Urban Bicycle Research Articles

Spatio-temporal analysis of bicyclists' PM2.5 exposure levels in a medium sized urban agglomeration.

Many cities have promoted decarbonized transportation modes to mitigate climate change, reduce air pollution and promote healthy behaviors. However, cyclists may be particularly exposed to higher concentrations of on-road air pollutants than other commuters due to their proximity to traffic, higher breathing rates, and prolonged commutes. In addition, there is scarce information analyzing the geographic exposure levels of cyclists in medium-sized urban agglomerations of Latin American cities. We aimed to assess cyclists' exposure to PM2.5 at the spatio-temporal level using low-cost sensors in the Mendoza Metropolitan Area, Argentina. We calculated PM2.5 inhalation doses (IDs) for different routes with distinctive characteristics, considering different age ranges and gender of cyclists. The dose was represented as temporally and spatially disaggregated exposure maps, one of the first to represent it in Latin America using this method. All link types analyzed exhibited significant differences in PM2.5 concentrations, although the most frequent concentrations were less than 5µgm-3, with secondary peaks of 6.5 and 9µgm-3. As expected, ID increases with age and is greater in males than in females. Our findings further reinforce the fact that route choice (busy vs. quiet) and time of day (peak vs. off-peak) appreciably affect the pollutant exposure of cyclists. All these results could be helpful in the selection of alternative cycling routes with lower PM2.5 at different hours of day but also enable us to investigate further implications of exposure to PM2.5 for the health of urban bicycle commuters.

Characterization of a Covid-fired urban bike delivery system: The Montreal experience

The Covid-19 pandemic led to a rapid change in consumers' demand for home deliveries and traditional delivery modes were unable to deliver consumers and shops due to the sudden increase in demand. This study investigates the results of a local cargo bike delivery initiative in Montreal, Canada, during the forced closure of non-essential businesses from March to May 2020. The objective of this study is to identify the variables which interacted with the performance of a cargo bike delivery initiative while considering the specific context of the Covid-19 pandemic. To assess such changing conditions, a multilevel linear regression analysis is carried out. This analysis allows to quantify the number of home deliveries while considering the impact of the number of active Covid-19 cases and sociodemographic predictors on the cargo bike initiative. The dataset covers 6,700 deliveries made by cargo bikes in the city of Montreal over 16 weeks. The results show that the presence of advertising and the characteristics of consumers' households, such as a smaller household size and a younger average age are amongst the strongest factors for the success of such initiative. The specific context of the Covid-19 pandemic allowed a better penetration of cargo bikes within the last-mile ecosystem.

Reshaping cyclist mobility: Understanding the impact of autonomous vehicles on urban bicycle users

Autonomous Vehicles (AVs) present a jarring new normal for negotiation on public streets. Current communication and interaction stand to be disrupted with the presence of AVs. Active transport users, specifically bicycle users, rely on human communication and subtle cues to feel confident when negotiating with other road users. Shifts in communicability with AVs presents a unique challenge for bicycle users. It remains unclear how AVs will impact urban bicycle users. This paper employs a mixed-methods approach to understanding impacts that AVs have on urban bicycle users in four test sites with varying levels of traffic stress. Interviews and focus groups were used across different traffic stress scenarios in three countries to understand how bicycle users will be impacted by AVs in real-world scenarios. The results represent a pioneering cross-section of four bicycle cultures’ exposure to AVs. Results universally show that, compared to conventionally operated vehicles, an enduring sense of unease remains unresolved with AVs and that shifting focus to bicycle user experiences and prioritizing the needs of a diverse population of non-occupants will be critical. If AVs are not deployed responsibly and responsively, they could turn bicycle users away from cycling in a streetscape where active transport users are already marginalized.

SUITABILITY ANALYSIS FOR URBAN BICYCLE TOURISM OF ILOILO CITY USING GIS

Abstract. Urban bicycle tourism refers to the recreational and enjoyable use of bicycles in navigating urban cities like Iloilo City. The city has already established an 11-kilometer cycling infrastructure within its boundaries. It is imperative to maximize its use to combat the negative impacts of rapid urbanization and promote safe and sustainable transportation. This paper shows the use of Geographic Information Systems (GIS) to assess the bike lanes network's suitability and accessibility for building a bike-sharing system that also caters to drop-off and ride-on points at each station. Deletion of duplicates and cleaning of road network data and tourist attraction point data was performed prior to any analysis. Service areas were generated to determine the number of tourist attractions catered by each potential bike share station – defined by main road intersections. Point distance analysis was performed to reduce the number of road intersections to just thirty-nine (39). Bicycle route maps and itineraries were produced to visualize the optimal routes tourists can use for exploring the city based on preferred sites category. From here, Route Analysis was conducted to create four (4) itineraries based on tourism categories: Top 10 Most Rated Tourist Attractions, Food and Dining, Museums and Churches, and Shopping and Leisure. The output maps and itineraries can be presented to the City Tourism Office of Iloilo City as a decision support tool for its programs and policies regarding bike lanes.

