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This research addresses the phenomenon of varying bicycle friendliness in urban areas and considers which elements are necessary to design a city in a bike-friendly manner. It aims to provide a deeper understanding of the term bikeability, in relation to the established term walkability, and methods to create models that measure the degree of bikeability in urban areas. We explain different established models and compare their computational bases. The focus of this paper is to define a computational methodology built within a Geographic Information System (GIS) and a subsequent evaluation based on an investigation area in Munich, Germany. We introduce a bikeability index for specific investigation areas and geovisualize four selected factors of this index. The resulting map views show the road segments of the traffic network where the conditions for biking are adequate, but also those segments which need to be improved.

This study conducts a comparative analysis of the Bikeability Index (BI) and CycleRAP in assessing urban cycling infrastructure. The BI, developed in previous research, evaluates cycling conditions through a user-centric framework incorporating safety, comfort, attractiveness, directness, and coherence. In contrast, CycleRAP employs a data-driven methodology focusing on safety by assessing crash risks and severity across different cycling facilities. Using field data and online tools, this research applies both methods to bicycle infrastructure in Hasselt, Belgium, comparing their results and evaluating their alignment in identifying safety concerns and infrastructure needs. A significant correlation between BI and CycleRAP scores was observed, indicating that a higher bikeability score corresponds to reduced safety risks measured by CycleRAP. The study highlights the complementary nature of the two tools, emphasizing the broader insights of the BI and the focused safety evaluations of CycleRAP. The BI safety score extracted from the BI equation showed an even stronger correlation with CycleRAP, suggesting that despite using different methodologies, both indices can yield similar results. These findings provide meaningful guidance for urban planners seeking to enhance cycling infrastructure safety.

Many North American cities are building bicycling infrastructure. Lively discussions on social media, where people passionately support or reject bicycling infrastructure projects, provide a unique data set on attitudes towards bicycling infrastructure. Our goal is to analyse social media posts in Edmonton and Victoria, Canada as new bike infrastructure was implemented to understand the thematic and social elements of the conversation and how these changed over time. We collected Twitter messages (n = 13,121: 7640 in Edmonton; 5481 in Victoria) and compared three timeframes: before lanes opened (January 2015 to lane opening); the first riding season (opening to April 2017); and the second riding season (May 2017 to November 2018). For each timeframe, we evaluated word-combination frequencies (to understand the use of language) and social network structures (to understand which accounts were influential and how they interacted). We observed a change in the three time periods. Before the bicycling infrastructure was built, Twitter activity was focused on advocacy, which was especially strong in Victoria. The first riding season had the most social media activity, the most diverse perspectives and the most controversy. The second riding season held more support. Based on the Twitter activity, we found that Edmonton had more support from local businesses and traditional media, launching a connected network of infrastructure with less social media opposition. Our results suggest that attitudes associated with change in bicycling infrastructure may have a cycle, with initial negative responses to change, followed by an uptick in positive attitudes. 许多北美城市正在建设自行车基础设施。人们在社交媒体上热烈讨论支持或拒绝自行车基础设施项目，这为人们对自行车基础设施的态度提供了一个独特的数据集。我们的目标是分析新的自行车基础设施实施之际，加拿大埃德蒙顿和维多利亚的社交媒体帖子，以了解对话的主题和社交元素，以及这些元素如何随着时间的推移如何变化。我们收集了推特帖子（n = 13,121，其中7640来自埃德蒙顿；5481来自维多利亚），并比较了三个时间范围：车道开放前（2015年1月至车道开放）；第一个骑行季节（车道开放至2017年4月）；以及第二个骑行季节（2017年5月至2018年11月）。针对每个时间段，我们评估了单词组合频率（以了解语言的使用）和社交网络结构（以了解哪些账户有影响力以及它们如何互动）。我们观察到了三个时间段的变化。在自行车基础设施建成之前，推特的活动主要集中在宣传上，这在维多利亚尤为突出。第一个骑行季有的社交媒体活动数量最多、视角最多样化、争议性也最大。第二个骑行季节获得了更多的支持。基于推特的活动，我们发现埃德蒙顿得到了当地企业和传统媒体的更多支持，其在推出一个基础设施互联网络时在社会媒体上遇到的反对较少。我们的结果表明，与自行车基础设施变化相关的态度可能有一个周期，最初对变化的反应是消极的，随后是积极态度的上升。

Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.

