Bicyclist Behavior Research Articles

Pedestrian and bicyclist behavior at Highway-Rail grade crossings: An observational study of factors associated with violations, distraction, and crossing speeds during train crossing events

Pedestrian-train collisions at highway-railroad grade crossings (HRGCs), though relatively rare events, often result in severe injuries or fatalities. In the ten year period from 2008-2017, there were 1,470 pedestrian-train collisions reported in the United States resulting in 908 fatalities and 492 injuries according to the Federal Railroad Administration. Furthermore, pedestrians in over 27% of these crashes were coded as disregarding gates at HRGCs, indicating unsafe behavior at these crossings may lead to crashes. In order to examine this behavior (for both pedestrians and bicyclists) and add to the body of knowledge for this topic, this observational study utilized pedestrian and bicyclist behavior data extracted from videos of HRGCs. Several pedestrian and bicyclist behavior characteristics were extracted including the prevalence of pedestrian and bicyclist violation (e.g., disregarding active devices), pedestrian and bicyclist distraction, and pedestrian and bicyclist crossing speeds. Using these data, along with rail-grade crossing features, train crossing parameters, and demographic data, statistical analyses were performed to examine factors significantly associated with pedestrian and bicyclist violations, distraction, and crossing speeds at HRGCs. Ultimately, the results of this study provide important information which may be useful in planning strategies to mitigate the impacts of unsafe pedestrian or bicyclist behavior at HRGCs.

Accounting for drivers’ bicycling frequency and familiarity with bicycle infrastructure treatments when evaluating safety

Municipalities in the United States often encourage bicycling for the health, economic, and environmental benefits by implementing new and innovative bicycle infrastructure treatments. Unfortunately, many treatments are unfamiliar to or misunderstood by drivers, especially when lacking explicit rules (e.g., shared lanes). To date, research has largely investigated bicycle infrastructure from a bicyclist's perspective, but with little research from the driver's perspective. The objective of this research is to utilize a driving simulator to investigate driver behavior towards different bicycle infrastructure treatments when driver behavior is not provoked by an interaction with bicyclists. More specifically, this research intends to investigate the impact of bicycling frequency and treatment familiarity, as well as the combined effect of the two, on driver behavior at each treatment type. The treatments investigated are shared lane markings called “sharrows”, standard bike lanes, bike boxes, and merge lanes. The results show that bicycling frequency significantly affects the proportion of drivers making eye glances at treatments. In addition, drivers more familiar with bike boxes stopped significantly further back from bike boxes, and drivers more familiar with merge lanes performed the merge maneuver significantly earlier. Furthermore, driver speed and lane positioning at bike lanes was significantly affected by the combination of bike lane familiarity and bicycling frequency, but not individually. This research is a first step towards understanding driver behavior and expectation of bicyclists; an essential understanding for infrastructure treatments that do not provide physical barriers between bicycles and automobiles, and instead rely on driver behavior for safety.

Modeling the illegal lane-changing behavior of bicycles on road segments: Considering lane-changing categories and bicycle heterogeneity

Road segments using traffic markings to separate the vehicle lane and the adjacent bicycle lane are widely adopted on urban roads. Bicycles’ illegal lane-changing behavior (ILC) is commonly observed on such road segments, resulting in severe influences of traffic efficiency and bringing hidden dangers. However, existing models disregard different categories of ILC and heterogeneities of ILC between electric bikes (e-bikes) and regular bikes (r-bikes), which has limitations in representing the realistic ILC. To address these disadvantages and understand how heterogeneities affect the ILC, this paper analyzes the behavioral characteristics of ILC using field data and proposes a new cellular automaton (CA) model. Corresponding rules are set for different categories of ILC in the model, and simultaneously the bicycle heterogeneities of ILC are also considered in rules-making. Simulation results indicate that as the proportion of e-bikes increases, the frequency of ILC increases. Furthermore, vehicle volume decreases due to the occurrence of ILC, and the effect is more evident with a higher proportion of e-bikes. Findings of the study can help us better understand the ILC, and the proposed simulation model is useful for pre-evaluating unimplemented designs for the mixed traffic road segment.

