Journal of Cycling and Micromobility Research最新文献

This paper presents a comparison of different route choice models for cyclists. The data includes approx. 3,700 cycling trajectories. The network is derived based on the Open-Street-Map that contains street-level attribute information. We estimate two path-based and one link-based models. We present descriptive statistics, model results, resulting indicators, and compare different validation approaches. The results reveal important differences across the models, especially in context of applications and policy-making.

The objective of this research is to explore the determinants of shared bike route choice in both urban and rural settings in Germany. To this end, a stated preference (SP) survey on route choice was conducted across several German cities. Participants were drawn from the service area of a regional bike-sharing system (BSS) in Germany. This area included five major cities and several mid-sized and smaller municipalities. The survey includes responses from both users and non-users of the BSS. Mixed multinomial logit models were used in analysis of the SP data. The study also calculated the willingness to pay (WTP) values for selected attributes. Significant influences on route choice included access and egress time, ride time, and ride cost. Significant random heterogeneity was found, especially for ride cost. Further heterogeneity was reported for interactions with sociodemographic attributes. Street type, surface, and bike infrastructure also had significant effects on route choice. Linkages between infrastructural preferences and respondents’ ages were evident. Although WTP values were strongly influenced by random heterogeneity in ride cost perception, most WTP values were comparable to the values obtained in other studies.

Shared bicycle and e-scooter programs, which we refer to as ‘shared micromobility’, have been expanding in cities across the globe. To date, little research has directly examined the role that shared micromobility programs play in supporting the travel needs of low-income populations. To fill this research need, this paper aims to evaluate a subsidy program for low-income riders by examining the demographics, usage patterns, benefits and barriers for low-income riders relative to general riders. We explore this aim using a survey of 1037 Lime customers from the United States, Australia and New Zealand. Lime operates shared e-bike and e-scooter programs in seventeen countries and over 200 cities around the world. They operate a program called ‘Lime Access’ that provides subsidized rides to qualifying customers. Using descriptive and comparative statistics, we find that Lime Access riders were more likely than general riders to be locals who use shared micromobility for utilitarian purposes (commuting, shopping) as a regular part of their daily travel patterns (35% of Access riders used Lime daily vs 7% of non-Access riders) and in combination with transit (44% of Access riders connected to transit on their last trip vs 23% of non-Access). Open-ended comments revealed the important role that Lime played in meeting the mobility needs of Access customers, especially customers with a disability or who do not own a car. The findings suggest that if cities want to expand the uptake of shared micromobility among low-income populations, they may want to consider agreements that incentivise or support the expansion of subsidized ridership programs.

This study compares two different strategies for connecting bike networks – traditional design-based and algorithm-supported – to investigate how their results differ along metrics such as proportion of bike lanes along simulated routes and the resulting cycling stress. The objective is to find optimal strategies for connecting isolated existing cycling infrastructure to form complete networks that improve both active mobility and public transit ridership. By aligning the bike network with transit and activity locations, this research develops an algorithmic framework for generating a skeleton of multimodal networks best suited to become "complete streets." The network generated through an algorithm is compared with a proposed traditionally designed network to determine their relative network performance. The findings suggest that a judicious combination of traditionally designed, and algorithm-supported networks offer better cycling infrastructure than either strategy alone. In addition, algorithms can also be developed to indicate the potential for street segments to be complete streets.

Bicycle helmets are a main measure for injury prevention in case of a crash and are a central variable in transport safety studies. Despite this, helmet use data is only collected sporadically, as the observation of helmet use in traffic by human observers is costly and time-consuming. An automated method for the accurate registration of bicycle helmet use would enable the broad and precise registration of cyclists’ helmet use. In this paper, we develop and test a computer vision-based detection method that can be applied to traffic video data. We record bicycle traffic at two observation sites in Copenhagen, Denmark, and annotate a dataset of 4000 cyclists, registering their helmet use. We then train a state-of-the-art object detection algorithm on the detection of cyclists and helmet use. The developed model has good accuracy in registering active cyclists. For helmet use registration on the test data set, there was an underestimation of 0.52% (algorithm registered helmet use: 50.23%; actual helmet use: 50.75%). Cross-testing the algorithm, i.e., training on one observation site and applying it to another, results in a larger underestimation of bicycle helmet use between 5.28% and 6.31%. Finally, we apply the algorithm to a week of video data from two Copenhagen sites, identifying commuting-related peaks of cyclists and registering helmet use differences between the observation sites. This study shows that computer vision algorithms are a feasible method for the automated detection of bicycle helmet use. Further research needs to be conducted to make the site transfer more robust and to increase accuracy levels.