Methodology for planning short-term urban bike lane networks; an example of downtown Gijón

Recently there has been increasing interest in the construction of bike lanes in cities due to their enormous positive effects; however, in some places society is resistant to these infrastructures and it is difficult to break a dynamic defined as the vicious circle of cycling mobility. In view of this, there is the option of proposing temporary bike lanes to evaluate the social response and, if successful, to make them permanent. This option is called temporary or tactical urbanism and has been widely used in the field of bicycle infrastructure during the coronavirus disease 2019 pandemic. The problem is that there is no quick and low-cost method to determine a comprehensive and well-connected scheme of bike lanes in the whole of a city; therefore, sometimes design mistakes are made and as a consequence temporary infrastructures, far from becoming permanent, disappear. This research study proposes an agile and economical methodology to establish which streets, within those that define the urban grid as a whole, can accommodate a temporary bike lane without altering the motorised traffic grid. The method is based on the use of quantum geographic information system and on a quick and easy data collection. The method was applied to downtown Gijón, Spain.

Transformer based 3D semantic segmentation of urban bicycle infrastructure

ABSTRACT This work investigates Point Cloud Semantic Segmentation (PCSS) to detect bicycle paths from 3D point cloud data of bicycle-mounted LiDARs. For the task of PCSS, an existing Convolutional Neural Network architecture (CNN) is first pre-trained on the Semantic KITTI data set, an important data set for PCSS in LiDAR scans of driving situations. Furthermore, a new, semantically-labelled 3D LiDAR data set, the Salzburg Bicycle LiDAR Data Set (SBLD), is presented, which consists of 16,008 point clouds recorded by a ROS2-enabled sensor bicycle with five 3D LiDARs covering every direction. After fine-tuning the CNN on the SBLD train set, the segmentation performance is evaluated on the SBLD test set. The CNN shows promising results in recognising bike paths, vegetation, terrain and buildings in the SBLD. To further push the segmentation performance, the existing CNN is enhanced with self-attention blocks. The evaluation of this enhanced architecture shows a performance gain of 2.79% points compared to the original architecture. The proposed segmentation approach shows practical potential for detecting bike paths. The SBLD is provided to the scientific community to further drive research in the field of sensor bicycles.

Dengue Fever and Novel Detection Methods Based on Biosensors

Dengue fever, a mosquito-borne viral infection, poses a significant global health threat, and early diagnosis is crucial for effective disease management. Infection of humans usually occurs through infected Aedes mosquitoes such as Aedes aegypti or Aedes albopictus with the dengue fever virus. The spectrum of disease ranges from asymptomatic infection and mild febrile illness (dengue fever) to more serious symptoms such as dengue hemorrhagic fever and dengue shock syndrome. Dengue fever is characterized by severe headache, high fever, skin rash, muscle and joint pain, nausea, and vomiting. Dengue hemorrhagic fever is characterized by high fever, an enlarged liver, hemorrhagic phenomena, shock, and often cardiovascular disorders. Dengue fever is transmitted through urban bicycle (human transmission cycle) and Sylvatic (animal transmission cycle). The demand for early detection of this virus has increased to control the spread of infectious diseases and protect humankind from its harmful effects. For laboratory diagnosis of this virus, various methods can be employed to detect the virus itself, viral nucleic acids, antibodies, or antigens, or a combination of these approaches can be used. Recently, biosensors have been found to be a potential tool to detect and quantify the virus with fast detection, relatively cost-effective, high sensitivity, and selectivity compared to conventional diagnostic methods such as immunological and molecular techniques. Mostly, biosensors employ electrochemical detection techniques with transducers, owing to their easy construction, low cost, ease of use, and portability.

