Search Results (10)

Cycling is widely recognized as a sustainable urban mobility solution, and many municipalities focus on cycling infrastructure expansion to promote improved environmental sustainability. However, the current literature on cycling has predominantly focused on safety and health benefits, while the environmental benefits including GHG mitigation benefits remain less explored. To summarize findings from the current literature that explore the GHG emissions-related benefits (or costs) of cycling infrastructure, we conducted a literature review using five major scientific databases, following the PRISMA guidelines. Out of 824 screened records, 17 studies met the inclusion criteria. Most studies were published in the last decade, reflecting a limited but growing interest in this topic. The current analytical approaches include mode shift analysis, life cycle assessment, and scenario modelling. Among these, mode shift analysis (i.e., assessing the potential benefits related to replacement of car trips with cycling) remains a commonly used method. We found that cycling offers significant operational benefits by reducing GHG emissions, especially in the context of large-scale expansions of cycling infrastructure. Existing research indicates that even when embodied emissions are considered, bicycle is a more sustainable mode of transportation compared to cars or even public transit. However, emissions associated with installation and maintenance of cycling infrastructure may sometimes negate the GHG benefits associated with additional cycling. We discussed gaps in the current literature and directions for future research. Full article

In road cycling, developing bike handling skills can prevent crashes and falls. Nevertheless, bike handling remains largely unexplored in the world of road cycling. The goal of this research was to develop a methodology to assess bike handling during races and training by estimating the rider–bicycle roll angle and road-plane accelerations from global positioning system (GPS) data only. A multi-dimensional bike-rider mathematical model was included in an optimal control framework to follow a reference trajectory generated from GPS data points. Estimated variables and experimental data collected with a cost-effective setup showed good agreement, i.e., root mean square error (RMSE) of 12° and 0.1 g for roll angle and both longitudinal and lateral accelerations, respectively, in the worst-case scenarios. This methodology might allow for the estimation of key bike handling variables during fast segments with cost-effective instrumentation. It can therefore constitute a tool for objectively assessing bike handling in road cycling training and racing. Full article

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s² to 4.5 m/s², the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles. Full article

Cycling can bring a wide range of social, economic, and health benefits to individuals and communities. The safety and efficiency of bicycle facilities can be significantly impacted by the interactions among riders. This study aims to examine the microscopic characteristics of how cyclists interact with each other when they are in a single file movement based on the trajectory data collected from an experiment. Reaction delay was obtained by optimizing the correlation between relative speed and acceleration curves for individual cyclists and it was found that even for a given cyclist, this characteristic time delay could vary considerably, and be situation-dependent. Furthermore, it was found that the distribution of reaction delay, which has an average (±SD) of 0.66 s (±0.33 s), followed a log-normal distribution. The strong correlation observed between relative speed and time-delayed acceleration resembles the behavior observed in car-following situations, highlighting that relative speed is an essential factor influencing the acceleration behavior of cyclists. Multiple linear regression models were used to understand the association between acceleration and other key microscopic variables, e.g., spacing and relative speed, which are commonly used in microscopic behavior models. While the spacing between cyclists was found to have a significant impact on acceleration behavior, its effect was not as significant as that of relative speed. The outcomes of this study provide valuable insights into the cyclists’ behavior and can aid in the development of microscopic simulation models. Full article

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain). Full article

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e., non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China. Full article

As bicycling on roadways can cause adverse health effects, there is an urgent need to understand how bicycle routes expose bicyclists to traffic emissions. Limited resources for monitoring reveal that bicycle travel patterns may constrain such understanding at the network level. This study examined the potential exposure of bicyclists to traffic-related air pollution in El Paso, Texas, using Strava Metro data that revealed bicycle patterns across the city networks. An initial spatial mapping analysis was conducted to explore the spatial patterns of bicycling and traffic pollutant emission, followed by exploratory descriptive statistics. A spatial bicycle model was then developed to explore factors influencing bicycling activity in El Paso. Analysis results indicated significant associations between greater bicycle volume and both higher levels of particulate matter (PM_2.5) emissions and more frequent bus services, implying adverse health concerns related to traffic-related air pollution. The results also indicated significant effects of various environmental characteristics (e.g., roadway, bicycle infrastructure, topography, and demographics) on bicycling. The findings encourage extending this study to provide guidance to bicyclists whose regular trips take place on heavily trafficked roads and during rush hours in this region and to evaluate the net health impacts of on-road bicycling for the general population. Full article

