Traditional Bikes Research Articles

Kinematic performance of micro-mobility vehicles during braking: experimental analysis and comparison between e-kick scooters and bikes

According to the Italian legislation, e-kick scooters and bikes are considered a single category of vehicles and can travel on the same infrastructures with the same rules; however, their kinematic behavior is very different.The adoption of a bike as a vehicle for covering short distances i.e., within 5 km is widely known both at the kinematic level and for its use by users. Conversely, e-kick scooters are "unknown" vehicles both for their kinematic characteristics and for their use by users. A handful of studies have shown how the behavior of e-kick scooters and bikes is very different; however, there are not many studies that analyze the different kinematic behavior of e-kick scooters and bikes. This study presents an experimental analysis that evaluates braking behavior by comparing e-kick scooters and traditional bikes according to several vehicle speeds.These analyzes help build a probabilistic mathematical model for estimating the stopping space of e-kick scooters and bikes. The availability of this model is crucial for the design of safe intersections between cycle paths and roads intended for motor vehicle traffic. Moreover, this model may reveal insights that could challenge the recent European regulations that equated e-kick scooters as bikes.

Machine Learning Model to Forecast Demand of Boston Bike-Ride Sharing

Bike-ride sharing systems are the new generation of traditional bike rentals, where the entire process is automated. A user rents a bike from one location and returns it at another location. There are more than 500 bike-ride sharing systems around the world, consisting of more than 500,000 bikes. Bike-ride sharing systems are typically found in urban and large cities such as Boston, N.Y. City, Washington DC, Paris, Montreal, and Barcelona. Bike-ride sharing is particularly important due to their important impact on traffic, environment, and health. As popular as bike-ride sharing systems are, there is a lack of a reliable model to forecast (predict) bike rental demand daily. Lack of available bikes constitutes an inconvenience to individuals seeking a bike at a certain location and a loss of revenues for companies operating the bikes. This paper develops a Machine Learning (ML) model (algorithm) to forecast (predict) the number of bikes rented daily based on historical data. Moreover, the model overlays environmental and seasonal settings to study their impact on bike rental demand. We test our ML model using a real-life dataset obtained from a local bike-ride sharing company in the City of Boston in the state of Massachusetts in the United States. We also applied the model to historical dataset from New York City (NYC). In both cases, the model is accurate and reliable.

Challenges and Opportunities of Emerging Data Sources to Estimate Network-Wide Bike Counts

Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning.

The impact of weather conditions on everyday cycling with different bike types in parents of young children participating in the CARTOBIKE randomized controlled trial

Knowledge about how weather conditions affect travel behavior in different user groups and contexts is relevant for planners and policymakers to facilitate sustainable transportation systems. We aimed to assess the influence of day-to-day weather on cycling for transportation among parents of young children with access to different bike types (e-bike vs non e-bike) in a natural study setting over nine months. We hypothesized less impact of weather variability on cycling when using an e-bike compared with a non e-bike. A randomized, controlled trial was conducted in Southern Norway. The intervention group (n = 18) was in random order equipped with an e-bike with trailer for child transportation (n = 6), a cargo (longtail) bike (n = 6) and a traditional bike with trailer (n = 6), each for three months. These 18 participants reported cycling on 832 out of 3276 person-days (25%). We used dynamic structural equation modeling for intensive longitudinal data to examine the relations between daily weather conditions, bike type (e-bike vs traditional bike), and cycling (dichotomized daily at yes or no). Air temperature (positively) and wind speed (negatively) were both credible predictors of cycling, whereas the other predictors (precipitation in the morning (yes or no) and presence of snow (yes or no) were not. We added interaction terms between bike type and weather conditions, but none of the interaction terms had a credible effect on cycling. Thus, the relations between weather conditions and cycling were not moderated by bike type among parents of young children.

Cumbersome but desirable-Breaking the code of everyday cycling.

