Cyclist Behavior Research Articles

What is significant for engagement in cycling and walking in South Korea? Applying value-belief-norm theory

The value-belief-norm (VBN) theory has been widely used to describe pro-sustainability behavior. Despite being used to examine issues of sustainable and public transport behavior, research using the VBN has largely overlooked the pro-environmental behavior of cyclists and walkers, which is extremely significant given the role of active transport in environmental and human health. Therefore, this study creates and verifies a conceptually comprehensive active transport (cycling/walking) research model employing VBN theory, with a moderating role of cycling/walking and comparing cyclists and walkers, and utilizing PLS-SEM, multi-group analysis (MGA), necessary condition analysis (NCA), and fuzzy set Qualitative Comparative Analysis (fsQCA). Results revealed that value has a great effect on belief in walking/biking. However, belief has a stronger impact on personal norm than social norm, and also personal norm shows twice the impact on behavioral intention than social norm. Behavior to engage in cycling and walking is greatly influenced by behavioral intention. Walkers have a stronger relationship between belief and personal norm than cyclists. Cyclists have higher prediction power with social norm, while walkers have a higher prediction power with belief, personal norm, behavioral intention, and behavior. Importantly for sustainable transport planning and interventions, the cycling and walking groups distinctively different from each other in terms of solutions for outcome high level of active transport behavior.

Improving cycling safety through infrastructure design: A bicycle simulator study

This study investigated the impact of two road treatments on cyclist behaviour using an bicycle simulator. The treatments were sharrows (presence or absence) at a roundabout and widening an on-road bike lane (width 1.2 m or 1.8 m) on a curved mid-block section of the road. Outcomes collected included count (e.g. number of lane excursions) and continuous responses (lane position upon entry into the roundabout, in metres from the curb). A convenience sample of 21 Perth-based participants was recruited between 1 July and 30 September 2021. The results found that the presence of sharrows was not significantly associated with the distance from the left curb when entering a single lane roundabout. However, females rode 0.44 m further from the left curb when entering the roundabout than males, which was significant (95 % CI = 0.02–0.85; p = 0.04). Riders, aged 26–40 years, also rode 0.61 m further from the left curb when entering the roundabout than riders aged 18–25 years, which was also significant (95 % CI = 0.18–1.05; p = 0.01). The rate of bike lane excursions significantly decreased by 68 % for the wide bike lane on the curved mid-block section of the road compared to the standard width bike lane (IRR = 0.32; 95 % CI = 0.11–0.88; p = 0.03). Riders, aged 26–40 years, had a significant increased rate of 8.72 excursions than riders aged 18–25 years (IRR = 8.72; 95 % CI = 3.32–22.89; p < 0.001). This study highlights that increasing the bike lane width on curved sections of mid-block roads may benefit cyclists. However, the value of sharrows in a single lane roundabout is less clear.

Understanding the Relation of Psychological/Behavioral Factors and Cycling During the Covid-19 Pandemic: A Case Study in Iran

Previous research on travel behavior has concentrated on the behavior of traveling by cars, especially by private vehicles, while the research on cycling has focused on cycling infrastructure, the built environment, and the natural environment. Furthermore, the studies conducted during pandemics are mostly based on behavioral changes in motorized transportation. The present research tries to identify and evaluate the variables influencing cyclist behavior during covid-19 pandemic. In this research, the sample size retrieved from a survey of 375 participants was checked with Cronbach's alpha standard and estimated using confirmatory factor analysis. Results show that the variables related to health protocols can greatly impact knowing the behavior of cyclists in the time of Covid-19. Furthermore, the results show that the health issues of shared bikes can be an obstacle for people to use them more.

The Influence of Gender on Food Consumption Patterns Among National-Level Adolescent Cyclists.

