Explore the vast range of titles available.

This work studies how the usage of shared mobility services could be influenced by latent factors. An integrated choice and latent variable model is adopted to explore the effects of three attitudinal and perceptual factors on bike- and car-sharing choices while simultaneously investigating the causes associated with each of the latent variables. A group of Chinese commuters’ stated preference mode choice data are collected. It is found that the probability to choose bike-sharing could be positively affected by “willingness to be a green traveler” and “satisfaction with cycling environment,” and car-sharing choice is positively correlated with “advocacy of car-sharing service.” By taking into account the interaction effects between the latent variables and travel time of the two services, a significant difference is discovered on the estimated value of travel time savings (VTTS) compared with other more restrictive model specifications. The finding highlights the importance to derive different VTTS for travelers with differentiated attitudes and perceptions as the tastes toward travel time spent could vary substantially. In other words, there would be different trade-off preferences across attitudinal groups, according to which transport service operators could customize their strategies on prices and levels of service offered.

Bike-sharing is known as a sustainable form of transportation. This travel mode is able to tackle the “last mile” transit issue and deliver financial, well-being, and low-carbon lifestyle advantages to users. To date, many studies have analysed the influence of various factors, including built environments, on bike-sharing ridership. However, no study has exclusively synthesised these findings regarding the association between built-environment attributes and bike-sharing ridership. Thus, in this study, a systematic literature review was conducted on 39 eligible studies. These studies were assessed with respect to (1) bike-sharing usage, (2) studies’ geographical distribution, (3) data collection and analysis method, and (4) built environment factor type. Most studies were carried out in the US and Chinese cities. Variables associated with diversity, density, and distance to public transport stations and public transport infrastructure were frequently employed by the studies reviewed. It was found that BS stations with an average capacity of 24.63 docks and street network systems with an average length of 12.57 km of cycling lanes had a significant impact on the bike-sharing ridership. The findings of these studies were combined, and a series of recommendations were proposed based on them for bike-sharing service providers and researchers in academia. The findings of this evaluation can help practitioners and scholars understand the important built environment elements that influence bike-sharing ridership. Knowledge in this field will enable bike-sharing service providers to direct their resources sufficiently to enhance the more essential aspects of bike-sharing users’ satisfaction.

The paper introduces a discussion regarding the development of a public bike-sharing system, considering random factors, based on selected external environmental analysis methods. The global energy crisis is forcing scientists to continuously improve energy-efficient sustainable methods and scientific solutions. It is particularly important in transportation since transport activities and the constant increase in the number of vehicles have a large share in global energy consumption. The following study investigates the social, technological, economic, environmental, and political aspects of bike-sharing systems in cities. The research purpose of the article is to select the most important macro-environmental factors and their mutual interaction influencing the sustainable development of bike-sharing systems based on the Polish cities case study. The evaluation was carried out through expert methods with STEEP environmental analysis, evaluation of factors with the weighted score, and structural analysis method with MICMAC computer application. The classification of key factors influencing the development of a bike-sharing system has divided them into five groups. It can support public transport service providers and organizers. This can optimize the planning process with decision-making based on future environmental trends.

Background: Conventional bike sharing systems are frequently adding electric bicycles. A major question now arises: Does the bike sharing system have a sufficient number of ebikes available, and are there customers who prefer to use an ebike even though none are available? Methods: Trip data from three different bike sharing systems (Indego in Philadelphia, Santander Cycles in London, and Metro in Los Angeles and Austin) have been used in this study. To determine if an ebike was available at the station when a customer departed, an algorithm was created. Using only those trips that departed while an ebike was available, a random forest classifier and deep neural network classifier were used to predict whether the trip was completed with an ebike or not. These models were used to predict the potential demand for ebikes at times when no ebikes were available. Results: For the system with the highest prediction accuracy, Santander Cycles in London, between 21% and 27% of the trips were predicted to have used an ebike if one had been available. The most important features were temperature, distance, wind speed, and altitude difference. Conclusion: The prediction methods can help bike sharing operators to estimate the current demand for ebikes.

Smart transportation system is a cross-field research topic that involves both the organizations that manage the large-scaled system and individual end-users who enjoy these services. Recent advancement of machine learning-based algorithms has either enabled or improved a wide range of applications due to its strength in making accurate predictions for complex problems with a minimal amount of domain knowledge and great ability of generalization. These nice properties imply potential to be explored for building smart transportation system. This paper studies how deep reinforcement learning (DRL) can be used to optimize the operating policy in modern bike sharing systems. As a case study, the authors demonstrate the potential power of the modern DRL by showing a policy-gradient-based reinforcement learning approach to the rebalancing problem in a bike sharing system, which can simultaneously improve both the user experience and reduce the operational expense.

The paper presents a method of the assessment of spatial integration of bike-sharing stations in urban agglomerations based on GIS tools for analyses. The method uses four sub-models: system of bike-sharing stations, road and street network, demand for bike-sharing ridership, bike-sharing ridership routing, and value matrix of spatial integration measures. The presented method allows the identification of different categories of segments of the road and street network used for bike travels and enables the identification of the set of segments that should be upgraded into bike-friendly infrastructure offering bike lanes or cycle paths in order to ensure the appropriate level of spatial integration of bike-sharing stations. The possibility of the application of the method has been studied on the example of the existing bike-sharing system in Katowice, a city in southern Poland. The research presented in the paper has been conducted based on data on bike rentals and bike trips from eight months of 2018. Selected results of the spatial integration assessment of bike-sharing stations, which may be useful for making investment decisions in the bike-sharing system development, are presented.

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.

The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.

A bicycle-sharing system (BSS) has been implemented in Seoul, South Korea to promote green transportation policy as a Sustainable Development Goal (SDG) to mitigate climate change, reduce traffic jams, and promote physical activity. However, the concentration of air pollutants in Seoul often exceeds the standards of the World Health Organization, thereby creating a conflict with SDG 3 (Health). Therefore, it is important to recognize the trade-offs between actions targeted at SDGs as they might offset each other. In this context, a primary concern is investigating how the behavior of BSS users regarding outdoor air pollution appears. This study explores the relationship between ambient air pollution and the behavior of BSS riders in Seoul. We conducted a time-series analysis of associations between particulate air pollution and participation in the BSS. We used generalized additive models, adjusted for mean temperature, humidity, rainfall, day of the week, long-term trends, and seasonality to construct an exposure–response relationship. We observed a nonlinear relationship between increasing air pollution and bicycle ridership. This study method can be used as a basis for similar analyses to investigate BSS policies in other cities.

Bike sharing system as mode of public transport is very popular in the world. This smart solution can be described as answer to an increasingly frequent traffic congestion and parking problems in many cities all around the world. This issue is beginning to relate to some cities in Slovakia as well. Bicycles address traffic congestion as they form a valid substitution for cars on short trips, contribute to the use of public transport by providing effective last-mile connectivity and simply take up less space on the road. As the system of shared bicycles works from 2016, it is relatively new in Slovakia. This is a reason why this system still has some problems and deficiencies that need to be optimized. Presented paper focused on the city of Nitra, which is currently struggling with the issue of traffic congestion. The main aim of paper is to point out the opportunities and constraints arising from the concept of shared bicycles in the conditions of city of Nitra. Our proposals and recommendations are based on the opinions of the citizens of Nitra obtained from conducted marketing survey(625 respondents – citizens of Nitra). The results of the survey have brought important insights into improving the strategy of shared bicycles, focusing on attractiveness for citizens, and ultimately, urban transport solutions.