Across a variety of low-cycling contexts, there are ambitious targets to reduce private car use and increase cycling to decarbonise everyday mobility practices. A component of many plans to achieve this modal shift is through active travel measures that redistribute rights to space, access or speed in a way that may prioritise cycling over driving. However, public opposition to proposals that might reduce the relative accessibility of driving can limit the possibility and scope of redistributive active travel measures, thereby preventing timely climate action and broader transport system change. In this study, we explored public opposition to a major redistributive active travel scheme proposed in the electoral county of Dún Laoghaire-Rathdown, located within the Dublin Metropolitan Area of Ireland, to examine more broadly how car-based automobility is politically sustained in this unique context. We focused our analysis on 150 public consultation submissions using Faircloughian Critical Discourse Analysis. In this paper, we present several major properties of an oppositional ‘technical discourse of transport planning’, that is normatively car-centric: ‘traffic’ as car-based (im)mobility, roads as ‘traffic’ spaces, ‘traffic’ as an immutable substance, and traffic demand-led planning. We interrogate the historical origins of this discourse in the context of Ireland and consider its effects on planning practices in relation to reproducing car-based automobility. Lastly, we conclude with recommendations that can form part of a counter-discourse that is more compatible with transport decarbonisation targets: wording cycle mobility as ‘cycle traffic’, construing redistributive cycleways as spaces of ‘traffic conversion’ rather than ‘traffic diversion’, and saliently outlining a principle of vision-led planning in redistributive active travel measures, amidst prevailing assumptions that transport planning ought to continue as a primarily ‘demand-led’ practice.

Cities with high levels of bicycling tend to be some of the safest cities for all road users. This paper investigates why this relationship exists for fourteen small and mid-sized cities across the U.S. (seven with high bicycling rates and seven paired comparison cities) using ten years of data and hierarchical negative binomial regression models. Findings confirm that higher-bicycling cities are significantly associated with better overall road safety outcomes. In terms of mode choice differences, pedestrian ‘safety in numbers’ as well as reduced driving activity had a positive impact on pedestrian safety. Results from hierarchical negative binomial regressions also suggest that more compact cities were significantly associated with better road safety outcomes for all road users. In terms of socio-demographic and socio-economic factors, the results reveal equity concerns with areas with lower incomes and more non-White residents seeing more overall road fatalities.

Predicting bicycle parking demand is critical to optimizing parking facilities and thereby promoting cycling. Unfortunately, previous studies have not considered facility type and location when predicting bicycle parking demand, which is critical to meeting user needs, especially in scenarios with multiple parking options, such as on university campuses, as in our case study. The paper presents a predictive model for bicycle parking demand using a synthetic population derived from building space utilization data, a mobility survey, parking facility data, and results from a stated preference experiment on bicycle parking preferences. We evaluate the model’s quality using count data from 2022 and 2023 and the influence of including facility types (front wheel racks, u-racks, bicycle parking stations) and whether they are covered. We also analyze the influence of beeline-based distances to reach a facility and to get from the facility to the destination and examine how to weigh them.

Incorporating facility types and coverage substantially improves the model’s predictive accuracy, but only if the model’s sensitivity to walking distances between facilities and buildings is increased. This suggests that stated preference experiments on bicycle parking choice behavior may underestimate cyclists’ sensitivity to walking distances. In contrast, accounting for cycling detours to reach a facility does not contribute to prediction quality. Thus, when cyclists have multiple parking options, it is crucial to consider walking distances for realistic predictions. Furthermore, user-centered planning requires careful consideration of parking facility attributes and the specific preferences of target cyclist groups when determining the size and location of parking facilities.

Shared Use Paths (SUPs) are becoming very popular in North America due to the current initiatives that promote active travel. SUPs can accommodate different types of users, including pedestrians, bicyclists, scooterists, and skateboarders. Although the interest in SUPs continues to increase, relatively less research has been performed on their utilization, especially using revealed preferences. Therefore, this study utilizes the survey data collected from Edmonton, Canada, between June 12th to 19th 2018 to explore the likelihood of utilizing the SUPs and the associated frequency of use. Results indicate that not all variables associated with the likelihood of utilization are also associated with the frequency of use. Specifically, higher levels of education influence the likelihood of SUP utilization, while the higher frequency of SUP usage is influenced by the secondary modes of transportation. On the other hand, as the age increases, the likelihood and frequency of SUP usage decreases. Further, households with higher income are associated with a higher likelihood of SUP utilization, male residents are likely to use the SUPs more frequently compared to their female counterparts. Other variations are also observed for home ownership and whether the resident resides in a downtown area. The application of the findings to the city planners and active travel initiatives have been provided to improve the planning and installation/construction of the SUPs facilities.

By offering the opportunity to make longer trips at a lower level of physical activity, the e-bike provides a promising alternative to car use. Despite all advantages (e-)cycling brings to urban accessibility, the environment, physical and mental health, not all car commuters regard the e-bike as a suitable alternative yet in their daily activity patterns. This study reports on changes in behavioral intention and actual e-cycling brought about by an e-cycling incentive program in the province of Noord-Brabant, the Netherlands. The impact of the program on behavioral intention and the actual change to e-cycling were analyzed based on a longitudinal three-wave survey design on past, intended, and actual commuting behavior. To explore the changes in behavioral intentional, the differences between intention and actual behavior and the factors influencing them, descriptive and ordinal logistic regression analyses were conducted. To explore the dynamics between e-cycling intentions and behavior a longitudinal structural equation model was developed. In general, this study shows that the incentive program has a positive impact on participants’ behavioral change to e-cycling during the incentive program. Results show that two-third of the participants actually use the e-bike as much as they intended at the start of the program. People who were used to taking the conventional bicycle to work before the stimulation program, are more consistent between their intention and behavior. Results also show that personal beliefs, habits, and goal-related variables do not influence the intention–behavior consistency.