Bicycle Trips Research Articles

Association between network characteristics and bicycle ridership across a large metropolitan region

Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership. We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated (length of the bicycle network, betweenness centrality, degree centrality, network density, network coverage, intersection density and average weighted slope) and Bayesian spatial models were used to explore associations between these network characteristics and bicycle ridership. We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region. These findings challenge the utility of approaches based on spatially modeling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. Further efforts are required to be able to quantify network characteristics that reflect the myriad of factors that influence comfort and safety for people of all ages and abilities.

ГАСТРОНОМІЧНА СПАДЩИНА ЯК МЕХАНІЗМ ЗБЕРЕЖЕННЯ НАЦІОНАЛЬНОЇ ІДЕНТИЧНОСТІ УКРАЇНЦІВ

The article examines the concepts of "identity" "national identity", "European identity", gastronomic heritage. In the conditions of transformation of global economic relations international trade in services offers research and implementation of tourist services to meet the demand of various categories of consumers. Gastronomic tourism is one of the options for recreation in modern conditions. The catalog of the gastronomic heritage of the cultural and pilgrimage route "Camino Podolico" of the cultural and educational route from Vinnytsia to Kamianets-Podilskyi similar to the Ways of St. James leading to the city of Santiago de Compostela. The route connects 13 points of Vinnytsia and Khmelnytskyi regions. It allows you to plan a walking or cycling trip with the necessary infrastructure for food and overnight stays every 20–25 kilometers. The uniqueness of the Camino Podolico lies in the fact that it passes through the most picturesque places of eastern Podillia. In the first part of it travelers have the opportunity to walk along the picturesque banks of the Southern Bug and Riv rivers. The route popularizes communities in Vinnytsia and Khmelnytskyi regions as a tourist attraction among domestic and foreign tourists and creates the necessary conditions for the development of local businesses. Moreover an electronic track of the route which is constantly being improved. Everyone who completes the route on foot or by bicycle will receive a passport for markings at the beginning of the route and a certificate of completion. "Camino Podolico" is implemented by territorial communities of Vinnytsia and Khmelnytskyi regions and DART. The catalog is a bilingual edition containing 22 recipes of unique dishes collected in 11 communities of Vinnytsia and Khmelnytskyi. Kamianets-Podilskyi is the final point on the Camino Podolico where we explored the intangible cultural heritage of communities. It is also planned to study and prospects of the development of tourism during the war and the mechanisms of the post-war recovery of the industry which were determined at the 1st National Tourism Summit.

Analysis of Changes in Characteristics of Shared Bicycle Trips in Megacities

Analysis of Changes in Characteristics of Shared Bicycle Trips in Megacities

From Geospatial to Spatial -Omics

When Los Angeles is mentioned, cycling is usually not the first thing that comes to mind. However, during my past 10 years in LA studying molecular biology and bioinformatics, my bike trips through the geographical space of LA have inspired many ideas in my research in spatial data analysis in bioinformatics. I have written software to bring decades of research in geospatial data analysis to spatial -omics, as my trips make me ponder on spatial phenomena in general.

Unveiling the roles of public bike systems: From leisure to multimodal transportation

As bike-sharing systems gain popularity, understanding their roles in urban transportation, such as the first/last mile problem and the replacement of public transit, is crucial to comprehensively promote urban transport systems. Due to the Internet of Things (IoT) technique, copious data from public bike trips worldwide are available to urban researchers. In this study, we examine how public bikes function not just as a means of transportation, but also for leisure. The leisure function was ignored in previous empirical studies using data mining techniques, an oversight that could skew results in places like Seoul where leisure trips constitute a significant portion of the trips. We investigate when, where, and to what degree bike-sharing serves as a leisure tool and substitutes for, integrates with, or complements the public transit system. We propose an integrated data-mining approach to identify the above four categories of public bike trips. Our method combines multiple sources of real-world data, the distributed density-based spatial clustering of applications with noise (DBSCAN), and spatial feature engineering. The results reveal that the distribution of each category shows substantial spatial–temporal heterogeneity. Longitudinal analysis reveals that the use of public bicycles as a transportation mode primarily contributes to the growth observed from 2018 to 2021, compared to use for leisure. The determining factors and non-linear functions of each type of bike trip were interpreted by explainable machine learning methods, Shapley additive explanations (SHAP), and partial dependence plot (PDP). We discovered that lower slopes, higher residential densities, and more commercial neighborhoods consistently attract users across all categories. Additionally, rivers and green areas attract more leisure trips, while areas with denser transit routes see more substitution trips and fewer complementary trips. By understanding the characteristics, spatial–temporal distribution, and determining factors of public bike trips across these four categories, city planners and operators can tailor services to meet the diverse needs of citizens, fostering a more convenient, sustainable, and accessible urban transport system.

