Rider Behavior Research Articles

Identifying the role of route choice behavior of motorcycle riders in microscopic simulation modeling under mixed traffic conditions

Microscopic simulation models are pivotal in transportation planning for their ability to intricately capture complex traffic patterns. This study specifically explores the role of dynamic route choice behavior within micro-simulation frameworks, emphasizing its application in the context of motorcycle-dominated traffic in Ninh Kiều District, Cần Thơ, Vietnam. By integrating discrete choice models for route selection with rigorous calibration and validation in AIMSUN, the study developed a comprehensive traffic simulation model for large-scale networks. Findings reveal that motorcycles dynamically adjust routes based on current traffic conditions rather than adhering strictly to shortest path strategies. However, the availability of roadside traffic information systems accessible to motorcycles in developing countries is limited, hindering optimal route choices. To address this gap and encourage the enhancement of route choice behavior, the study evaluates the impact of real-time traffic information provision using Macroscopic Fundamental Diagrams within the micro-simulation model. The results demonstrate significant improvements in network efficiency when informed route selection behaviors are facilitated, particularly with comprehensive coverage across all road types. Higher compliance rates notably improved network capacity by up to 84%. Overall, this study contributes methodologically by increasing model precision and practical insights into managing heterogeneous traffic environments, highlighting the transformative potential of traffic information systems and advocating for broader implementation in similar motorcycle-centric regions, which currently still rarely extend beyond toll roads. Future research should extend these methodologies to diverse urban contexts for broader validation and application.

Investigating the Influence of Time Pressure on Risky Riding Behaviour among P-Hailing Riders in Malaysia: The Mediating Role of Moral Disengagement

This study investigates the impact of time pressure on risky riding behaviour among p-hailing riders in Malaysia, with moral disengagement as a mediating factor. Drawing on Bandura's Moral Disengagement Theory and the Job Demand-Resources (JD-R) Model, the research explores how time pressure influences these behaviours. A survey of 200 respondents, selected through stratified convenience sampling, was conducted across three Northern States in Malaysia, namely Perlis, Kedah, and Penang. Data analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM) reveals that time pressure significantly increases risky riding behaviour, with moral disengagement significantly mediates this relationship. The study finds that younger and less experienced riders are particularly susceptible to time pressure effects. Demographic data indicate that 80% of riders are under 35 years old, with 75% having less than three years of experience. Based on these findings, the research emphasises the need for targeted interventions, including specialised training programmes and improved workload management strategies, to mitigate risks associated with p-hailing. The study contributes to a deeper understanding of the role of a cognitive mechanism factor in influencing risky riding behaviours among p-hailing riders and provides valuable insights for enhancing road safety measures. Future research directions are suggested, including exploration of additional cognitive mechanisms that may impact risky riding behaviours in this context.

Safety Insights from E-Scooter Rider and Bicyclist Behavior Observations in Two U.S. Cities

E-scooters, a novel micromobility mode, have transformed U.S. transportation in urban cores, offering potential advantages like cost-effective mobility, congestion reduction, community accessibility, and lower emissions. However, owing to their novelty, their associated safety impacts and rider behaviors remain uncertain, complicating policy formulation. Specifically, e-scooter policy often mirrors bicycle policy, yet there have been few studies that have explicitly investigated how e-scooter and bicycle rider behaviors compare. This research investigated rider behavior relative to infrastructure by examining riding patterns through a video-based platform on 26 Nashville and Portland streets, offering insights on e-scooter and bicyclist behaviors, including riding location and helmet use. This study differs from other observational studies since it covers longer observation periods than prior in-person studies, including nighttime riding behavior. The video analysis also allows for observations of basic demographic characteristics. The findings revealed that bike lanes were well-used and dramatically reduced sidewalk riding and general travel lane usage. Without bike lanes, riders typically resorted to using sidewalks on high-capacity roads and sharing lanes with vehicles on low-capacity streets. The data also suggested demographic trends, such as riskier behavior among younger, male riders, and low helmet usage among e-scooter riders. The results emphasize the need for micromobility-supportive infrastructure in road design to encourage safer riding practices.