IntroductionCycling for transport could integrate physical activity (PA) into daily routines and potentially increase total PA levels. However, for parents with young children, most factors affecting transport mode choice tend to facilitate car use. Greater insight is necessary into reasons for (not) using sustainable transport modes in parents with young children. Therefore, the objective of this study was to explore the experiences, including motives, perceptions, attitudes, and norms, of parents of young children by using an e-bike, a longtail bike, and a traditional bike for everyday travel to the workplace, kindergarten, and the grocery store during the autumn, winter, and spring, in nine months.MethodsSemistructured focus group interviews were conducted with 18 parents of young children residing in southern Norway. Parents were recruited through Facebook announcements and direct contact with kindergartens, selected organisations, and companies in the Kristiansand municipality. Data were analysed by systematic text condensation by using NVivo V.11.ResultsParticipants’ experiences were summarised by three main themes: ‘cycling is cumbersome’, ‘cycling reflects the desirable me’, and ‘breaking the cycling code’. Time use, planning, logistics, wet and cold weather, long distances, and no cycling habit were frequently mentioned barriers, and the most notable facilitator was the children’s attitude towards cycling. In general, children loved to cycle and preferred cycling to driving. Additionally, the freedom and independence of cycling were emphasised and valued.ConclusionIn challenging weather conditions, parents of young children may experience cycling as cumbersome but desirable, and bike access could increase the feasibility of daily cycling.

Maximum Closeness Centrality k-Clubs: A Study of Dock-Less Bike Sharing

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model.

Assessment of risky behaviours among E-bike users: A comparative study in Shanghai

In China, E2Ws were compared or equated to traditional bicycles for crash studies and regulation purposes. We compared the similarity in riding behaviours of e-bike riders, riders of traditional bicycles, e-scooter riders and riders of motorized two-wheelers (gasoline and Liquid Petroleum Gas-LPG). In Shanghai, different types of two-wheelers (N = 400) were compared on the basis of frequency of self-reported risky behaviours.Overall, the results show a continuous increase in the incidence of risky behaviours as the weight and power of vehicles increases. Based on these reported behaviours, e-bikes appear to be different from traditional bikes and similar to e-scooters and other motorized two-wheelers. E-scooters are not significantly different from other types of motorized two-wheelers.In terms of prevention and regulation, e-bikes and e-scooters could be brought closer to motorized two-wheelers in order to identify common and other targeted actions according to the type of two-wheeler.

From cars to bikes - The effect of an intervention providing access to different bike types: A randomized controlled trial.

IntroductionWe aimed to investigate whether providing parents with children in kindergarten with access to different bicycle types could influence (i) travel behavior and cycling amount, and (ii) intrinsic motivation for cycling and psychological constructs related to car use.MethodsA randomized, controlled trial was conducted in Southern Norway from September 2017 to June 2018. In total 36 parents were recruited and randomly drawn into an intervention (n = 18) or control group (n = 18). The intervention group was in random order equipped with an e-bike with trailer (n = 6), a cargo (longtail) bike (n = 6) and a traditional bike with trailer (n = 6).ResultsAt follow-up, more participants from the intervention group (vs. the control group) were classified as cyclists to the workplace (n = 7 (38.9%) vs. n = 1 (5.9%), p = 0.04), but not to the kindergarten (n = 6 (33.3%) vs. n = 2 (11.8%), p = 0.23) or to the grocery store (n = 2 (11.1%) vs. n = 0 (0%), p = 0.49). A significant (p = ≤0.05) increase in cycling frequency (0.1 to 2.0 days/week) from baseline to follow-up was found in the intervention group for all destinations and seasons, except to the grocery store during winter (p = 0.16). A decrease in frequency of car driving (-0.2 to -1.7 days/week) was found to be apparent in terms of travelling to the workplace and the kindergarten for all seasons, yet not to the grocery store for any season (p = 0.15–0.49). The intervention group (vs. the control group) reported significantly higher “intrinsic regulation” for cycling (p = 0.01) at follow-up.ConclusionAccess to different bike types for parents with children attending kindergarten resulted in overall increased cycling, decreased car use and higher intrinsic motivation for cycling. E-bikes obtained the greatest cycling amount in total, with the smallest sample variability. Hence, providing parents with children in kindergarten with access to e-bikes might result in increased and sustained cycling, also during the winter season.