Cyclists’ perception of streetscape and its influence on route choice: A pilot study with a mixed-methods approach

To encourage behavioural changes that reduce car use, understanding travel behaviour and interventions in infrastructure are necessary. Understanding the route choices of cyclists and their motivation based on their preferences regarding their trip attributes is essential to improve cycling infrastructure and encourage active travel modes such as cycling. This research aims to explore the influence of streetscape design features (SDFs) on cyclists' route choices by developing a mixed-method approach to collect objective and subjective data from 22 volunteers. We used a survey (Maptionnaire) with a virtual reality (VR) and eye-tracking experiment to simulate a bicycle trip in Enschede, The Netherlands. Results showed that despite differences between usual and ideal routes regarding SDFs (such as the presence of vegetation, water bodies, and road intersections), factors such as infrastructure provision and quality have a more significant impact when choosing a route. The novelty of the proposed methodology lies in combining data from the different methods to improve understanding regarding the influence of SDFs in cyclists' route choices and raise awareness among the participants about the influence of SDFs on their route choices. These results may encourage future studies to develop and implement alternative mixed-methods approaches to increase understanding of cyclists' travel behaviour. From a practitioner's perspective, implementing such mixed-method for data collection may increase the efficiency of the process to aid in the development of contextual data-driven interventions that encourage the use of bicycles.

Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent‐based approach

AbstractConvincing commuters to use a bike is a timely contribution to reach sustainability goals. However, more than other modes of transportation, cycling is heavily influenced by the current meteorological conditions. In this study, we assess the weather conditions experienced on individual cycling routes through an urban environment and how weather observations and forecasts may give guidance to a better cycling experience. We introduce an agent‐based model that simulates cycling trips in Hamburg, Germany, and a three‐category traffic light scheme for precipitation, wind and temperature comfort. We use these tools to evaluate the cycling weather based on the commonly used single‐station measurement approach versus spatially dense observations from an urban station network and radar measurements. Analysis of long‐term data from a single station shows that most frequently discomfort is caused by temperature with a probability of 33%. Wind and precipitation discomfort occur only for about 5% of the rides. While temperature conditions can be well assessed by a single station, only one‐third of critical precipitation events and less than 10% of critical wind events are captured. With perfect knowledge, temporal flexibility in start time of less than ±30 min reduces the risk of getting wet by 50%. For precipitation, nowcasting is able to predict 30% of the critical events correctly, which is significantly better than model forecasts. Operational ensemble forecast provides satisfactory guidance concerning temperature; however, the limited predictability of precipitation and wind renders these forecasts only useful for riders with a high risk‐awareness and small sensitivity to false alarms.

Spatial and temporal variability of urban cyclists’ exposure to PM2.5 in Medellín, Colombia

Fine Particulate Matter (PM2.5) has become a major issue in cities around the world as it adversely affects human health and the environment. This study aims to develop a deeper understanding of the impact of bicycle lanes designs on cyclist exposure to air pollution in a developing-country city. PM2.5 concentrations were measured along a predefined route with different bicycle lane designs in the city of Medellín, Colombia. The measurement campaign was made between October and December 2020 during peak and off-peak hours on weekdays, where a total of 29 bicycle trips were carried out. To obtain accurate measurements, we used a laser-based particle monitoring system. The study's findings reveal that the bicycle route section without dedicated bicycle lanes had the highest PM2.5 exposure and inhaled dose per kilometer traveled. Furthermore, the mean PM2.5 exposure for cyclists during the morning peak hours was higher (33.8 μg/m3) compared to the evening peak (16.1 μg/m3) and off-peak hours (11.1 μg/m3). The inhaled PM2.5 dose was three times higher during morning peak hours than during off-peak hours and twice as high during evening peak hours. These results show that segregated cycling lanes on the sidewalk can considerably lower PM2.5 exposure and inhaled doses for cyclists when compared to other lane designs, highlighting the significance of infrastructure development in supporting sustainable transportation and public health.