Yes, size does matter (for cycling safety)! Comparing behavioral and safety outcomes in S, M, L, and XL cities from 18 countries

Although most actions aimed at promoting the use of active transport means have been conducted in ‘large’ cities, recent studies suggest that their cycling dynamics could hinder the efforts put into infrastructural, modal share, and cycling culture improvements. AimThe present study aimed to assess the role of city sizes on riding behavioral and crash-related cycling outcomes in an extensive sample of urban bicycle users. MethodsFor this purpose, a full sample of 5705 cyclists from >300 cities in 18 countries responded to the Cycling Behavior Questionnaire (CBQ), one of the most widely used behavioral questionnaires to assess risky and positive riding behaviors. Following objective criteria, data were grouped according to small cities (S; population of 50,000 or fewer), medium cities (M; population between 50,000 and 200,000), large cities (L; population between 200,000 and one million), and megacities (XL; population larger than one million). ResultsDescriptive analyses endorsed the associations between city size, cycling behavioral patterns, and mid-term self-reported crash outcomes. Also, it was observed a significant effect of the city size on cyclists' traffic violations and errors (all p < .001). However, no significant effects of the city size on positive behaviors were found. Also, it stands out that cyclists from megacities self-reported significantly more violations and errors than any of the other groups. Further, the outcomes of this study suggest that city sizes account for cycling safety outcomes through statistical associations, differences, and confirmatory predictive relationships through the mediation of risky cycling behavioral patterns. ConclusionThe results of the present study highlight the need for authorities to promote road safety education and awareness plans aimed at cyclists in larger cities. Furthermore, path analysis suggests that “size does matter”, and it statistically accounts for cycling crashes, but only through the mediation of riders' risky behaviors.

Bicycle Sharing Travel Characteristics Analysis

The emergence of bicycle sharing has greatly impacted the urban transportation structure and residents' travel modes. The massive spatial-temporal data carried by it is of great significance for studying the characteristics of residents' slow travel and guiding the development of green transportation in cities. Through the analysis of Xiamen's bicycle sharing order data, this paper analyzes the riding characteristics of users and explores the urban functional elements that affect the use of bicycle sharing. This paper will use urban bicycle sharing data, Point of Interest (POI) data in internet maps, and population distribution data to carry out mining and analysis on the travel characteristics and spatial distribution features of bicycle sharing.

Bikeability and the induced demand for cycling

To what extent is the volume of urban bicycle traffic affected by the provision of bicycle infrastructure? In this study, we exploit a large dataset of GPS trajectories of bicycle trips in combination with a fine-grained representation of the Copenhagen bicycle-relevant network. We apply a model for bicyclists' choice of route from origin to destination that takes the complete network into account. This enables us to determine bicyclists' preferences for a range of infrastructure and land-use types. We use the estimated preferences to compute a generalized cost of bicycle travel, which we correlate with the number of bicycle trips across a large number of origin-destination pairs. Simulations suggest that the extensive Copenhagen bicycle lane network has caused the number of bicycle trips and the bicycle kilometers traveled to increase by 60% and 90%, respectively, compared with a counterfactual without the bicycle lane network. This translates into an annual benefit of €0.4M per km of bicycle lane owing to changes in generalized travel cost, health, and accidents. Our results thus strongly support the provision of bicycle infrastructure.

A Hybrid Model for Evaluating the Bikeability of Urban Bicycle Systems

Improving people’s willingness to ride bicycles has become the main green transportation policy of the government in the world. Bikeability is an important factor affecting the willingness to ride. Since the urban riding environment is more complex than the suburbs, it is necessary to establish a complete urban bikeability evaluation framework. This study applies Bayesian BWM (Best Worst Method) and modified VIKOR to develop an urban bikeability evaluation framework. First, this study collects criteria affecting urban bikeability through literature review and experts’ surveys to develop a novel evaluation framework. Second, the Bayesian BWM was used to evaluate the relative weights of criteria and dimensions. Finally, the modified VIKOR was used to evaluate the riding environment of urban bicycle systems. The modified VIKOR replaces the relatively good concept as the aspiration level, which can effectively reflect the real situation. This study used two cities of Taiwan as case studies to demonstrate the usefulness and effectiveness of the proposed model. The results show that “completeness of facilities” is the most important dimension and “maintenance of bicycle pavements”, “width of bicycle lanes”, and “separation of bicycle lanes and car lanes” are the critical criteria. Based on the findings, some management implications and improving strategies are provided.