In recent years, the relationship between transportation and subjective well-being has been a major subject. Well-being is a factor that can affect travelers’ psychology and transport mode choice. For this reason, policymakers have attempted to improve travelers’ subjective well-being and promote sustainable modes of transport. For a better understanding of these factors, a questionnaire-based survey was conducted to identify the travel eudaimonia aspect of subjective well-being (comfort, safety, autonomy, self-confidence, physical, and mental health), for the various means of transport in the city of Thessaloniki. During the survey, 300 valid questionnaires were completed. The collection of the above data was followed by statistical analysis. The aim of the analysis was to identify the factors of travel eudaimonia that contributed to the mode choice. For that reason, four ordinal regression models were developed to determine how travel eudaimonia affected the usage frequency of the four available means of transport in the city of Thessaloniki (i.e., private car, bicycle, public transport, walking). Walking was rated higher than other modes in all factors, whilst cycling was rated high in physical and mental health, self-confidence, and autonomy, but low in comfort and safety. Public transport scored very low in all factors, demonstrating the poor quality of service provided by the city’s public transport. Moreover, from the ordinal regression models’ results, it could be demonstrated that travel eudaimonia factors had a significant role to play in mode choice. Recognizing the impact of these factors on transport mode choice is particularly useful for policymakers, researchers, and engineers, as it helps them to make informed decisions about what improvements are needed to promote sustainable modes of transport (mainly walking, cycling, and secondarily, public transport). Full article

Most cities in China benefit from having a commercial and public bicycle-sharing system. However, the bicycle-sharing markets still face unbalanced development problems, i.e., initial rapid expansion in most areas, and a recent disappearance in some local areas. Thus, the economic features and rules of this market need further exploration to introduce better management measures. Based on agent-based modeling, the current paper stimulated the interactions between supply and demand with two models to illuminate the supply characteristics of the bicycle-sharing market. The main findings included the following: (1) the bicycle-sharing market is governed by a set of objective laws which naturally require an oversupply, meaning that the attainment of a high level of user satisfaction depends on high supply; (2) based on each customer’s tolerance level, there is a supply density threshold that determines the existence and disappearance of the market; and (3) the width and elasticity of the supply density threshold are influenced by the tolerance of the customers, which, in turn, reflects their values and attitudes. The current research is a preliminary exploration of the interactive characteristics of supply and demand in the bicycle-sharing market. We believe that the current paper provides insights and implications to illuminate the law of existence in the bicycle-sharing market. It also includes a discussion on the sustainable development of the bicycle-sharing market in China. Full article

We conducted individual and ecologic analyses of prospectively collected data from 839 injured bicyclists who collided with motorized vehicles and presented to Bellevue Hospital, an urban Level-1 trauma center in New York City, from December 2008 to August 2014. Variables included demographics, scene information, rider behaviors, bicycle route availability, and whether the collision occurred before the road segment was converted to a bicycle route. We used negative binomial modeling to assess the risk of injury occurrence following bicycle path or lane implementation. We dichotomized U.S. National Trauma Data Bank Injury Severity Scores (ISS) into none/mild (0–8) versus moderate, severe, or critical (>8) and used adjusted multivariable logistic regression to model the association of ISS with collision proximity to sharrows (i.e., bicycle lanes designated for sharing with cars), painted bicycle lanes, or physically protected paths. Negative binomial modeling of monthly counts, while adjusting for pedestrian activity, revealed that physically protected paths were associated with 23% fewer injuries. Painted bicycle lanes reduced injury risk by nearly 90% (IDR 0.09, 95% CI 0.02–0.33). Holding all else equal, compared to no bicycle route, a bicycle injury nearby sharrows was nearly twice as likely to be moderate, severe, or critical (adjusted odds ratio 1.94; 95% confidence interval (CI) 0.91–4.15). Painted bicycle lanes and physically protected paths were 1.52 (95% CI 0.85–2.71) and 1.66 (95% CI 0.85–3.22) times as likely to be associated with more than mild injury respectively. Full article