A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset

A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

Identifying and Modeling the Factors That Affect Bicycle Users’ Satisfaction

The parameters that affect bicyclists’ satisfaction are of vital importance when it comes to determining the reasons that endure bicycle usage in an urban environment. This research refers to the factors that affect bicycle users’ satisfaction with the existing infrastructure in the Municipality of Thessaloniki, Greece. Bicycle dependence in Thessaloniki is relatively limited when it is compared to other European cities with similar topological and demographic characteristics. This article aims to determine the most suitable measures that policymakers should implement to enhance bicycle infrastructure. The data collection process was realized through an online questionnaire survey addressed to the bicycle organizations and groups that are situated in Thessaloniki. Inferential statistical analysis investigated the dependency between the perceived satisfaction and the perceived level of safety of cyclists. Ordinal and multinomial logistic regressions were applied to identify the significant problems that influenced cyclists’ satisfaction, as well as the most important improvement measures suggested by the survey participants. The findings revealed that the lack of safety and urban integration of bicycle infrastructures were the statistically significant issues affecting users’ satisfaction. The development of a safe, integrated, and interconnected bicycle network area emerges as a priority to increase cyclists’ satisfaction and daily bicycle trips in cities with low bicycle culture.

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.

Differences in walking and cycling between professional immigrants and comparable ethnic Dutch: A quantitative analysis from the Netherlands

Active travel (e.g. walking and cycling) is encouraged in the Netherlands for the many environmental and health benefits it offers, and, as a result, the country has among the highest rates of cycling in the world. This is at least partially attributed to investments in cycle infrastructure. Yet few other countries have followed the Netherlands lead and invested similarly, and arguments are made that the Dutch life experience and culture are also important in encouraging walking and cycling. To examine the influence mobility culture on active travel behaviour, this study assesses similarities and differences in walking and cycling between a sample of professional immigrants living in the Netherlands’ Randstad region and a socio-demographically comparable sample born and raised in the Netherlands. The study found both similarities and differences in walking and cycling between professional immigrants and ethnic Dutch, with the immigrants actually walking and cycling more than the ethnic Dutch. In order to identify factors that influenced preferences to and levels of walking and cycling, a multiple linear regression analysis of dichotomous and ordinal variables based on their polychoric correlations was applied. Findings show that socio-demographic characteristics, car and bicycle access and trip purpose have a significant effect on the active travel behaviour of the two population groups. In addition, findings demonstrate that car access and use is associated with reduced active travel mode use. However, the reasons and causes of the differences in walking and cycling behaviour between immigrants and ethnic Dutch need further research.

Temporal metagraph: A new mathematical approach to capture temporal dependencies and interactions between different entities over time

Predicting real estate prices is a difficult task that requires consideration of various factors and their dynamic interactions over time. In this study, based on the introduction of a new mathematical structure called temporal metagraph, we use a novel approach that exploits the properties of this structure to predict real estate prices in Helsinki by integrating information derived from bicycle trips. The temporal metagraph concept intrinsically captures temporal dependencies and interactions between different entities or agents (nodes of the metagraph), allowing us to model and analyze different real situations and, in particular, the dynamic relationships between bicycle commuting and real estate prices. Our experimental results demonstrate the effectiveness of the proposed approach, as more accurate and reliable predictions are achieved than traditional models, which are based solely on historical price data.

A bike trips survey as an impetus for the transition to sustainable cities and societies

This study aims to analyse the factors and strategies that condition the bicycle's use and highlight various unknown facts about the electric bicycle. To identify the brakes (examples: road safety, urban culture or social norms) on the use of the bicycle and the possibilities of modal shift, a survey was carried out within three main campuses at the University of Liege (ULg) in Belgium. A brief presentation of the main routes and the description of the route most taken (based on cartographic identification, a field visit and interviews) complete the survey results. During this study, 29,000 people (students, doctoral students, and staff members) were contacted to participate in the survey. The results of this work show that despite the topographical conditions of the city studied, a priori unfavourable, and some major obstacles, such as the lack of cycle paths and road insecurity, or the price in the case of the electric bicycle, the potential for a modal shift towards cycling, and particularly towards electric bicycles, is great. 70 % of respondents live less than 12 km from the University, a distance considered the limit for travelling by electric bike. Mitigating these brakes is a priority to stimulate modal shifts. The study of the image of the bicycle and the perception of the brakes shows that better communication could avoid erroneous evaluations as to the possibility of travelling by bicycle. Analysis of the profile of respondents reveals that contrary to the data in the literature, students represent the public least attracted to cycling. Initiating a modal shift to obtain a mass effect is therefore essential. One of the most important factors in pursuing a modal balance is the development of safe cycle paths, where a potential increase in bicycle use is recognized by 62 % of car users, 82 % of bus, 62 % of staff and 74 % of students. Finally, some paths are suggested to improve the downtown-rural cycle route and promote the use of bicycles in the university community.

A data fusion approach with mobile phone data for updating travel survey-based mode split estimates

Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorisation techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit (all public transportation), motorised (cars and similar vehicles), active (pedestrian and cycle trips), and taxi (traditional taxi and ride-hailing services). Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. Findings also point to a rise in the share of taxi usage, and a drop in active mobility, suggesting a modal shift towards less sustainable modes of travel. We validate our findings comparing our updated estimates with official smart card transaction data. The consistency of findings with expert domain knowledge from the literature and historical transport usage trends further support the robustness of our approach.