Smart mobility in a smart city concept

Purpose: The purpose of this publication is to present the most important features with which the smart mobility approach is characterized. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: In a smart city, smart mobility also plays an important role in environmental protection. Modern modes of transportation, such as electric cars and urban bicycles, reduce greenhouse gas emissions and air pollution. Thus, smart mobility contributes to improving air quality and the health of residents. As part of the smart city, smart mobility is also seen as part of improving traffic safety. Smart traffic monitoring systems and rapid response to dangerous situations, such as collisions or accidents, can help minimize accidents and collisions. Smart mobility is also one of the key elements of a smart city, which contributes to improving the quality of life for city residents by increasing mobility, reducing air pollution, improving road safety and introducing innovative transportation solutions. Originality/value: Detailed analysis of all subjects related to the problems connected with the smart mobility in smart city. Keywords: smart mobility, smart city, quality of life, biking. Category of the paper: literature review.

Nazwy stacji rowerów miejskich i systemów wypożyczania rowerów w Polsce

The article is devoted to the names of urban bike stations and bicycle-sharing systems in Poland. The main naming types of urban bike stations are discussed using selected examples from across the country. The aim of the analysis was an attempt to show the similarities and differences in relation to other urban names, namely to the names of public transport stops). It was concluded that the same naming models were used in both types of onyms. The onomastic analysis of the names of bicycle-sharing systems aimed to indicate the main functions of these onyms. It has been shown that individual elements of the names of bicycle-sharing systems have a persuasive and identifying function, and language games were used to create the names.

Safety Factors and Patterns of Bicycle Trauma in a Tertiary Level 1 Trauma Center in Boston, Massachusetts: A Retrospective Review and Survey Study.

Increasing bicycle ridership is accompanied by ongoing bicycle-related accidents in many urban cities. There is a need for improved understanding of patterns and risks of urban bicycle usage. We describe the injuries and outcomes of bicycle-related trauma in Boston, Massachusetts, and determine accident-related factors and behaviors associated with injury severity. We conducted a retrospective review via chart review of 313 bicycle-related injuries presenting to a Level 1 trauma center in Boston, Massachusetts. These patients were also surveyed regarding accident-related factors, personal safety practices, and road and environmental conditions during the accident. Over half of all cyclists biked for commuting and recreational purposes (54%), used a road without a bike lane (58%), and a majority wore a helmet (91%). The most common injury pattern involved the extremities (42%) followed by head injuries (13%). Bicycling for commuting rather than recreation, cycling on a road with a dedicated bicycle lane, the absence of gravel or sand, and use of bicycle lights were all factors associated with decreased injury severity (p<0.05). After any bicycle injury, the number of miles cycled decreased significantly regardless of cycling purpose. Our results suggest that physical separation of cyclists from motor vehicles via bicycle lanes, regular cleaning of these lanes, and usage of bicycle lights are modifiable factors protective against injury and injury severity. Safe bicycling practices and understanding of factors involved in bicycle-related trauma can reduce injury severity and guide effective public health initiatives and urban planning.

Skid Resistance Analysis of Urban Bike Lane Pavements for Safe Micromobility

The use of micromobility vehicles is considerably growing in cities worldwide. As a result, crashes involving these vehicles are also increasing, with single-bicycle crashes accounting for a significant percentage. In most infrastructure-related crashes, the road surface was slippery. In this context, the study of pavement skid resistance is crucial to improve micromobility safety. In this research, the British pendulum tester was used to test the skid resistance of 5 different types of pavements on 17 bike lane locations in Valencia (Spain). Additionally, micromobility users’ speed was collected to analyse users’ behaviour. The results showed that asphalt, concrete, and rough painted tile pavements had the greatest skid resistance, whereas painted cobble and smooth painted tile pavements presented poor skid resistance. These values were compared with the limits set by the few guidelines that includes skid resistance thresholds. Moreover, skid resistance variability was also studied, with asphalt pavement being the most homogeneous. Based on the results of the research, several recommendations are proposed for the pavement to be used in the micromobility facility according to its typology. To this end, the investigatory level of skid resistance and the minimum braking distance required were also defined for each type of pavement and bike lane. The findings of this study contribute to the consideration of micromobility safety from the construction stage to the pavement management.

Designing efficient urban bike path networks that meet the needs of cyclists.

Designing efficient urban bike path networks that meet the needs of cyclists.