Background and objective Adolescents in general make poor food choices due to a lack of awareness, social pressure, and other factors, leading to a faulty lifestyle. On the other hand, the adolescent athletic population is associated with a healthy eating pattern. In light of this, this study aimed to evaluate the eating behavior of adolescent cyclists competing at the national level. Methods A total of 50 national-level adolescent cyclists (26 males and 24 females) were assessed for eating behavior, daily food consumption patterns, and eating habits around exercise time by using a pre-tested validated questionnaire, Food Frequency Questionnaire (FFQ), and 24-hour Food Recall. Results The majority (82%) of the cyclists were non-vegetarians, followed by lacto-vegetarians (14%) and lacto-ovovegetarians (14%). Of note, 72% of the cyclists consumed four meals consisting of breakfast, lunch, snacks, and dinner daily, while 28% skipped at least one of the meals. The preference for takeaways (52%) surpassed dine-outs (34%) and home-cooked (14%) food. Pre-training snack was consumed by 37% and post-training snack by 47%. Cyclists daily consumed breakfast cereals (76%), bread (94%), pulses (92%), fruits (100%), vegetables (62%), milk (84%), milk products (90%), egg (82%), poultry, fish, and meat (74%), dry fruits, nuts, and seeds (78%), and saturated fats (100%). Junk foods (94%) and sweetened beverages (70%) were consumed at least once a week. No significant difference was observed in eating behavior and daily food consumption pattern between male and female cyclists. Conclusion The eating behavior of adolescent cyclists was inclined towards the consumption of saturated fats, junk food, and sweetened beverages. Male and female athletes have similar food habits. There is a need for the implementation of behavior change-oriented nutrition strategies to inculcate healthy eating habits among adolescent cyclists.

Evaluation of Cycling Safety and Comfort in Bad Weather and Surface Conditions Using an Instrumented Bicycle

Understanding how vulnerable road users (cyclists and pedestrians) behave enables the construction of better roadways with adapted geometric and surface design which leads to improve cycling safety and comfort. This study examines the behavior of cyclists using an instrumented city bicycle that allows collecting exact data about bicycle dynamics, trajectory, and speed, as well as essential information to study the behavior of the cyclists, their reaction to the different features of the road surface and geometric design, and their interaction with other road users such as pedestrians, vehicles and other cyclists. 22 cyclists participated in an experiment following a predetermined route in Stockholm, Sweden. The route consisted of a circuit with different types of cycling facilities in order to study the different interactions (cyclist-car and cyclist-pedestrian), the circuit was divided into 3 zones: the first is mixed traffic, the second is a separate cycling lane and the third is shared pedestrian-cycling path. The results show significant data to evaluate cycling safety and comfort in snowy weather conditions and the perception-reaction behavior of cyclists; accordingly, the infrastructure-related risks were evaluated from subjective and objective points of view. In this paper, we propose a new concept to evaluate cycling behavior. This concept allows us to evaluate cyclists’ behavior through the calculation of Behavioral Risk Indicator (BRI) based on different risk factors owing to weather, road and traffic conditions, interaction with other road users and reaction to infrastructure drawbacks. The applications of the proposed concept allow us to evaluate the risks caused by multiple traffic factors and infrastructural drawbacks and study cyclist–bicycle–road interactions and their influences on cycling safety. In addition, the concept provides a new foundation for establishing cycling safety measures that could be applied to improve the infrastructure and reduce traffic accidents in order to attract more people to ride bicycles.

Behavior and Psychological Analysis of Electric Bike Rider Going Straight to Cross the Street at Signal Intersections

As an important part of China’s transportation means, electric vehicles have an important impact on China’s traffic environment and traffic safety, and the straight violation behavior of electric vehicles is widespread, which has become one of the main causes of traffic accidents. This paper uses the video method to observe the behavior of electric cyclists crossing the street and study the characteristics, and analyzes the behavior of running a red light in the process of going straight across the street. Using questionnaire survey to collect cyclist’s psychological data, understand the different rider in the street psychological state, the classification of various psychological activities, analysis of different psychological activities on the influence of the rider behavior choice, establish behavior and psychological contact, find measures to improve traffic safety, and help for reasonable standardization management.

