Search Results (96)

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. Full article

As cities globally prioritize sustainable transportation, understanding gender-differentiated responses to the urban built environment is critical for equitable mobility planning. This study combined the Social Ecological Model (SEM) with the theoretical perspective of Gendered Spatial Experience to explore the differentiated impacts of the Perceived Street Built Environment (PSBE) on the cycling behavior of men and women. Questionnaire data from 285 e-bike and traditional bicycle riders (236 e-bike riders and 49 traditional cyclists, 138 males and 147 females) from Gulou District, Nanjing, between May and October 2023, were used to investigate gender differences in cycling behavior and PSBE using the Mann–Whitney U-test and crossover analysis. Linear regression and logistic regression analyses examined the PSBE impact on gender differences in cycling probability and route choice. The cycling frequency of women was significantly higher than that of men, and their cycling behavior was obviously driven by family responsibilities. Greater gender differences were observed in the PSBE among e-bike riders. Women rated facility accessibility, road accessibility, sense of safety, and spatial comfort significantly lower than men. Clear traffic signals and zebra crossings positively influenced women’s cycling probability. Women were more sensitive to the width of bicycle lanes and street noise, while men’s detours were mainly driven by the convenience of bus connections. We recommend constructing a gender-inclusive cycling environment through intersection optimization, family-friendly routes, lane widening, and noise reduction. This study advances urban science by identifying gendered barriers in cycling infrastructure, providing actionable strategies for equitable transport planning and urban design. Full article

The increasing popularity of e-biking and e-mountain biking offers new opportunities for sustainable tourism and environmental education, particularly in mountain regions. This study focuses on the Italy–Switzerland “E-Bike” project, which integrates e-bike-friendly routes with scientific and cultural education across the Alps. By analyzing key points of interest along the routes, particularly glaciers and earth pyramids in Lombardy, we explore strategies for sustainable management, conservation, and public engagement. Glaciers (Forni and Ventina), facing rapid retreat due to climate change, represent sensitive environments requiring monitoring and visitor regulation. Similarly, earth pyramids in Postalesio exemplify fragile landforms shaped by erosion, requiring visitor management. This study highlights the need for strategic promotion, clear scientific communication, and sustainable tourism practices to balance conservation with accessibility. E-biking facilitates low-impact exploration of geosites, enhancing public awareness of environmental challenges while minimizing ecological footprints. Innovative digital tools (QR-coded virtual guides) enhance visitor education and engagement. By integrating e-bike tourism with geoheritage conservation, this study proposes guidelines for managing soft mobility in mountain areas, combining conservation needs with accessibility, and fostering public engagement. These findings contribute to broader discussions on sustainable tourism development, offering a replicable model for other regions seeking to harmonize recreation with environmental stewardship. Full article

This paper aims to propose innovative solutions to improve the management of Barcelona’s bike-sharing system, known as Bicing. This study addresses one of the system’s main challenges: the unequal distribution of bicycles across the city and at different times of the day, which affects the users. The analysis combines advanced statistical techniques, predictive models and optimization algorithms to identify vulnerable areas in terms of accessibility and design strategies to balance bicycle distribution. Using methods such as clustering and predictive models based on machine learning, the system’s usage patterns are anticipated. These predictions feed optimization algorithms that enable the planning of more efficient routes for bicycle repositioning, reducing unnecessary vehicle movement and supporting a more environmentally friendly mobility network. The results highlight the importance of proactive system management, improving both user satisfaction and operational efficiency while fostering a more sustainable urban transport ecosystem. Full article

Cycling significantly contributes to improving metro accessibility; however, the quality of bicycle environments surrounding metro stations remains insufficiently studied. This study develops a criteria–indicators assessment framework that incorporates both objective characteristics of bicycle infrastructure and subjective perceptions of bicycle access to metro stations. The framework consists of four primary criteria—accessibility, convenience, safety, and comfort—along with eighteen sub-level indicators. Taking central Tianjin as the study area, the study evaluated the cycling environment quality around eight representative metro stations by employing information entropy and the analytic hierarchy process, with cosine similarity used to compare the outcomes against human–machine adversarial scoring result to ensure analytical robustness. The findings reveal substantial disparities in cycling infrastructure, with safety and accessibility exhibiting higher scores than convenience and comfort. Additionally, cycling environment quality is higher around comprehensive and public-service stations compared to residential stations, while commercial stations exhibit the lowest quality. The study underscores the necessity of expanding protected bike lanes, enhancing route directness, and improving parking and wayfinding facilities to promote cycling as an effective first- and last-mile metro access mode. Full article