Evaluating the Crash Risk and Traffic Offenses of Motorized Two-Wheelers: A Study Specific to Food Delivery Riders

Food delivery riders (FDRs) rely on motorized two-wheelers that are innately unstable, resulting in safety risks. FDRs perform several traffic violations because of delivery time restrictions that result in concern for road safety. In this study, the available Motorcycle Rider Behavior Questionnaire (MRBQ) is modified by considering the opinion of focus groups, to study the riding behavior of FDRs using a sample of 431 participants. Exploratory factor analysis revealed a five-factor solution loaded with 31 items. This research emphasizes that the risky behavior of FDRs is influenced by surrounding traffic and the road environment, in addition to their individual riding behavior. The self-reported crash, near crash, and traffic offenses data of FDRs showed a higher variance when compared with the mean (i.e., over-dispersed). Therefore, the relationship between the target variables—crash in past year, near crash in past year, and penalized traffic offenses in past year—and MRBQ factor scores were established using negative binomial regression analysis. Negative binomial regression analysis shows that the surrounding traffic factor has the highest incidence rate ratio, proving it to be the most significant predictor of crash risk for FDRs in addition to their own aberrant riding behavior. Based on this research, the enforcement of road safety policies can be effective in reducing aberrant riding behavior performed by FDRs such as traffic violation. The risky driving behavior of surrounding traffic can be properly dealt with by imposing hefty penalties and strict enforcement measures, and by spreading awareness.

Impact of attitude, behaviour and opinion of e-scooter and e-bike riders on collision risk in Singapore

Active Mobility Devices (AMDs) such as electric scooters (e-scooters) and electric bikes (e-bikes) are increasingly used on shared paths and Park Connector Networks (PCNs) in Singapore, leading to frequent interactions between AMD riders, pedestrians, and cyclists. To ensure safety, it is crucial to understand the factors associated with collision risk related to these AMDs. To gain insights into the riders’ perspectives on the risk-taking behaviours and attitudes towards safety and sharing paths, a survey was conducted with 369 e-bike and 133 e-scooter riders across Singapore. The collected data was analysed to identify critical features of behaviour, attitudes, and opinions of e-scooters/e-bikes riders and their impact on perceived collision risk. Logistic Regression was used to select the most important behavioural features linked to collision risk, and significance of each was quantified by using the odds ratios in the chosen model. The results reveal that e-bike riders who regularly brake hard to avoid obstacles and highly value capacity of e-bike to carry goods face an increase in collision risk by 49.1% and 43.48% respectively. Those preferring quieter AMDs face 33.31% lower collision risk. Additionally, e-bike riders advocating for more traffic enforcement or the importance of slowing down when overtaking pedestrians face 20.69% and 38.84% lower collision risk respectively. E-scooter riders who manoeuvre quickly to dodge collisions or prioritize passenger-carrying capacity encounter a 142.25% and 67.43% higher collision risk, respectively. Furthermore, e-scooter riders willing to bend rules when not causing inconvenience to others face an increase in collision risk by 123.00%. These outcomes offer significant insights for the design and regulation of active mobility to safeguard all road users in a multi-modal transport environment.

Crash risk associated with powered two-wheeler filtering maneuvers in urban mixed and weak-lane-disciplined traffic streams

The paper studies the crash risk associated with the filtering Powered Two-Wheelers (PTWs) using the trajectory data. A methodology is proposed to automatically identify the filtering PTWs from the trajectory data. A state-of-the-art surrogate safety measure, considering both proximity and evasive actions, was used for identifying the PTW conflicts. The results revealed that the side-swipe conflicts, particularly involving larger vehicles, are predominantly observed during filtering maneuvers. However, the results revealed no significant correlation between the accepted lateral gap and relative speed during PTW filtering. These aspects highlight the hazardous driving behavior of PTW riders while engaging in the filtering maneuvers. The side-swipe crash risk during PTW filtering is about three times higher than the rear-end and side-swipe crash risk during non-filtering. The study suggests that improving infrastructure by implementing designated PTW lanes, where feasible, can reduce the interaction of PTWs with larger vehicles, thereby mitigating the risk of severe crashes.