Analysis of stopping behaviour of cyclists at a traffic light-controlled intersection using trajectory data

Analysis of stopping behaviour of cyclists at a traffic light-controlled intersection using trajectory data

Near misses and split routes: Comparing rider behavior, driver interaction, and route choice for cyclists

The built environment, cyclist behavior, and driver interactions can influence route choice and, ultimately, cyclist safety. Recent studies use crowdsourced data, such as Strava, to document route selection; however, aggregated data may not fully explain the factors underpinning route selection. Utilizing naturalistic methods, we analyze videos of recorded rides and GPS data from six cyclists representing three types of riders—commuters, recreational, and athletes—to explore route choices, behavior, and driver interactions in Tucson, Arizona. Our analysis of three route selection cases highlights how intersection design, driver interactions, pavement conditions, and type of riding lead cyclists to modify behaviors and select longer detours to avoid unsafe intersections. Additionally, our study combines Strava heatmaps and physical bicycle counts to explore the number of cyclists potentially facing similar factors influencing route choice. By studying cyclists with different riding aims and utilizing both Strava heatmaps and video recordings, researchers can determine the underlying conditions, identify route locations in need of improvements, and collaborate with practitioners to implement changes to increase cyclist safety through appropriate solutions. This analysis can help ensure that designs meet user expectations.

The Impact of COVID-19 on Motivation, Involvement, and Behavior of Cyclists in Taiwan.

Coronavirus disease (COVID-19) has spread all over the world and has impacted tourism globally, with countries taking various measures such as travel restrictions, border closures, lockdowns, or quarantines to contain the virus. Tourists' motivation has also been affected by COVID-19, but so far, the literature has not yet discussed their concern over COVID-19 as well as the relationships among their motivation, involvement, and behavior intention. Therefore, this study fills the gap in the literature by taking cycling tourism as an example to understand the involvement of tourists concerning COVID-19 and presents the depth and breadth of its effects upon tourism. Due to the challenge of face-to-face, on-site investigation, we employ an online survey for data collection, use exploratory factor analysis to extract the main factors of motivation, involvement, and behavior intention, and set up a structural equation model to examine the relationships among the three factors. The results show that COVID-19 has positively and significantly affected motivation and involvement. Motivation positively and significantly affects involvement, and involvement affects motivation and behavior intention. The main finding herein is that motivation does not affect behavior, but involvement does mediate between the motivation and behavior of cyclists during COVID-19. Therefore, people may perceive the risk of health and wellbeing through such involvement.

Cross-culturally approaching the cycling behaviour questionnaire (CBQ): Evidence from 19 countries

Given different advances in applied literature, risky and positive behaviours keep gaining ground as key contributors for riding safety outcomes. In this regard, the Cycling Behaviour Questionnaire (CBQ) represents one of the tools available to assess the core dimensions of cycling behaviour and their relationship with road safety outcomes from a behavioural perspective. Nevertheless, it has never been psychometrically approached through a cross-cultural perspective. Therefore, this study aimed to perform the cross-cultural validation of the CBQ, examining its psychometric properties, reliability indexes, validity insights and descriptive scores in 19 countries distributed across five regions: Europe, America, Asia, Africa, and Oceania. For this purpose, it was used the data retrieved from a full sample of 7,001 urban cyclists responding to a large-scale electronic survey. Participants had a mean age of M = 36.15 (SD = 14.71), ranging between 16 and 83 years. The results of this large-scale study empirically support the assumption that the 29-item version of the CBQ has a fair dimensional structure and item composition, good internal consistency, reliability indexes, and an interesting set of validity insights. Among these results, there can be highlighted that: (i) Structurally speaking, the questionnaire works better under a three-factor dimensionality, keeping all its 29 items, whose factor loadings are >0.400 in all cases; (ii) The CBQ shows greater reliability indexes than in previous applications using smaller samples, with good Cronbach’s alphas [0.768 - 0.915], McDonald’s omegas [0.770 - 0.913] and Composite Reliability Indexes [981 - 0.994]; and (iii) Robust tests comparing riding behaviours of riders with different levels of risk perception and crash involvement support the concurrent validity of the Cycling Behaviour Questionnaire. These outcomes endorse the usefulness of the CBQ to assess both risky and positive riding behaviours of cyclists in different countries, contributing to assess and improve cycling safety from the human factors approach.