As cities increasingly prioritize green and low-carbon transportation, the development of effective cycling infrastructure has become essential for alleviating traffic congestion and reducing environmental impacts. However, the service quality of bike lanes remains inadequate. To address this gap, this study proposes a multi-data-fusion framework for evaluating bike lane “cycling friendliness”, integrating subjective perceptions with objective metrics. The framework combines survey-based subjective data with digital measurements to enable rapid, large-scale assessments that align with user expectations. Tailored evaluation models are developed based on revealed preference (RP) survey analysis to account for variations among target user groups. Key factors such as road roughness, motor vehicle encroachment, cycling-friendly amenities, and roadside scenery are quantitatively assessed using vibration analysis and computer vision techniques. Validation results reveal a strong correlation between model predictions and subjective evaluations, demonstrating the framework’s reliability and effectiveness. This approach offers a scalable, data-driven tool for optimizing bike route selection and guiding infrastructure upgrades, thus advancing urban cycling transportation. Full article

The city of Valparaíso, Chile, faces significant mobility challenges due to its steep slopes, complex urban infrastructure, and socioeconomic conditions. In this direction, this study explores the potential promotion of E-bike uses by identifying the optimal routes that connect metro stations to strategic hilltop streets in the city. A hybrid methodology combining a multicriteria GIS-based analysis and an experimental study was used to evaluate potential routes and the possibility of increasing the power limitations for non-motorized mobility in Chile. Fifteen routes were assessed based on criteria including the slope, traffic safety, directionality, intersections, and travel distance. The results indicate that routes such as Cumming from Puerto and Bellavista stand out as the most viable for e-bike use given their favorable characteristics. The experimental study revealed that higher-powered E-bikes (500 W and 750 W) would be more able to overcome the steep slopes of Valparaíso, with an average speed of 5.36 km/h and 9.52 km/h on routes with a 10.88% average slope. These findings challenge the current regulatory limit of 250 W for non-motorized vehicles in Chile, highlighting the potential benefits of increasing their power limits to enhance sustainable mobility in the hilly urban contexts of this country. This study highlights the need to adapt urban mobility policies to the unique topographical conditions of each city. Future research should build upon more experimental studies, develop specific street-scale analyses using audit methods, incorporate climate-related variables, and evaluate the economic viability of e-bike infrastructure. Addressing these aspects could position Valparaíso as a leading example of sustainable urban mobility for cities facing comparable challenges. Full article

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change is now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has the potential to simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes for motorised transport. The ELEVATE study aims to understand the impacts of e-micromobility, including identifying the people, places, and circumstances where they will be most beneficial in terms of improving people’s health while also reducing mobility-related energy demand and carbon emissions. A complex mixed methods design collected detailed quantitative and qualitative data from multiple UK cities. First, nationally representative (n = 2000), city-wide (n = 400 for each of the three cities; total = 1200), and targeted study area surveys (n = 996) collected data on travel behaviour, levels of physical activity, vehicle ownership, and use, as well as attitudes towards e-micromobility. Then, to provide insights on an understudied type of e-micromobility, 49 households were recruited to take part in e-cargo bike one-month trials. Self-reported data from the participants were validated with objective data-using methods such as GPS trackers and smartwatches’ recordings of routes and activities. CO₂ impacts of e-micromobility use were also calculated. Participant interviews provided detailed information on preferences, expectations, experiences, barriers, and enablers of e-micromobility. Full article

The utilization of dockless shared bikes for shopping purposes has become increasingly prevalent. This research seeks to optimize the configuration of facilities and transportation policies for shared bike travel by analyzing the spatiotemporal patterns of shopping trips from the perspectives of destination (D), origin (O), and O-D correlation in Nanjing’s main city area. As the second-largest commercial center in East China, Nanjing offers a significant context for this research. First, we introduce the “cycling intensity” indicator to analyze the patterns of shared bicycle trips with shopping facilities as destinations at both the subdistrict and road section scales. Second, we utilize spatial autocorrelation analysis and k-means clustering to explore the outflow patterns of shared bicycle trips originating from shopping facilities. Finally, we employ grey correlation analysis to investigate the dynamic flow correlations of shared bicycle O-D trips around various grades of shopping facilities at both subdistrict and road section levels. Concurrently, we endeavored to delineate the practical transformation and application of the research findings. Our results indicate the following: (1) There is a high concentration of cycling intensity around shopping facilities on east–west and north–south roads, with community shopping facilities primarily associated with north–south roads. (2) The outflow of shared bikes from shopping areas can be categorized into four distinct modes. (3) The inflow and outflow of shopping trips exhibit significant synchronicity, particularly on the branch routes. These findings can provide valuable insights for zoning planning, construction of bicycle infrastructure, and formulation of sustainable urban transportation policies. Full article

