Journal of Cycling and Micromobility Research最新文献

Purpose

The aim of this study was to describe how electric-assist (e-assist) handcycles facilitate participation in adapted mountain biking for people with a spinal cord injury.

Materials and Methods

Ten adults (n=10) living with a spinal cord injury who actively ride trails each completed an online semi-structured interview. Seven of these participants have ridden nature trails using an e-assist handcycle. Following descriptive phenomenology principles, interviews were thematically analyzed using NVivo software.

Results

Commentary on e-assist was categorized into the following themes: e-assist descriptions, e-assist as enjoyable, and e-assist accessibility. Handcycles with pedal e-assist were described as a prominent facilitator, whereby the electric motor can mitigate overexertion, help navigate uneven terrain, and reduce injuries. Participants described using e-assist mountain biking handcycles as a way to facilitate perceived safety, inclusivity, and overall accessibility in adapted mountain biking participation.

Conclusions

Findings of this research emphasize the importance of innovative technology in facilitating adapted mountain biking for people with spinal cord injuries. E-assist mountain biking handcycles make the outdoors more accessible to people with a spinal cord injury by increasing opportunities to enjoy exploring nature and travel further, with enhanced safety features, which are not replicable while using a traditional handcycle or wheelchair.

While shared micromobility has been gaining popularity in developed countries, these innovative technologies have yet to penetrate the market of mid-sized cities in developing countries, which make up the overwhelming majority of cities in the world. Shared micromobility includes inexpensive systems that could drive the first wave of electrification in the transportation sector in these regions. We designed and implemented a dynamic stated preference pivoting survey and used a panel data mixed logit model to assess the effect of temperature, precipitation, and availability of bike lanes on the propensity to use bikeshare, e-bike share, and e-moped share, controlling for sociodemographic factors. Using Kathmandu, Nepal, as a case study, where shared micromobility does not currently exist, we also assessed modal shifts from the existing travel modes. We found heavy rain negatively impacts users’ preference for shared micromobility, while users preferred e-moped share during cold temperatures. The effect of bike lane availability was positive but weak on bikeshare and e-bike share. Gender also had an effect on the choice of shared micromobility vehicles – females preferred e-mopeds over other vehicles. Ride-hailing users had a high preference for e-moped share, while introducing bikeshare and e-bike share caused a uniform modal shift among existing travel modes. We recommend that transportation agencies begin micromobility pilot programs by combining this study’s findings with the best practices of existing micromobility programs. We also suggest collecting usage and operations data to empower data-driven decision-making.

Our understanding of non-linear and time varying effects on shared e-scooter ridership dynamics is limited. Consequently, both operators and city councils supporting shared e-scooter schemes do not have the requisite information to help optimise infrastructure planning and operation management. Focussing on subtropical Brisbane, Australia, the current study examines time varying and non-linear effects of weather and built environment factors on shared e-scooter ridership. Results from XGBoost models reveal threshold relationships with both the availability of cycling infrastructure and the presence of park and commercial land uses. Additionally, we show how hot weather increases ridership especially around large parks and in commercial areas on both weekdays and weekends. Understanding the intricate (non-linear) interplay (interaction) between weather and built environment factors and their variation over time on shared e-scooter ridership have important implications for policymakers, transportation planners, and environmental advocates in providing the requisite evidence for data driven decision making.

Many cities are currently planning and building new large-scale active transport networks to achieve a wide array of environmental, economic, and social equity goals. This is often combined with developing public campaigns to increase public awareness of their plans and introduced infrastructure to build a culture that celebrates and supports using them. While several studies explored the impacts of active transport infrastructure on users perception and travel behaviour, very little has been done to explore the factors associated with people’s awareness of such infrastructure at an early stage of their introduction. Accordingly, this study examines the factors affecting people’s likelihood of being aware of a new flagship cycling network in Montréal known as Réseau Express Vélo (REV), while understanding equity of awareness across gender identities. To achieve this goal, the study uses summary statistics and weighted multilevel logistic regressions to analyze data collected from a large-scale survey. The results of the paper show that various socioeconomic factors including age, ethnicity, income, language, as well as individuals’ travel behaviour and lifestyle are associated with being familiar with such a large bike network. Significant differences between women and men can also be observed, in which women are less likely to be aware of REV. Younger women in their 20 s tend to know much less about the project in comparison with men in the same age group. Women who identify as non-white only and used English to complete the survey are the group with the lowest probability of being aware of the project. Findings from this research unmask key aspects related to the likelihood of being aware of a new large-scale cycling network, offering important insights to transport planners, policy makers, and researchers.

Active travel to school is an important contributor to the physical activity levels of children and adolescents and the source of many health benefits. In this work, we model cycling to school in Finland. The probability to cycle on a given trip is modelled using a binary logistic regression model based on trip length, average route gradient, the cyclist’s gender, and age. Variables denoting the city regions were also included to account for differences in cycling cultures and infrastructure. In addition, weather variables were added as cycling levels in Finland are highly dependent on the season. Air temperature and the presence of snow were found to reflect well the observed seasonal variations. The observed influence of winter conditions on trip distances and the cycling of girls is also replicated in the model through interaction terms. This model is employed to explore two sustainable mobility scenarios: a scenario where the cycling of girls increases to the same level as that of boys and a scenario where all school children cycle as much as those living in Oulu, Finland’s top cycling region. Our results suggest that it would be possible to increase the number of trips by bicycle and cycled mileage significantly, up to 76 % in the Oulu scenario, even though school children already cycle much more than the general population.

