Search Results (98)

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. 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

Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. Full article

Bicycle riding requires a high standard width and continuity of lanes, as an appropriate width directly improves the service level of the lanes. Therefore, the width of bicycle lanes should be designed considering the characteristics of the bicycle traffic flow and the actual conditions of an area. In order to explore the relationship between bicycle traffic flow characteristics and lane width, this study references the vehicle traffic flow model and introduces the concept of bicycle traffic flow width, defined as the average width of bicycle traffic flow over a certain distance in a unit of time. Based on measured data, this study analyzes the relationships among bicycle traffic flow, lane width, and other parameters. The research results show that when the bicycle lane width is between 2.0 and 3.4 m, there is a clear linear relationship between the speed of bicycle traffic flow and the traffic flow width, with bicycle traffic flow width increasing as speed increases. Furthermore, overly wide bicycle lanes can result in more instances of bicycle over-speeding. These findings will guide the design of bicycle lanes. 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

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article

Modern and smart cities prioritize providing sufficient facilities for inclusive and bicycle-friendly streets. Several methods have been developed to assess city bicycle environments at street, neighborhood, and city levels. However, the importance of micro-level indicators and bicyclists’ perceptions cannot be neglected when developing a bikeability index (BI). Therefore, this paper proposes a new BI method for evaluating and providing suggestions for improving city streets, focusing on bicycle infrastructure facilities. The proposed BI is an analytical system aggregating multiple bikeability indicators into a structured index using weighed coefficients and scores. In addition, the study introduces bicycle infrastructure indicators using five bicycle design principles acknowledged in the literature, experts, and city authorities worldwide. A questionnaire was used to collect data from cyclists to find the weights and scores of the indicators. The survey of 383 participants showed a balanced gender distribution and a predominantly younger population, with most respondents holding bachelor’s or master’s degrees and 57.4% being students. Most participants travel 2–5 km per day and cycle 3 to 5 days per week. Among the criteria, respondents graded safety as the most important, followed by comfort on bicycle paths. Confirmatory factor analysis (CFA) is used to estimate weights of the bikeability indicators, with the values of the resultant factor loadings used as their weights. The highest-weight indicator was the presence of bicycle infrastructure (0.753), while the lowest-weight indicator was slope (0.302). The proposed BI was applied to various bike lanes and streets in Hasselt, Belgium. The developed BI is a useful tool for urban planners to identify existing problems in bicycle streets and provide potential improvements. Full article

This research explores the implementation of green infrastructure in supporting sustainable transportation systems to improve urban mobility in big cities. The background of this research is based on the need to reduce the environmental impact of the transportation sector, which contributes significantly to carbon emissions and air pollution, especially in dense urban areas. Green infrastructure, such as bicycle lanes, pedestrian-friendly sidewalks, and green open spaces, has been proven to have a positive impact in reducing pollution and improving people’s quality of life. This research aims to analyze how the implementation of green infrastructure can support sustainable transportation and improve the quality of mobility in urban areas, with a focus on case studies of cities such as Copenhagen, Amsterdam, and Singapore, and large cities in Indonesia, including Bandung. Using the literature review method, this research analyzes various reports, journal articles, and statistical data from previous studies regarding the impact of green infrastructure in reducing emissions and promoting environmentally friendly transportation. The results of the discussion show that the implementation of green infrastructure provides various benefits, such as reducing carbon emissions, improving public health, and creating a more comfortable urban environment. Large cities in Europe and Asia have been pioneers in implementing this system, while in Indonesia, cities such as Jakarta, Surabaya, and Bandung have begun to adopt similar concepts with some success, although they still face various challenges. In conclusion, green infrastructure is an important element in sustainable city development that not only improves mobility but also the overall quality of life of society. Full article

The rapid spread of micromobility vehicles such as bicycles and electric scooters poses new challenges to urban transportation systems, particularly in terms of road safety and infrastructure integration. This study investigates the driving behavior of micromobility users at a mini-roundabout, focusing on their speed profiles and their position within the lane during the entry, circulation, and exit phases. A structured recruitment process was used to select 20 participants with previous micromobility experience. Participants performed crossing maneuvers at a mini-roundabout in Gravina di Catania, Italy, which were monitored using drone footage and analyzed with tracking software to extract trajectories and speed data. The results show significant differences between e-scooter and bicycle users, with bicycles showing less speed variability, especially during the crossing and exit phases, while e-scooters showed greater variability, especially during the entry and exit phases. The results highlight the influence of vehicle stability and user posture on riding behavior and emphasize the need for infrastructure adaptations to increase safety. Mini-roundabouts designed for moderate speed are identified as a promising solution to improve the coexistence of micromobility and motor vehicles. This research identifies key differences in speed profiles and behavioral patterns between e-scooter and bicycle users, offering actionable insights and recommendations for safer and more efficient urban infrastructure. These contributions provide valuable guidance for urban planners and policymakers in promoting safer and more sustainable urban mobility. Full article