Indonesian version of the motorcycle rider behaviour questionnaire: cross-cultural adaptation and validation

The study revealed that safety behaviour played a significant role in traffic accidents. One of the primary concerns pertains to the implementation of driving safety practices and the associated risks they pose on the roadways. Numerous studies have been conducted in various countries to investigate the negative consequences of safety behaviour on both victims and nations. These studies have employed the motorcycle rider behaviour questionnaire (MRBQ), a widely utilized instrument to evaluate and mitigate the prevalence of hazardous behavior in road driving scenarios. Nevertheless, the validity of this method has not been established in the Indonesian setting. The present study aimed to assess the reliability and construct validity of the Indonesian adaptation of the MRBQ. The study recruited a cohort of 37 students representing a diverse range of academic disciplines from 12 distinct universities. The study's findings revealed that the validity values obtained varied between a minimum of <0.001 and a maximum of 0.987. Additionally, the reliability of the 27 items was assessed using Cronbach's alpha coefficient, which yielded a value of 0.742. The present study confirms the reliability of the Indonesian version of the MRBQ, as well as the acceptability of its construct validity, which exceeds the threshold of 0.70. Therefore, this tool can be valuable for evaluating driving safety behaviour.

Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

The struggle to overcome traffic congestion: A study of social interaction and effects on deviant behavior of motorcycle riders

Background: Motorcycle riding as a means of daily transportation in Southeast Asia, especially Jakarta, Indonesia, manifests the struggle to use the road. This struggle - associated with congestion, irregularity, and lack of control - can lead to physical harm or material loss. Studies on deviant behavior related to motorcycle use mainly discuss motorcycle gangs and traffic violations. This paper intends to explicate how the context of this struggle informs the different meanings of the deviant behavior of motorcyclists in daily traffic life. It employs concepts of innovation and ritualism, learning, harm, visuality, and secret deviance to shatter the prevailing understanding of motorcyclist traffic behavior. Methods: This study chooses T.B. Simatupang Street, one of the busiest roads in Jakarta, as a research location. A mixed method is used to examine the context of the struggle, first qualitatively by utilizing visual data collected through (a) direct video recordings on the road and (b) aerial drone video recordings. Both recordings captured images of motorcyclist behavior considered deviant and can cause harm, such as stopping illegally, clamoring while cutting lanes and other vehicles, and slipping between two automobiles. Then quantitatively, it conducts a survey to collect data on motorcyclists' experiences. Included in the survey were questions with images captured from the direct video recording footage to collect responses toward motorcyclist riding behavior. Findings: The survey findings show that responses toward images of motorcyclist behavior - concerning harm - show a lack of understanding of driving safety and traffic rules, and some consider it as just the daily routine of motorcyclists. Conclusion: Therefore, the visuality of motorcyclist traffic violation is learned through innovation (driving recklessly) or ritualism (daily habit) as a struggle that is ironically visibly secretly upheld by fellow motorcyclists. Novelty/Originality of this Study: This study offers a novel perspective by framing motorcyclists' behavior in Jakarta's traffic as a form of adaptive struggle rather than mere deviance or rule-breaking. It uniquely utilizes visual criminology to argue that what is often seen as traffic violations may be better understood as contextual adaptations to urban congestion, thereby challenging prevailing interpretations of motorcyclist behavior.

The association of road, traffic, and environmental factors with wrong-way riding behavior on shared e-bikes

Wrong-way riding (WWR) behavior of electric bikes (e-bikes) is a hazardous practice that frequently threatens traffic safety. It is imperative to accurately detect instances of WWR and analyze the various factors contributing to this behavior to develop and implement effective management policies. The emergence of shared e-bikes and advancements in machine learning have opened up new avenues for accurately examining the determinants of WWR behavior. Therefore, this study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting WWR behavior. Subsequently, association rule mining investigates the correlation among road, traffic, environmental factors, and the likelihood of higher and lower WWR behavior. The results indicate that: (1) In the association rules for higher WWR frequency, the absence of central separation facilities and bike lane separation facilities on the tertiary road, as well as lower land use density and the lack of public transportation facilities, enhanced the likelihood of WWR occurrence; (2) In the association rules for lower WWR frequency, longer crossing facility distance, physical bike lane separation, along with higher land use mixing and the presence of public transportation facilities, reduce the probability of WWR occurrence; (3) Time conditions display spatial heterogeneity, and different land use and road condition modulate the impact of time factors on rider’ intention for WWR. Finally, based on the study findings, adequate policy recommendations are proposed for urban management agencies to reduce WWR behavior and enhance the safety of the e-bike traffic system.

Exploring the impact of right-turn safety measures on E-bike-heavy vehicle conflicts at signalized intersections

A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders.