Factors Influencing Electric Bike Share Ridership: Analysis of Park City, Utah

In recent years, bike share programs have become more popular as they contribute to the move toward sustainable mobility in cities. Electric bike sharing, however, remains in the early stages of development. In contrast to traditional bikes, electric bicycles (e-bikes) provide an extra boost via an electric pedal-assist motor, thereby making it much easier to travel around a city with a hilly terrain, such as Park City, Utah. The Summit Bike Share system in Park City is the nation’s first fully electric bike share system (e-BSS). Based on an analysis of historical trip data of Summit Bike Share, this paper presents the system’s performance experience and evaluates user characteristics and travel behavior. A Poisson regression analysis is performed to investigate the factors that influence the e-bike share usage of this e-BSS. The regression results reveal that weather factors, including temperature and wind speed, significantly affect e-bike share usage. It was also found that weekends, summer months, high population density, proximity to public transit centers, recreational centers, and bike trails positively affect the demand for e-bikes. The findings of this paper can help the operators of Summit Bike Share to better understand their users and their e-BSS, while also providing a guide for other e-bike projects currently in the planning stages.

Predicting The Onset Of Sweat During Cycling In Simulated Environments

E-bikes have become a growing alternative to traditional bikes. E- bikes are often used for transportation to and from the workplace, thus, in the initiative to promote adoption, advanced features such as appropriate assistance to eliminate the onset of sweat would be appealing to most users. PURPOSE: To provide a starting point for the development of a regression equation that can predict sweat onset. METHODS: Ten participants volunteered for this study. Participants committed to 5 experimental cycling sessions that varied by workload and climate. Participants cycled on an indoor bike trainer at 2 power outputs (25W and 75W) and 2 climatic conditions (25°C @ 60% RH and 30°C @ 60% RH) until sweating commenced. Physiological measures included: electromyography, heart rate, skin temperature, core temperature, galvanic skin response, and VO2. RESULTS: The average subjective sweat onset time for the 75W condition was less effected by the climatic condition than the 25W condition. The subjective sweat onset times for the 75W condition was 8.53 ± 2.19 minutes and 5.83 ± 1.44 minutes for the low and high temperatures, respectively. The subjective sweat onset times for the 25W condition was 23.52 ± 7.40 minutes and 12.49 ± 7.08 minutes for the low and high temperatures, respectively. A regression equation was developed and is able to predict subjective sweat onset with 61.5% of the variance explained with two measured variables. Workload alone explained 41.5% of the variance for sweat onset determination. When the regression was designed with workload as the outcome instead of sweat onset time, subjective sweat onset time was able to predict wattage with 40.1% of the variance explained. CONCLUSION: For the conditions simulated in this study, external temperature had less of an influence on sweat onset times than cycling workload. Sweat onset can be predicted with 61.5% of the variance explained using only two input variables. Heart rate was a poor indicator of sweat onset and simply using power output would be a better starting point. Overall, workload proved to be the most influential variable for predicting sweat onset. This project was funded by the Natural Sciences and Engineering Research Council Engage Grant

Investigating cycling kinematics and braking maneuvers in the real world: e-bikes make cyclists move faster, brake harder, and experience new conflicts

Pedelecs (e-bikes), which facilitate higher speeds with less effort in comparison to traditional bicycles (t-bikes), have grown considerably in popularity in recent years. Despite the large expansion of this new transportation mode, little is known about the behavior of e-cyclists, or whether cycling an e-bike increases crash risk and the likelihood of conflicts with other road users, compared to cycling on t-bikes. In order to support the design of safety measures and to maximize the benefits of e-bike use, it is critical to investigate the real-world behavior of riders as a result of switching from t-bikes to e-bikes.Naturalistic studies provide an unequaled method for investigating rider cycling behavior and bicycle kinematics in the real world in which the cyclist regularly experiences traffic conflicts and may need to perform avoidance maneuvers, such as hard braking, to avoid crashing. In this paper we investigate cycling kinematics and braking events from naturalistic data to determine the extent to which cyclist behavior changes as a result of transferring from t-bikes to e-bikes, and whether such change influences cycling safety.Data from the BikeSAFE and E-bikeSAFE naturalistic studies were used in this investigation to evaluate possible changes in the behavior of six cyclists riding t-bikes in the first study and e-bikes in the second one. Individual cyclists’ kinematics were compared between bicycle types. In addition, a total of 5092 braking events were automatically extracted after identification of dynamic triggers. The 286 harshest braking events (136 cases for t-bike and 150 for e-bike) were then validated and coded via video inspection.Results revealed that each of the cyclists rode faster on the e-bike than on the t-bike, increasing his/her average speed by 2.9–5.0 km/h. Riding an e-bike also increased the probability to unexpectedly have to brake hard (odds ratio = 1.72). In addition, the risk of confronting abrupt braking and sharp deceleration were higher when riding an e-bike than when riding a t-bike.Our findings provide evidence that cyclists’ behavior and the way cyclists interact with other road users change when cyclists switch from t-bikes to e-bikes. Because of the higher velocity, when on e-bikes, cyclists appear to have harder time predicting movements within the traffic environment and, as a result, they need to brake abruptly more often to avoid collisions, compared with cycling on t-bikes. This study provides new insights into the potential impact on safety that a cycling society moving to e-bikes may have, indicating that e-cycling requires more reactive maneuvers than does cycling traditional bicycles and suggesting that any distractive activity may be more critical when riding e-bikes compared to traditional bikes.