In this paper, we study a novel static bike-sharing repositioning problem. There is a set of stations spread over a given area, each containing a number of operative bikes, damaged bikes, and free slots. The customers may pick up an operative bike from a station, use it, and return it to another station. Each station should have a target number of operative bikes to make it likely to meet customer demands. Furthermore, the damaged bikes should be removed from the stations. Given a fleet of available vehicles, the repositioning problem consists of designing the vehicles’ routes and calculating the number of operative (usable) and damaged (unusable) bikes that will be moved (loading instructions/loading policy) between stations and/or the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic based on a variable neighborhood search combined with several improving algorithms, including an integer linear programming model to optimize loading instructions. The algorithm was tested in instances based on real-world data and could find good solutions in reasonable computing times. Full article

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions. Full article

Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce the Spatio-Temporal Embedding Fusion Transformer (STEFT). This deep learning model leverages attention mechanisms and feature fusion to effectively model dynamic dependencies in traffic data. STEFT includes an Embedding Fusion Network that integrates spatial, temporal, and flow embeddings, preserving original flow information. The Flow Block uses an enhanced Transformer encoder to capture periodic dependencies within neighboring regions, while the Prediction Block forecasts inflow and outflow dynamics using a fully connected network. Experiments on NYC (New York City) Taxi and NYC Bike datasets show STEFT’s superior performance over baseline methods in RMSE and MAPE metrics, highlighting the effectiveness of the concatenation-based feature fusion approach. Ablation studies confirm the contribution of each component, underscoring STEFT’s potential for real-world traffic prediction and other spatial–temporal challenges. Full article

This paper presents a Decision Support System (DSS) designed to enhance cyclability and perceived bikeability in urban areas, with an application to the city of Milan, Italy, focusing on cycling toward the urban university campuses of Politecnico di Milano. Despite the increasing emphasis on sustainable urban mobility, research gaps remain in optimizing cycling infrastructure development based on both observable factors (e.g., availability and quality of cycleways) and latent factors (e.g., cyclists’ perceived safety and security). The objective of this study is to address these gaps by developing a DSS, based on a macroscopic multimodal transport simulation model, to facilitate an in-depth analysis and prioritization of cycling transport policies. Findings from the DSS simulations indicate that strategic enhancements to cycling infrastructure can shift user preferences toward safer and more dedicated cycling routes, despite potential increases in travel time and distance. This paper concludes that implementing a DSS not only supports more informed policymaking but also encourages sustainable urban development by improving the overall cycling experience in cities, highlighting the importance of addressing both tangible and intangible factors in the design and prioritization of cycling infrastructure projects. 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 the quest to enhance urban green mobility and promote sustainable leisure activities, this study presents a comprehensive analysis of the potential for cycling greenways within the urban fabric of Chengdu, China. Leveraging the built environment and cycling routes, simulated by dockless bike-sharing (DBS) big data on weekend afternoons, the cycling flow on existing networks reflects the preference for leisure cycling in surroundings, thus indicating the potential for future enhancements to cycling greenway infrastructure. Employing Multi-Scale Geographically Weighted Regression (MGWR), this research captures the spatial heterogeneity in environmental factors influencing leisure cycling behaviors. The findings highlight the significant roles of mixed land use, network diversity, public transit accessibility, human-scale urban design, road network thresholds, and the spatially variable impacts of architectural form in determining cycling greenway potential. This study culminates with the development of an evaluation model, offering a scientific approach for cities to identify and prioritize the expansion of cycling infrastructure. Contributing to urban planning efforts for more livable and sustainable environments, this research underscores the importance of data-driven decision-making in urban green mobility enhancement by accurately identifying and efficiently upgrading infrastructure guided by public preferences. Full article