Sustainable development and environmental considerations have resulted in many cities around the world recognising the importance of non-motorised modes of transport. Problems related to the proper development and maintenance of cycling infrastructure have already been the subject of various studies. However, they have mainly dealt with the identification of factors influencing the development of cycle paths and the optimisation of the design of safe and comfortable cycle routes. The influence of individual factors on each other and on the development of cycling infrastructure has not been studied. The research aims of this article are to identify which factors influence the development of bicycle infrastructure, their role and interdependence, and their prioritisation. It also looks at whether there are differences between the opinions of bicycle users and experts professionally involved in the development of bicycle paths in assessing the importance of the factors indicated. As a result of the study, eight factors influencing the development of bicycle infrastructure were identified. Based on the opinions of cyclists and experts, the nature of each factor was analysed. Taking into account the complex relationships between the factors, the key factors contributing to the development of bicycle infrastructure were shown: (1) the planning of bicycle paths, taking into account the separation of individual paths and their continuity, consistency, and length; (2) legal regulations promoting cycling in terms of transportation policy; (3) the elimination of obstacles; and (4) the design of bicycle paths, taking into account the safety, space management, terrain, and attractiveness of the surroundings. The results for both groups of respondents were compared. They indicate that both groups of respondents reported the same factors as the most important, with the only differences being in the order of the importance of the factors. The academic value of this work lies in showing the usability of the underrated original version of DEMATEL methodology in the considered area for key factors. The practical significance of this paper is the provision of a rather simple, yet reliable, tool for addressing the complexity of interrelated issues that make the development of urban infrastructure a cumbersome task.

Promoting bicycling and making it attractive requires appropriate infrastructure. Sociodemographic characteristics, frequency and experiences of bike use, and purpose of bicycle trips can affect preferences towards bicycle infrastructure facilities in urban areas. Hence, this study aims to explore the heterogeneity in the perceived importance of bicycle infrastructure facility attributes in various cyclist groups based on gender, age, weekly biking frequency, daily cycling distance, cycling experience, and bicycle trip purpose. Data were collected from bicycle users through a questionnaire disseminated via social media platforms and QR code brochures distributed in Hasselt, Belgium. A 5-point Likert-type ordinal scale was used to collect data on the perceived importance of bicycle infrastructure facility indicators. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the indicators. At the same time, Mann–Whitney U and Kruskal–Wallis tests were utilized to verify the heterogeneity among the groups. The findings reveal that bicycle infrastructure, i.e., bicycle lanes or paths, is the most critical variable, while the slope was considered the least important. No heterogeneity was found regarding the importance of bicycle infrastructure indicators based on gender. However, heterogeneity was observed based on age, daily bicycle use, cycling experience, weekly bicycle use, and bicycle trip purpose. The findings of this research help urban and transport planners develop improvement strategies for the city’s existing bicycling facilities and prioritize future developments by considering various cyclist groups’ preferences.

Measuring the impact of bicycle infrastructure and other mobility improvements has been a challenge in the practice of transportation planning. Transportation planners are increasingly required to conduct complex analyses to provide supporting evidence for proposed plans and communicate well with both decision makers and the public. Cyclists experience two important factors on roads: (a) travel stress related to the built environment along with the traffic conditions and (b) changes in physical burden due to topography. This study develops a method that integrates an energy consumption calculation and “bicycling stress” score to take into account external conditions that influence cyclists substantially. In this method, the level of traffic stress (LTS) is used to select street segments appropriate for different comfort levels among cyclists and is combined with biking energy consumption, in addition to distance, which is used as travel impedance to consider the effects of slopes and street intersections. The integrated Geographic Information System (GIS) analysis methods are used to evaluate bicycle infrastructure improvements in the coming years in Montgomery County, MD, USA. The analysis results demonstrated that the infrastructure improvements in the county’s bike master plan are well-targeted to improve bicycling accessibility. Furthermore, the use of energy as opposed to distance to generate bikeshed areas results in smaller bikesheds compared to distance-generated bikesheds. The method presented herein allows planners to characterize and quantify the impact of bicycle infrastructure and prioritize locations for improvements.

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.

This paper aims to provide insight on whether bicycling for everyday travel can help US adults meet the recommended levels of physical activity and what role public infrastructure may play in encouraging this activity. The study collected data on bicycling behavior from 166 regular cyclists in the Portland, Oregon metropolitan area using global positioning system (GPS) devices. Sixty percent of the cyclists rode for more than 150 minutes per week during the study and nearly all of the bicycling was for utilitarian purposes, not exercise. A disproportionate share of the bicycling occurred on streets with bicycle lanes, separate paths, or bicycle boulevards. The data support the need for well-connected neighborhood streets and a network of bicycle-specific infrastructure to encourage more bicycling among adults. This can be accomplished through comprehensive planning, regulation, and funding.

With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.