From cars to bikes \u2013 the feasibility and effect of using e-bikes, longtail bikes and traditional bikes for transportation among parents of children attending kindergarten: design of a randomized cross-over trial

BackgroundThe present study aims to increase bicycling and level of physical activity (PA), and thereby promote health in parents of toddlers, by giving access to different bicycle types. There is a need for greater understanding of e-bikes and their role in the transportation network, and further effects on PA levels and health. Moreover, longtail bikes could meet certain practical needs not fulfilled by e-bikes or traditional bikes, hence increased knowledge regarding their feasibility should be obtained. No previous studies have investigated whether providing an e-bike or a longtail bike over an extended period in a sample of parents of toddlers influence objectively assessed amount of bicycling and total PA level, transportation habits, cardiorespiratory fitness, body composition and blood pressure.MethodsA randomized cross-over trial will be performed, entailing that participants in the intervention group (n = 18) complete the following intervention arms in random order: (i) three months access to an e-bicycle with trailer for child transportation (n = 6), (ii) three months access to a longtail bicycle (n = 6), and (iii) three months access to a regular bicycle with trailer (n = 6), in total nine months. Also, a control group (n = 18) maintaining usual transportation and PA habits will be included. A convenience sample consisting of 36 parents of toddlers residing in Kristiansand municipality, Southern Norway, will be recruited. Total amount of bicycling (distance and time), total level of PA, and transportation habits will be measured at baseline and in connection to each intervention arm. Cardiorespiratory fitness, body composition and blood pressure will be measured at baseline and post-intervention. Main outcome will be bicycling distance and time spent cycling.DiscussionNew knowledge relevant for the timely issues of public health and environmental sustainability will be provided among parents of toddlers, representing a target group of greatest importance. There is a call for research on the influence of e-bikes and longtail bikes on travel behavior and PA levels, and whether voluntary cycling could improve health. If the present study reveals promising results, it should be replicated in larger and more representative samples. Eventually, inclusion in national public health policies should be considered.Trial registrationID NCT03131518, made public 26.04.2017.

Alternative Methods of Measuring Operating Speed of Electric and Traditional Bikes in China-Implications for Travel Demand Models

Travel survey respondents often over- or under-estimate their travel time depending on a number of factors. This research presents 2 methods of measuring average speed and thus travel time. The traditional method, stated travel times from travel surveys are compared and corroborated against measured implied travel time based on a GPS-based floating vehicle study to identify the measurable operating speed. An example of this approach is shown for a survey of 2-wheelers (bicyclists and electric bike users) in 2 cities in China. In these cases, survey respondents report up to 32% higher travel times than what is measured on a floating vehicle study, depending on the case. The implication is that mode choice models calibrated on respondent stated travel time more than triples the effect of travel time compared with a model calibrated on average GPS speed (and inferred trip travel time).

Alternative Methods of Measuring Operating Speed of Electric and Traditional Bikes in China-Implications for Travel Demand Models

Alternative Methods of Measuring Operating Speed of Electric and Traditional Bikes in China-Implications for Travel Demand Models

Integration of electric pedal assisted bikes in a bike-sharing system

Abstract In the past years the urban mobility problems have increased; the vehicle sharing systems are one of the possible solutions to reduce the mobility problems. Accordingly to these needs, the bike-sharing systems are becoming very popular, mostly in the cities grown on a flat area. Even if there are a lot of systems using traditional bikes, there are no systems using electric bikes. This paper presents a system architecture which allows the integration of commercial pedal assisted bikes in a vehicle sharing system; its characteristics do not require the redesign of the vehicle. After presenting the system architecture, the paper describes the design of the device allowing the integration. Some hints about the use of the same approach to lightweight electric vehicles are provided in the end.