Biking islands in cities: An analysis combining bike trajectory and percolation theory

Bike trajectories generated by the dockless bike-sharing service provides a great opportunity to explore users' travel behavior within the shared mobility transportation ecosystem. This paper proposes a new concept, namely biking islands, defined as geographical areas of interest with a high concentration of bike usage. Leveraging high-resolution trajectory data, biking islands are identified via percolation theory because of its suitability in describing the formation of clusters and critical road segments that have significant influence on biking behavior in an urban context. We showcase our methodology using the bike trajectory dataset provided by a market-leading company and use Shanghai, China as the study area. Results reveal a hierarchical structure of biking islands. With the increase of threshold, the biking islands start to shrink and split into various smaller ones. Larger biking islands are usually located in the central urban area of Shanghai and the Huangpu River acts as a natural barrier that impedes biking continuity across the region. Besides, the formation of biking islands is highly influenced by the surrounding land uses. The proposed concept and methods are not only helpful to understand travel behavior of cyclists and urban structures used for cycling, but also has the potential to support relevant urban and transportation planning, such as identifying designated non-motorized areas for cycling and biking facilities and pinpointing critical road segments that can improve the cycling efficiency of the entire network. Biking islands could be designed as designated areas for cycling where sufficient bike facilities are provided, and/or motorized transport modes are restricted or even prohibited, so as to ensure the convenience and safety of cyclists and support the development of bike-friendly cities.

Impact of the Built Environment and Bicycling Psychological Factors on the Acceptable Bicycling Distance of Rural Residents

The ability to understand bicycling behavior in China’s rural areas is critical in constructing an improved, sustainable, countryside amid the rapid urbanization in the country. This study analyzes the influence of individual bicycling psychology, objective, and perceived built environment on the acceptable bicycling distance of rural residents. This research is conducted by controlling for the socio-demographic characteristics of the residents on the bases of a face-to-face questionnaire survey and an on-site measurement. Exploratory factor analysis shows three attitudinal common factors on bicycling infrastructure, namely, bicycling ancillary facilities, bicycle lane conditions, and safety, and two bicycling motivation factors, namely, convenience and other motivations. Multiple linear regression was estimated and results of the models were consistent. Individual bicycling psychology and built environment factors significantly influence the acceptable bicycling distance of rural residents. The socio-demographic variables insignificantly influence the acceptable bicycling distance, which is inconsistent with the existing literature. The research results provide a broad empirical base for the complex relationships among individual bicycling psychological factors, objective and perceived built environment, and bicycling behavior. This study presents the first research on bicycling in Chinese rural areas and provides guidance for the development of effective countermeasures in constructing ecovillages.

Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone

Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone

Risk Riding Behaviors of Urban E-Bikes: A Literature Review.

In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Evaluating the Safety and Behavioral Impacts of Green Bike Lanes in Suburban Communities

A study of the effects on bicyclist and driver behavior when painting a bike lane green was conducted using video footage along East Thach Avenue in Auburn, Alabama. Whereas most previous studies on the implementation of green bike lanes have focused on primarily urban areas, this study aims to investigate the implications in a suburban community in eastern Alabama. Before and after data on vehicle speed, vehicle lateral location, and bicyclist lateral location were collected and two-sample t-tests were used to analyze the differences between each set of data. Analysis showed that with the addition of green paint to existing bike lanes, vehicle speeds dropped statistically significantly and motorists who were previously giving small amounts of passing space moved further away when passing a bicyclist. In addition, motorists who were previously driving in or near the bike lane when bicyclists were not present moved further toward the center of their lane. Together, these results indicate that the introduction of green paint to existing bike lanes in a suburban area has positive impacts on bicyclist–motorist interactions.