The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections

Introduction: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light running behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. Method: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. Results: The results indicate higher reductions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersections without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). Conclusions: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light violations in all three groups. Practical Applications: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.

Do People Desire to Cycle More During the COVID-19 Pandemic? Investigating the Role of Behavioural Characteristics through a Structural Model

Background: Most cycling behaviour studies have defined it using objective variables and focused on normal conditions. Objective: This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19. Methods: We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed. Results: Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour. Conclusion: Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper.

A New Method for the Quality of Service Assessment of Highly Frequented Bicycle Facilities in Germany

Owing to increasing bicycle traffic volumes in Germany, more and more bicycle facilities are being designed with widths beyond the standard values given in the existing design guidelines. However, the level of service assessment method for bicycle facilities in the current edition of the German Highway Capacity Manual, HBS, is based on empirical data from facilities with mostly low traffic demand and small widths. Therefore, a new quality of service assessment method for highly frequented bicycle facilities was developed. For this, the cycling behavior of cyclists at high traffic volumes was analyzed, considering different bicycle types. Bicycle traffic flow quality, which is mainly characterized by the ability for cyclists to pass, was examined on segments of bicycle facilities with different geometric parameters, including facility type, gradient, and width. The speed and lateral distance behavior of cyclists were analyzed by using radar and video measurements. It was found that a large variance in the bicycle speed distribution mainly resulted from an inhomogeneous composition of bicycle types. The bicycle facilities were modeled with the microscopic traffic simulation tool PTV Vissim and calibrated using the empirical data. The models were used to analyze the impact of different geometric parameters and high proportions of specific bicycle types. As expected, the simulations revealed that the quality of service was mainly influenced by the lane width. Based on the results, a method to assess the quality of service of bicycle facilities, in which traffic density is used as the measure of effectiveness, was developed.

Do People Desire to Cycle More During the COVID-19 Pandemic? Investigating the Role of Behavioural Characteristics through a Structural Model

Background: Most cycling behaviour studies have defined it using objective variables and focused on normal conditions. Objective: This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19. Methods: We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed. Results: Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour. Conclusion: Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper.

Application of EMGB to Study Impacts of Public Green Space on Active Transport Behavior: Evidence from South Korea.

Public green spaces (e.g., parks, green trails, greenways) and motivations to engage in active transport are essential for encouraging walking and cycling. However, how these key factors influence walker and cyclist behavior is potentially being increasingly influenced by the use of smart apps, as they become more ubiquitous in everyday practices. To fill this research gap, this work creates and tests a theoretically integrated study framework grounded in an extended model of goal-directed behavior, including public green space and motivation with perceived usefulness of smart apps. In order to accomplish the purpose of this study, we conducted an online survey of Korean walkers (n = 325) and cyclists (n = 326) between 10 and 25 July 2021 and applied partial least squares, structural equation, and multi-group analysis to validate the research model. Results revealed that active transport users’ awareness of public green space positively influences attitude toward (γ = 0.163), as well as behavioral intention of (γ = 0.159), walking and cycling. Additionally, motivation (extrinsic and intrinsic) greatly influences attitude (γ = 0.539) and behavioral intention (γ = 0.535). Subjective norms (γ = 0.137) and positive (γ = 0.466) and negative anticipated emotions (γ = 0.225) have a significant impact on the desire that leads to behavioral intention. High and low perceived smart app usefulness also significantly moderates between public green space and attitude (t-value = 25.705), public green space and behavioral intention (t-value = 25.726), motivation and attitude (t-value = −25.561), and motivation and behavioral intention (t-value = −15.812). Consequently, the findings are useful to academics and practitioners by providing new knowledge and insights.