The emergence of micro-mobility transportation in urban areas has led to a transformative shift in mobility options, yet it has also brought about heightened traffic conflicts and crashes. This research addresses these challenges by pioneering the integration of image-processing techniques with machine learning methodologies to analyze crash diagrams. The study aims to extract latent features from crash data, specifically focusing on understanding the factors influencing injury severity among vehicle and micro-mobility crashes in Michigan’s urban areas. Micro-mobility devices analyzed in this study are bicycles, e-wheelchairs, skateboards, and e-scooters. The AlexNet Convolutional Neural Network (CNN) was utilized to identify various attributes from crash diagrams, enabling the recognition and classification of micro-mobility device collision locations into three categories: roadside, shoulder, and bicycle lane. This study utilized the 2023 Michigan UD-10 crash reports comprising 1174 diverse micro-mobility crash diagrams. Subsequently, the Random Forest classification algorithm was utilized to pinpoint the primary factors and their interactions that affect the severity of micro-mobility injuries. The results suggest that roads with speed limits exceeding 40 mph are the most significant factor in determining the severity of micro-mobility injuries. In addition, micro-mobility rider violations and motorists left-turning maneuvers are associated with more severe crash outcomes. In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. These factors demonstrate elevated rates of prevalence among younger micro-mobility users and are found to be associated with distracted motorists, elderly motorists, or those who ride during nighttime. Full article

This study examines the influence of transportation policies and urbanisation on cycling participation and environmental sustainability in Slovenia. Factor and regression analyses were employed. The yearly data from 2008 to 2021 were isolated. A modest increase in urban cycling frequency was observed, bolstered by investments in environmental protection and safety enhancements; however, additional evidence is needed to confirm the long-term effects (H1). Furthermore, while increased cycling was linked to a reduction in CO₂ emissions and improved air quality, the overall environmental benefits were found to be affected by other factors, such as motorisation and public transportation in summer (H2). The study revealed that the introduction of reduced urban speed limits and expanded cycling lanes significantly enhanced cycling safety and desirability, leading to a shift from car usage to bicycles (RQ). These findings indicate that cycling could play a vital role in advancing Slovenia’s sustainable development goals, emphasising the need for continued investments and supportive urbanisation policies. Full article

(1) Background: As cycling gains popularity as a mode of transportation, the frequency of accidents involving cyclists also rises. This has become a major concern for traffic safety, sustainability, and city planning. Identifying the risk factors that contribute to bicycle road accidents remains a significant challenge. This study aims to figure out which risk factors make some road segments more dangerous for cyclists than others. (2) Methods: This study introduces the use of a bicycle simulator to test different road segments involving thirty-nine participants. The impact of demographics and some risk factors related to infrastructure were analyzed in terms of their influence on the perceived level of risk through pre- and post-surveys. (3) Results: The findings showed that the bicycle facility type affects the perceived level of risk. Shared-use roads were ranked as riskiest, while separated bike lanes were least risky. Bicycle roads with no separated safety barriers had higher risks. Heavy traffic jams increased danger among cyclists. Women gave higher risk ratings than men. The perceived levels of risk were then compared with the previously developed risk index and they correlated well. (4) Conclusions: This confirms that the risk index can reliably evaluate the degree of risk of each road segment. Full article

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making. Full article

Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings. Full article

Over recent decades, intense urbanization, city expansion, and unregulated construction have led to a scarcity of green spaces and environmental degradation. Green spaces significantly enhance residents’ quality of life by supporting mental and physical health, improving environmental conditions, and benefiting the local microclimate. However, adding green spaces alone is insufficient for modern cities. Increasing population mobility and demand for sustainable transportation modes highlight the role of bicycles and safe bike lane networks in urban development. This study focuses on the perspectives of Trikala’s citizens regarding the contributions of green spaces to their quality of life and cycling habits, and to the effectiveness of current cycling infrastructure. Using a structured questionnaire, data were analyzed with SPSS through descriptive and multivariate analysis. The results demonstrate a strong public acknowledgment of green spaces and bicycles as essential components for sustainable urban planning. Nevertheless, challenges with bike lane safety and network continuity were evident. This study concludes that enhancing both green spaces and cycling infrastructure is crucial for fostering a more environmentally friendly and healthy urban environment. Policy recommendations include improving bike lane safety and expanding green space access, creating a foundation for sustainable, resilient urban living. Full article