Neighborhood Social Disadvantage and Bicycling Behavior: A Big Data-Spatial Approach Based on Social Indicators

Characterizing the travel behavior of disadvantaged neighborhoods is of significance for social inequality elimination and public well-being promotion. However, rather few studies have empirically been conducted in respect of bicycling, especially in developing countries. Using the case of Shenzhen in China, this paper explores bicycling behavior associated with neighborhood social disadvantage (NSD). Data of 3 explained variables (bicycling frequency, travel duration and trip distance) for bicycling behavior is crawled from mobile application programming interface. Based on a set of 16 potential social indicators, we apply principal component analysis to construct NSD index and it produces 5 sub-indices including income, housing, occupation, education and population. Results of spatial regression demonstrate that NSD generally is positive with bicycling frequency and trip distance, while negative with travel duration. Specifically, income and population disadvantaged neighborhoods tend to bicycling for higher frequency, less time and longer distance. Housing disadvantage and education disadvantage reduce the likelihood of bicycling, while occupation disadvantage encourages bicycling frequency and trip distance. The variance decomposition shows that income disadvantage contributes most for bicycling frequency, housing disadvantage is more important for travel duration. For trip distance, education disadvantage is more influential. Our attempt provides an innovative insight into social indicators research.

Binomial and multinomial regression models for predicting the tactical choices of bicyclists at signalised intersections

Bicyclists are extremely flexible road users who employ various tactical behaviours to optimise comfort, directness and time efficiency while crossing a signalised intersection. Tactical choices faced by bicyclists at signalised intersections include whether to use the bicycle lane, roadway or sidewalk, to stop at or violate a red traffic signal, to ride with or against the mandatory direction of travel and the method of executing a left turn. The outcome of these choices has a direct impact on traffic safety and efficiency at intersections. In this paper, revealed choice data from 4710 bicyclists at four intersections in Munich, Germany are used to estimate binomial and multinomial logistic regression models to predict tactical choice outcomes. Optimal predictor sets are selected from the main and two-way interaction effects of 43 independent variables describing the situation, strategic behaviour and prior tactical choices of bicyclists using recursive feature elimination. A simplified model is estimated using the statistically significant variables of the optimal predictor set. The prediction power of the resulting regression model is assessed using k-fold cross validation. The models to predict response to a red signal and the type of left-hand turn exhibit high predictive power while the prediction of infrastructure selection and the direction of travel proves to be difficult.

Daily travel behavior and emotional well-being: Effects of trip mode, duration, purpose, and companionship

Positive emotions have long-lasting benefits for human development. Understanding the connections between daily travel behavior and emotional well-being will not only help transportation practitioners identify concrete strategies to improve user experiences of transportation services, but also help health practitioners to identify innovative solutions for improving public health. Prior research on the subject had focused on limited travel behavior dimensions such as travel mode and/or travel duration. Other dimensions such as travel purpose and travel companionship have received limited attention. Using data from the 2012–2013 American Time Use Survey, this paper applied the generalized ordered logistic regression approach and examined how the mode, duration, purpose, and companionship characteristics of a trip shape six different emotions during the trip, including happy, meaningful, tired, stressful, sad, and pain. After controlling for personal demographics, health conditions, and residential locations, we find that biking is the happiest mode; public transit is the least happy and least meaningful; and utilitarian walking for transportation is associated with all four negative emotions. Trip duration has a negative association with happiness and a positive association with stress. Travel for discretionary purposes such as leisure, exercise, and community activities is generally associated with higher levels of positive emotions and lower levels of negative emotions than travel for work or household maintenance. Trips with eating and drinking purposes appear to be the happiest and trips with the purpose of spiritual and/or volunteering activities appear to be the most meaningful. Travel with family especially children or travel with friends is happier and more meaningful than travel alone. Transportation planners in the U.S. are recommended to promote biking behavior, improve transit user experiences, and implement spatial planning strategies for creating a built environment conducive to shorter trips, more discretionary trips, and more joint trips with family and friends.