In recent years, efforts to promote safer active school travel (AST), i.e., walking and cycling behaviors in girls, have heightened in light of huge gender disparities in micromobility adoption. In developing countries where transport infrastructure is not advanced, and AST adoption is influenced by societal perceptions, theorizing empirical studies will help better explore and understand the travel needs of girls for effective planning. This study investigated the factors influencing AST behaviors of 232 adolescent girls in primary, junior, and senior high schools based in Sunyani Municipality, Ghana. A novel combination of the Health Belief Model and the Theory of Planned Behavior was employed to formulate a conceptual model that was evaluated using SEM-MIMIC modeling. Results revealed that age plays a significant role in AST preferences, with younger girls favoring walking and older girls showing interest in cycling. Interventions can capitalize on this to promote cycling habits during the transition to senior high school. Moreover, environmental attitudes have the most decisive influence on the perceived social benefits of AST, while perceived parental support positively impacts safety self-efficacy. Together with perceived parental support, safety self-efficacy, and distance/accessibility barriers, they significantly influence girls' intentions to use active transportation. Authorities can adopt proposed targeted interventions such as the walking school bus and bicycle train concepts to make AST more attractive to girls.

This study introduces a progressive methodology for examining the travel behaviour of bicyclists and pedestrians under both daylight and electric lighting conditions. By analysing data from naturalistic/undisturbed settings, we aimed to assess the representativeness of participant behaviour in an invited context compared to that of typical road users. This paper expands upon the methodological exploration initiated in the original Video Analysis of Pedestrian Movement (VAPM) study by analysing the microscopic movements of both pedestrians and bicyclists in outdoor environments under both natural and electric lighting conditions. Furthermore, the paper investigates bicyclists’ bi-directional interactions in a naturalistic setting, adding a valuable dimension to the study's comprehensive analysis. Our research introduces an effective approach to data collection through the use of drone technology to obtain microscopic data from video analysis. This method has the potential to enhance travel quality for bicyclists during the hours of twilight and darkness and increase the likelihood of short motor vehicle trips being replaced by active, sustainable forms of transport.

Local authorities actively advocate for cycling as a pivotal mode to shift urban transportation towards greater sustainability. Weather significantly influences bicycle traffic and may hinder the spread of bicycle adoption, potentially limiting its impact to mitigate climate change. Likewise, rising temperatures and extreme weather events are anticipated to influence mobility patterns. To better understand the complex effects of weather on bicycle traffic, an explainable artificial intelligence analysis is carried out on four territories in France. Employing a neural network, we model the effects of weather conditions and control variables (e.g., pollution) on bicycle traffic. Subsequently, we examine the marginal effects of each variable using Accumulated Local Effects plots. Based on this analysis, we formulate a nonlinear model with seasonal autoregressive with moving-average errors. This analytical model encapsulates new equations describing the effects of weather conditions on bicycle traffic. The methodology combines the ability of black-box model to capture complex nonlinear relationships without prior assumptions, with the transparency and generalization capabilities of analytical models. It also highlights the asymmetric sensitivity of bicycle traffic to humidity, with humid conditions being more deterrent than dry conditions. Statistical analysis reveals that atmospheric pressure is significantly correlated to bicycle traffic, whereas air quality does not demonstrate notable effects, contrary to observations in other territories.

Cyclist physical exertion is largely ignored in quantitative travel analysis, partly due to a lack of appropriate tools. Microscopic models of second-by-second energy expenditure based on equations of motion are data intensive and cannot be applied to hypothetical routes (such as needed for route choice modelling). Macroscopic models of aggregate energy expenditure based on a fixed assumed energy intensity are insensitive to traveller, trip, and contextual factors that are relevant for behavioural research and policy analysis (such as bicycle type or trip purpose). Building on concepts from motor vehicle emissions analysis, this paper proposes a mesoscopic approach to model cycling trip energy expenditure based on the distribution of travel time across discrete states of motion (“operating modes”) for different classes of traveller and trip (“model segments”). We aim to answer two key questions for model implementation: 1) which variables most effectively classify trips into model segments and 2) what operating mode definition most consistently characterizes cycling energy expenditure within model segments? We also evaluate the precision of the mesoscopic model relative to cycling energy estimates from microscopic and macroscopic models. Applied to a dataset of naturalistic cycling trips in Vancouver, Canada, the proposed mesoscopic model with six model segments based on 3 segmenting variables (rider gender, electric-assist bicycle, and high or low speed tier) explains up to 28 % of the variance in trip-level energy estimates from the microscopic model (within around 35 W of the microscopic estimates, on average). Further research to develop cycling trip energy models for general application is discussed.

With a growing number of shared mobility options, choosing not to own a car may become a new norm in the future. Shared mobility offers users a flexible range of low or zero-emission alternatives, potentially reducing the use of private cars, as well as noise and air pollution. However, to date, there is little evidence of shared mobility acting as an alternative to private car use. Indeed, recent research suggests that shared vehicles not only substitute trips by private car, but also trips by active travel and public transport, thus not resulting in emission savings. Therefore, as part of the eHUBS project focusing on shared electric mobility hubs, the mode substitution patterns of 602 self-reported shared mobility users across Europe were investigated. More specifically, respondents of an online survey were asked to recall their last trip using shared mobility and to report on both their chosen and substituted travel mode in addition to common trip characteristics including trip distance and frequency. Chosen shared modes considered were both conventional or electric alternatives including cars, bicycles, cargobikes, and scooters. Overall, the results suggested that shared vehicles were about as or more likely to substitute public transport, cycling and walking, as private car trips, thus providing a mixed picture with respect to their potential contribution to reducing private car use and associated carbon emissions. Local authorities and policy makers are urged to actively continue to improve active travel facilities and to be proactive in better integration of shared mobility and public transport services.