Search Results (7)

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 study presents an optimisation framework for operating a battery swapping station (BSS) to enhance efficiency and sustainability in electric vehicle (EV) infrastructure. A hybrid modelling approach combines agent-based discrete event simulation and linear programming to model the dynamic behaviour of batteries and operational processes within the BSS. The model considers factors such as charging speed, battery degradation, grid power constraints, customer behaviour, and range anxiety. The agent-based model simulates the interaction between vehicles, batteries, and the station, capturing the stochastic nature of EVs’ arrivals and battery demand with the discrete event simulation. The linear programming component optimises battery state transitions to minimise degradation and ensure that the demand is met while respecting the power limits of the grid. Different battery types are considered based on vehicle category, each with specific capacity and usage patterns, reflecting real-world market conditions. The results demonstrate that the proposed optimisation framework can effectively manage the complex operational needs of a BSS. The proposed framework effectively balances service quality with resource efficiency by employing a strategic mix of charging modes and inventory management, reducing operational and degradation costs. This approach supports a more sustainable EV infrastructure, highlighting BSS as a viable solution to enhance the efficiency and sustainability of EV operations. Furthermore, the analysis highlights the critical role of power limits in determining charging strategies and their impact on operational efficiency. The findings suggest that with optimised operations, BSS can play a critical role in accelerating the adoption of EVs by offering a faster, more reliable, and sustainable alternative. 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

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

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

Bicycle sharing systems (BSSs) have been implemented in cities worldwide in an attempt to promote cycling. Despite exhibiting characteristics considered to be barriers to cycling, such as hot summers, hilliness and car-oriented infrastructure, Southern European island cities and tourist destinations Limassol (Cyprus), Las Palmas de Gran Canaria (Canary Islands, Spain) and the Valletta conurbation (Malta) are all experiencing the implementation of BSSs and policies to promote cycling. In this study, a year of trip data and secondary datasets are used to analyze dock-based BSS usage in the three case-study cities. How land use, socio-economic, network and temporal factors influence BSS use at station locations, both as an origin and as a destination, was examined using bivariate correlation analysis and through the development of linear mixed models for each case study. Bivariate correlations showed significant positive associations with the number of cafes and restaurants, vicinity to the beach or promenade and the percentage of foreign population at the BSS station locations in all cities. A positive relation with cycling infrastructure was evident in Limassol and Las Palmas de Gran Canaria, but not in Malta, as no cycling infrastructure is present in the island’s conurbation, where the BSS is primarily operational. Elevation had a negative association with BSS use in all three cities. In Limassol and Malta, where seasonality in weather patterns is strongest, a negative effect of rainfall and a positive effect of higher temperature were observed. Although there was a positive association between BSS use and the number of visiting tourists in Limassol and Malta, this is predominantly explained through the multi-collinearity with weather factors rather than by intensive use of the BSS by tourists. The linear mixed models showed more fine-grained results and explained differences in BSS use at stations, including differences for station use as an origin and as a destination. The insights from the correlation analysis and linear mixed models can be used to inform policies promoting cycling and BSS use and support sustainable mobility policies in the case-study cities and cities with similar characteristics. Full article

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques. Full article