Electric Bicycle Lane-Changing Behavior under Strategy Games

The random lane-changing behavior of electric bicycles brings a great safety hazard to urban transportation: According to the national traffic accident statistics in 2015, 8.22% of traffic accidents are caused by non-motor vehicle violations. Among them, the accident rate caused by electric bicycles is as high as 78%. In order to effectively analyze and quantify the risk of this lane-changing behavior, based on the non-cooperative strategy model under complete information, a Z-test of the speed before and after the “illegal” lane-changing behavior was carried out by using the data of the mixed-flow sections of Xi’an city. Constructing the judgment matrix to determine the weight value of motor vehicles’ game strategy and constructing the charge matrix, the utility income analysis of the electric bicycle and the affected motor vehicle is made by using the payment function, and the exceeding conflict model is established to quantify the risk level of the conflict number in the unit road. It is verified that the “illegal” lane-changing behavior of electric bicycles is a goal-oriented behavior for obtaining greater desired speed.

Impacts of group behavior on bicycle flow at a signalized intersection

Bicycle’s riders widely have group behaviors due to the bicycle’s features and the group behaviors may have significant impacts on bicycle flow (especially at a signalized intersection). In this paper, we propose a cellular automaton (CA) model to explore the impacts of shoulder group behavior and following group behavior of bicycles during the process that bicycles pass through a signalized intersection. In the proposed model, the acceleration/waiting phenomenon (caused by the two group behaviors at the intersection) is explicitly considered. The numerical results indicate that the proposed model can effectively reproduce the actions of group bicycles at the intersection and that the two group behaviors have prominent negative impacts on the operational efficiency of the intersection, which shows that the group behaviors should be controlled at the intersection.

Comparing spatial patterns of crowdsourced and conventional bicycling datasets

Conventional bicycling data have critical limitations related to spatial and temporal scale when analyzing bicycling as a transport mode. Novel crowdsourced data from smartphone apps have the potential to overcome those limitations by providing more detailed data. Questions remain, however, about whether crowdsourced data are representative of general bicycling behavior rather than just those cyclists who use the apps. This paper aims to explore the gap in understanding of how conventional and crowdsourced data correspond in representing bicycle ridership. Specifically, we use local indicators of spatial association to generate locations of similarity and dissimilarity based on the difference in ridership proportions between a conventional manual count and crowdsourced data from the Strava app in the Greater Sydney Australia region. Results identify where the data correspond and where they differ significantly, which has implications for using crowdsourced data in planning and infrastructure decisions. Fourteen count locations had significant low-low spatial association; similarity was found more often in areas with lower population density, greater social disadvantage, and lower ridership overall. Five locations had high-high spatial association, or were locations of dissimilar rank values indicating that they did not have a strong spatial match. Higher coefficients of variation were associated with population density, the number of bicycle journey to work trips, and percentage of residential land use for the significant locations of dissimilarity. IRSD and bicycle infrastructure density were lower than the locations that were not significantly dissimilar. For the significant locations of similarity, all coefficient of variation measures were lower than the locations that were not significant. Areas where ridership show locations of similarity are those where it may be suitable to substitute conventional data for the more detailed crowdsourced data, given further investigation into potential bias related to rider demographics.

Design and Construction of a Virtual Bicycle Simulator for Evaluating Sustainable Facilities Design

The rising interest in sustainable modes of transportation has increased demand for the design and implementation of bicycle facilities in the United States. However, as compared to the vehicular mode, bicycle facilities have relatively less development, research, and understanding. The availability of a bicycling simulator has the potential to contribute to the understanding of bicycle facility design and bicyclist behavior. The design and construction of a bicycling simulator differs from a driving simulator in many ways. A bicycling simulator requires interfaces for bicycle speed, braking, and steering angle as well as a visual interface. In addition, a representation of a real-world network, including pavement, buildings, the sky and background, and fixed and moving objects, needs to be modeled using a simulator engine. This paper presents the details of the ZouSim bicycling simulator development and the tradeoffs associated with various design decisions, such as the choice of a steering sensor and graphical display. A sample application of a wayfinding and detection markings study illustrates the use of ZouSim. The authors hope that this article will encourage other researchers who conduct research in sustainable cities to explore the use of bicycle simulators for improving bicycle facility design and operations.