Modeling cyclist behavior using entropy and GPS data

This paper analyzes the relation between Shannon’s information entropy and trips made by cyclists in an urban context. Such entropy indicator depends on people distribution across a territory in a specific time. Its evaluation and temporal evolution have multiple applications in transport planning, from the specification of demand distribution models to the calibration and validation of transport demand models to be used where transport data are scarce. This feature is crucial in transport planning, as the lack of robust data to estimate travel demand is still a common challenge, especially for cycling mobility. The entropy indicator has been applied to the cyclists’ case in the Italian city of Bologna, using approximately 165,000 GPS traces recorded from April to September 2017. The results are encouraging because the entropy indicator is able to explain the evolution of the agents’ distribution throughout a day across the territory. However, it is suggested that future research may further deepen the concept to test its transferability, and to better establish its sensibility to other case studies.

Extracting Overtaking Segments by Unsupervised Clustering and Predicting Nonmotorized Vehicle’s Trajectory

Interpretation of flexible cycling behavior has always been a tough task. It is meaningful to understand the overtaking behavior of cyclists for its threats to safety and its high frequency on shared roads. Advanced unsupervised nonparametric clustering methods are compared to distinguish the overtaking segments from the whole trajectory based on the cycling characteristics of nonmotorized two-wheelers, while the hierarchical Dirichlet process hidden Markov model (HDPHMM) outperforms the mixture model via the Dirichlet process (DP mixture model) and topic model via the hierarchical Dirichlet process (HDP topic model). HDPHMM clusters each record into different states and results in more continuous segments. Based on marked vehicle types, which state of clustering model represents the overtaking condition is deduced. The overtaking segments resulted from HDPHMM show the highest homogeneity in cycling features with actual overtaking behavior. Another practical task is to predict the overtaking trajectory and respond to overtaking behavior in advance. Comparing original trajectory and subdivided trajectory, it is found that training model with grouped data, which have homogeneous features, can improve prediction accuracy. With enough trainable samples, CNN + LSTM hybrid structure can achieve trajectory prediction with a mean absolute error of 3 cm. The segmentation produces trajectory segments with similar characteristics. The model is trained with overtaking trajectory segments. With tens of times less trainable data, the prediction on overtaking trajectory still keeps a mean absolute error of about 5 cm. Subdividing trajectory into segments with homogeneous features can improve the prediction accuracy and reduce the requirement of trainable data volume.

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network.

Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists' route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists' preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

Analysing the influence of a farmers’ market on spatial behaviour in shared spaces

ABSTRACT Temporary street activities influence pedestrian movement in public spaces, yet there is no evidence of their influence in shared space environments. Therefore, this paper presents a comparative analysis of the spatial behaviour of pedestrians, cyclists, and motor vehicles in two different shared space schemes in Germany. Their trajectories were extracted from video recordings to evaluate the use of space and speed. The results indicate that a farmers’ market could be considered an influential factor in extending pedestrian movement in the circulation zone and potentially increasing their dominance in the shared space while reducing the speed of all road users.

Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations

Cyclists occupying motorized vehicle lanes disrupt road traffic order and increase collisions. Exploring the contributing factors could help develop countermeasures to regulate such behaviors. The purpose of this study is to explore the intrinsic features influencing the behavior of cyclists in occupying motorized vehicle lanes at different bicycle facilities. We investigated a total of 34,631 cycling behavior samples in the urban area of Pingdingshan, China. A Bayesian random parameter logit model was used to account for the unobserved heterogeneous effects. The experimental results of all bike facilities demonstrate that the bike type, dividing strip type, bike lane width, temporary on-street parking, and whether it is a working day significantly affect cyclists’ occupying motorized vehicle lane behaviors. Factors associated with unobserved heterogeneity are age, barriers dividing strip, vehicle lane numbers, bike volume, vehicle volume, and daily recording time intervals. Comparing the estimated model of five type bike lane facilities across different dividing strips, we find that cyclists have a significantly different occupying probability and the heterogeneity factors of the various bike facilities also have their focus. When the non-motorized road conditions become more open, the cyclist behavior becomes more random and the heterogeneity factors become broader.