Analysis of the Social Capital in a Technological System of a Smart City Using a PLS-SEM Model

In this paper, social capital was analyzed through the lens of the resources and capabilities theory, understanding it as a capability that has a role in the performance of the Smart City. Social capital relates to the technological capabilities and the technological learning process. The objective of this investigation was to build a representation model of the social capital resources and technological learning, taking social capital as a capability of the Smart City. First, on a methodological level, a preliminary exercise that shows the relation and the gaps between the concepts and the process to build the model are presented. Later, the Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to build the conceptual and measured model. Based on the configuration of the tangible variables, the model was tested with the information provided by the final users of the public urban bicycle system of the Medellín–EnCicla technological system. The impact of technological learning is interpreted by relating it to the sources of social capital, to its resources (trust, social interaction, and shared vision), and to the technological system chosen. As a result, the construction of an exploratory model is presented that interprets the resources of social capital and their relation to technological learning in a smart city. The investigation aims to contribute to the decision-making exercise in the public policies regarding the sustainable mobility of the smart city that was the object of study.

Growing urban bicycle networks

Cycling is a promising solution to unsustainable urban transport systems. However, prevailing bicycle network development follows a slow and piecewise process, without taking into account the structural complexity of transportation networks. Here we explore systematically the topological limitations of urban bicycle network development. For 62 cities we study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning: cities must invest into bicycle networks with the right growth strategy, and persistently, to surpass a critical mass. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality. Growing networks from scratch makes our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal data requirements.

Automated Detection of Missing Links in Bicycle Networks

Cycling is an effective solution for making urban transport more sustainable. However, bicycle networks are typically developed in a slow, piecewise process that leaves open a large number of gaps, even in well‐developed cycling cities like Copenhagen. Here, we develop the IPDC procedure (Identify, Prioritize, Decluster, Classify) for finding the most important missing links in urban bicycle networks, using data from OpenStreetMap. In this procedure we first identify all possible gaps following a multiplex network approach, prioritize them according to a flow‐based metric, decluster emerging gap clusters, and manually classify the types of gaps. We apply the IPDC procedure to Copenhagen and report the 105 top priority gaps. For evaluation, we compare these gaps with the city’s most recent Cycle Path Prioritization Plan and find considerable overlaps. Our results show how network analysis with minimal data requirements can serve as a cost‐efficient support tool for bicycle network planning. By taking into account the whole city network for consolidating urban bicycle infrastructure, our data‐driven framework can complement localized, manual planning processes for more effective, city‐wide decision‐making.

Probability Model of Riding Behavior Choice of Two-Wheelers under the Influence of the Subsidence Area of a Manhole Cover

To objectively evaluate the influence of the subsidence area of a manhole cover on the riding behavior of two-wheeler riders in urban bicycle lanes, a probability model of riding behavior of two-wheeler riders was established. The main factors influencing the probability of riding behaviors under the condition of the subsidence area of a manhole cover are investigated. Based on the field data of 10 survey sections in Guilin, the regression models between the selected probability of independent riding behavior, combined riding behavior and the main influencing factors are respectively established by using the multiple regression analysis method. The results show that the selected probabilities of deceleration riding behavior and original-speed riding behavior have a linear regression relationship with the subsidence depth of the manhole cover and the lane integrity. The subsidence depth of manhole covers is positively correlated with the probability of deceleration riding behavior and negatively correlated with the probability of original-speed riding behavior; lane integrity is positively correlated with both the probability of deceleration riding behavior and the probability of original-speed riding behavior. The data goodness-of-fit value for the relevant influence factors in the two models is 0.941 and 0.900, respectively. The probability of acceleration riding behavior has a linear regression relationship with the lane integrity and section flow. Lane integrity is negatively correlated with the probability of acceleration riding behavior and positively correlated with section flow. The data goodness-of-fit value for the relevant influence factors is 0.821. The probabilities of straight riding behavior and detour riding behavior have a linear regression relationship with the subsidence depth of manhole covers and the minor width of the flat part. The subsidence depth of a manhole cover is negatively correlated with the probability of straight riding behavior and positively correlated with the probability of detour riding behavior. The minor width of flat part is negatively correlated with the probability of straight riding behavior and positively correlated with the probability of detour riding behavior. The data goodness-of-fit value for the relevant influence factors in the two models is 0.601 and 0.603, respectively. The results provide the theoretical basis for the evaluation of the severity and optimization design of the subsidence of manhole covers in urban bicycle lanes.