Exploring intra-urban human mobility and daily activity patterns from the lens of dockless bike-sharing: A case study of Beijing, China

In recent years, the emergence of dockless bike-sharing has brought new ways of transportation, while also providing support for observing urban dynamics at a finer granularity. This study uses dockless bike-sharing data, combined with point of interest (POI) data, to explore intra-urban human mobility and daily activity patterns, using Beijing as a case study. Here, we employ spatial statistics and graph network to quantify the characteristics of travel behavior. Firstly, spatiotemporal statistics analysis was conducted to investigate the travel patterns and spatiotemporal differences of bike-sharing in Beijing. It was found that bike trips have clear peak hours and highly overlap with the subway lines, indicating that the bike-sharing system effectively completes the ‘last mile’ of travel in Beijing. Secondly, graph networks were used to reveal the core patterns and community structures of human mobility in Beijing and their connections to daily activities. Our findings indicate that there are different core structures of human mobility in Beijing, some dominated by a single core node, while others are formed by the aggregation of multiple cores. Finally, POI data was used to analyze human daily activity patterns. We analyzed the temporal characteristics and the hotspot areas of each daily activity in Beijing and found that transferring to other transportation modes is the most popular activity for people riding bikes, followed by shopping, working, and eating out. These findings provide a reference for understanding urban human dynamics and urban structure from the perspective of dockless bike-sharing.

Exploring year-to-year spatiotemporal changes in cycling patterns for bike-sharing system in the pre-, during and post-pandemic periods

The bike-sharing system plays an important role during the COVID-19 pandemic due to its benefit of reducing the risk of infection. Previous studies have employed bike trip datasets to analyze the impact of COVID-19 on spatiotemporal cycling mobility, which was limited to a short pandemic period. An understanding of the long-term impact of the pandemic on human travel patterns is urgently needed. This paper presents a framework for investigating the long-term spatiotemporal change in cycling patterns in the pre-, during and post-pandemic periods. A spatiotemporal clustering algorithm is combined with spatiotemporal visualization, network analysis, and important location identification to explore latent mobility patterns. The strength of the proposed framework is that it can model a unified representation of cycling networks across different years, and provide insightful spatiotemporal patterns that allow easy interpretation. The experiments are conducted on five years of data collected from the ‘Citibike’ system in New York City. The results demonstrate the efficiency of the proposed approach in exploring the evolutionary cycling patterns of how people respond to the COVID-19 pandemic and are beneficial for various stakeholders.

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.

Sensitivity and specificity of measuring children's free-living cycling with a thigh-worn Fibion® accelerometer.

Cycling is an important part of children's active travel, but its measurement using accelerometry is a challenge. The aim of the present study was to evaluate physical activity duration and intensity, and sensitivity and specificity of free-living cycling measured with a thigh-worn accelerometer. Participants were 160 children (44 boys) aged 11.5 ± 0.9 years who wore a triaxial Fibion® accelerometer on right thigh for 8 days, 24 h per day, and reported start time and duration of all cycling, walking and car trips to a travel log. Linear mixed effects models were used to predict and compare Fibion-measured activity and moderate-to-vigorous activity duration, cycling duration and metabolic equivalents (METs) between the travel types. Sensitivity and specificity of cycling bouts during cycling trips as compared to walking and car trips was also evaluated. Children reported a total of 1,049 cycling trips (mean 7.08 ± 4.58 trips per child), 379 walking trips (3.08 ± 2.81) and 716 car trips (4.79 ± 3.96). There was no difference in activity and moderate-to-vigorous activity duration (p > .105), a lower cycling duration (-1.83 min, p < .001), and a higher MET-level (0.95, p < .001) during walking trips as compared to cycling trips. Both activity (-4.54 min, p < .001), moderate-to-vigorous activity (-3.60 min, p < .001), cycling duration (-1.74 min, p < .001) and MET-level (-0.99, p < .001) were lower during car trips as compared to cycling trips. Fibion showed the sensitivity of 72.2% and specificity of 81.9% for measuring cycling activity type during the reported cycling trips as compared to walking and car trips when the minimum required duration for cycling was less than 29 s. The thigh-worn Fibion® accelerometer measured a greater duration of cycling, a lower MET-level, and a similar duration of total activity and moderate-to-vigorous activity during free-living cycling trips as compared to walking trips, suggesting it can be used to measure free-living cycling activity and moderate-to-vigorous activity duration in 10-12-year-old children.