Exploring Factors Which Influence Travel Behavior of Bicyclists- A Case Study in Kumamoto, Japan

Exploring Factors Which Influence Travel Behavior of Bicyclists- A Case Study in Kumamoto, Japan

Analysis of Effects by Bicycle Lanes at Residential Streets from Viewpoints of Bicycle Behavior at Junctions

Analysis of Effects by Bicycle Lanes at Residential Streets from Viewpoints of Bicycle Behavior at Junctions

VOLUNTARY RISK TAKING BY YOUNG BICYCLISTS: A CASE STUDY OF UNIVERSITY STUDENTS AT MONTREAL

Young bicyclists in Montreal are taking voluntary risks such as bicycling without wearing helmet and braking system of bicycle although they are aware of the presence of danger. This article analyses the behaviour of young bicyclists taking voluntary risks. The university students in Montreal are considered as the case study since they are more risk takers and the bicycle is a favorite mode of transport among them. This study reveals that half of the respondents did not use helmets. They were also spontaneously taking a risk by high speeding, violating signals, bicycling in mixed traffic, ignoring protective equipment after dark, and avoiding the bicycle designated roads. They were taking voluntary risks based on their attitude, subjective norm and perceived behavioural control. They did not perceive the severity of risks since they experienced prelevant and unremarkable minor injuries. The attractiveness of risk and accomplishing the risk activities encouraged the young bicyclists to take voluntary risks repeatedly. Increasing feelings of vulnerability among young bicyclists reduce voluntary risks taking attitude and physical and psychological sufferings of the victims of bicycle-related accidents. Findings of this study suggest that the City of Montreal as well as other cities consider the behaviour of bicyclists particularly the young people to avoid bicycle-related accidents along with other physical measures.

Cyclist Social Force Model at Unsignalized Intersections With Heterogeneous Traffic

Cycling is a typical green traffic mode, and takes a growing part of urban traffic volume. Yet limited cyclist behavior models shed light on cases at unsignalized intersections with heterogeneous traffic, where bicycle behavior is characterized by frequent confrontations with other road users (vehicles, bicycles, and pedestrians). This study developed a microscopic simulation model for cyclist behavior analysis at unsignalized intersection with heterogeneous traffic. The cyclist crossing model applied fuzzy logic and social force theory for this purpose. The parameters are either estimated directly based on empirical data or derived indirectly through maximum likelihood estimation. Finally model performance was confirmed through comparisons between estimations and observations on individual trajectory, minimum distances, and average riding speeds of collision avoidance behaviors with different conflicting road users. Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.

Vehicle–Bicyclist Dynamic Position Extracted From Naturalistic Driving Videos

Pre-collision systems (PCS) are designed to warn a driver or brake automatically to help avoid or mitigate accidents, protecting vulnerable road users such as pedestrians and bicyclists. A PCS system needs to understand both normal and abnormal behavior of the vulnerable users in order to have high true-positive and low false-positive detection of PCS events. In this paper, the normal bicyclist behavior is analyzed based on naturalistic driving data. These data can inform the PCS design and lead to proper testing and evaluation of PCS systems. This paper focuses on the following attributes of normal bicyclist behavior: time to collision, lateral position, and bicycle speed. The behavior was analyzed using the Transportation Active Safety Institute naturalistic driving data from 110 cars. This paper presents new analysis gleaned from these data, plotted in 2-D graphical form, showing heat maps of common bicycle positions and trajectories of the dynamic vehicle–bicycle interactions.