused on a European city, Lisbon, and the e-bike sharing system GIRA, in its early deployment, in 2018, where it had about 409 bikes of which 30% were non-electric conventional bikes and 70% were e-bikes. The research aims at answering the main research questions: (1) What is the number of trips per day and travel time in conventional bikes and e-bikes?; (2) Do the daily usage peaks follow the trends of other modes of transport in terms of rush hours?; (3) Are there seasonality patterns in its use (weekdays and weekends, workdays and holiday periods)?; (4) How do climate conditions affect its use?; and finally, (5) What would be the impact on final energy consumption and GHG emissions? The dataset for 2018 regarding GIRA trips (distance, time, conventional or e-bike, docking station origin and destination) and weather (temperature, wind speed, relative humidity, precipitation) was available from Lisbon City Hall by means of the program “Lisboa aberta”. Data regarding the profile of the users (which trips GIRA replaces?) and data regarding electricity consumption were not available. The latter was estimated by means of literature e-bike data and electric motor specifications combined with powertrain efficiency. Greenhouse gas (GHG) emissions were estimated by using the latest Intergovernmental Panel on Climate Change (IPCC) CO₂ equivalents and a spreadsheet simulator for the Portuguese electricity GHG intensity, which was adaptable to other countries/locations. In a private car fleet dominated by fossil fuels and internal combustion engines, the e-bike sharing system is potentially avoiding 36 Ton GHG/year and reducing the energy consumption by 451 GJ/year. If the modal shift occurs from walking or urban bus to an e-bike sharing system, the impact will be detrimental for the environment. 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

Multiple feeding modes, including walking, bus, private bike, docked bike-sharing, private electric bike (e-bike), car, and taxi, are applied for better accessibility in a metro-based trip. It is crucial to understand their access/egress distances and corresponding catchment areas of metro stations. This paper determines these two distances and accessible areas of stations for different feeding modes based on Nanjing Population Survey data and GIS data by using a network-based approach in Nanjing, China. Considering the distribution of access/egress distance, regression models are established for the exploration of the threshold of distance to delineate catchment areas. What is more, the spatio-temporal characteristics of multiple feeding modes are analyzed. The results indicate that the average feeding distance of walking is the shortest, but docked bike-sharing has the shortest average feeding time, about 8 min. The average feeding time of private e-bikes is close to that of the private bike, but the feeding distance of private e-bikes is about 1.3 times as long as that of private bikes. Moreover, the origin of an over-10 km transfer for accessing metro stations is usually far away from metro lines and the transferring station is mostly the terminal station. Generally, longer access distance means larger catchment area but the result is also influenced by the condition of street network. Moreover, catchment areas for the same feeding modes are different between urban and suburban areas. Full article

City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm. Full article

Vehicle-sharing systems—such as bike-, car-, or motorcycle-sharing systems—have become increasingly popular in big cities in recent years. On the one hand, they provide a cheaper and environmentally friendlier means of transportation than private cars, and on the other hand, they satisfy the individual mobility demands of citizens better than traditional public transport systems. One of their advantages in this regard is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. This availability obviously depends on different strategic and operational management decisions and policies, such as the dimension of the fleet or the (re)distribution of vehicles. Agglutination problems—where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others—are quite common in such systems, and need to be dealt with. Research has been dedicated to this problem, specifying different techniques to reduce imbalanced situations. In this paper, we present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems. Our first contribution is a novel recommendation strategy based on queuing theory that recommends stations based on their utility to the user in terms of lower distance and higher probability of finding a bike or slot. Then, we go one step further, defining a strategy that recommends stations by combining the utility of a particular user with the utility of the global system, measured in terms of the improvement in the distribution of bikes and slots with respect to the expected future demand, with the aim of implicitly avoiding or alleviating balancing problems. We present several experiments to evaluate our proposal with real data from the bike sharing system BiciMAD in Madrid. Full article