Accustomed or Regulated: Influencing factors of two-wheeler riders’ illegal Lane-Transgressing behavior when overtaking

Illegal lane-transgressing is a typical aberrant riding behavior of riders of two-wheelers, i.e., motorcycles, bicycles, and e-bikes, which is highly frequent in accident reports. However, there is insufficient attention to this behavior at present. This study aims to explore the socio-psychologic factors that influence the illegal lane-transgressing behavior of two-wheeler riders when overtaking. For this purpose, a questionnaire was first composed. The questionnaire included the behavioral intention of two-wheeler riders towards illegal overtaking behavior and five influencing factors: safety knowledge, descriptive norms, injunctive norms, perceived behavior control, and risk perception. Second, a survey was conducted on different two-wheeler riders in Xi’an. Third, various types of two-wheelers were analyzed jointly and separately by structural equation models and analyses of variance. Results show that e-bike riders were more similar to motorcycle riders in behavioral intentions, with their risk perception weaker than other riders. Descriptive norms and perceived behavior control played the most significant roles in the structural equation model. It was also found that two-wheeler riders with a car license had better traffic safety performance. Based on the above results, it is recommended that attention be paid to illegal lane-transgression in the process of law enforcement and education, and a higher level of safety training should be provided for two-wheeler riders.

Modelling Merging Behaviour of Drivers in Heterogeneous Traffic at Roundabouts

Roundabouts are among the widely used traffic control infrastructures, which function by defining priorities for various entry approaches and circulating traffic. In many developing countries, the priority rules are not implemented through pavement markings or road signs and drivers are also unaware of these rules. Thus, available gaps become the only criteria for merging and movement of vehicles through the roundabout. It has been observed that the gap acceptance behaviour of drivers also depends on the type of vehicle in heterogeneous traffic streams. The main objective of this study is to investigate the difference in gap acceptance behaviour of motorcycle riders and car drivers at roundabouts. The gaps accepted by car drivers and motorcycle riders under different traffic conditions are measured from the recorded video of the traffic stream at a selected roundabout using an unmanned aerial vehicle. The critical gap is estimated using Raff’s method and its relationship with circulatory volume and approach speed is analysed. The results show that the critical gap for motorcycles (2.46 seconds) is 23.3% smaller than the critical gap for cars (3.21 seconds). Motorcycles can move through smaller gaps in comparison with cars due to their size and ease of manoeuvring. Results also show that motorcycles tend to merge with greater approach speed as the available gap within the circulating vehicles reduces, whereas the cars are observed following an opposite trend.

Examining Risky Riding Behaviors: Insights from a Questionnaire Survey with Middle-Aged and Older Motorcyclists in Thailand

This research endeavors to achieve three primary objectives. Firstly, it seeks to develop a measurement model capable of assessing the motorcycle riding behavior of middle-aged individuals in Thailand. The construction of this model integrates the motorcycle rider behavior questionnaire (MRBQ) with statistical and descriptive analyses. Secondly, the research examines the accuracy of the measurement model using a factor analysis approach, comprising both exploratory and confirmatory factor analyses. Lastly, the study aims to furnish the people of Thailand with a set of guidelines for self-assessment of their motorcycle riding habits. The examination involves 399 middle-aged motorcycle riders aged 35 years or older, a significant majority of whom (81%) possess the requisite licenses for motorcycle operation, with the majority (83%) accumulating over a decade of riding experience. Through analysis, a set of 39 items capturing nuanced behaviors exhibited by middle-aged motorcyclists in Thailand is identified. These items are categorized into four distinct clusters: speed violations, control errors, traffic errors, and adherence to safety equipment protocols. The utilization of the MRBQ in this study holds significant importance, as it provides valuable insights into the riding practices of the Thai population. The resultant findings hold the potential to inform safety initiatives and strategies aimed at enhancing the overall motorcycle riding experience.

Assessment of Risky Riding Behaviors Using the Motorcycle Rider Behavior Questionnaire (MRBQ) among University Students

Assessment of Risky Riding Behaviors Using the Motorcycle Rider Behavior Questionnaire (MRBQ) among University Students

Suitable solutions and EU regulatory framework of electric light mobility vehicles for last-mile delivery: an overview

Due to current environmental European regulations in terms of pollutant emissions, electric cargo cycles are receiving growing interest compared to heavy-duty vehicles as a cheaper and healthier alternative for last-mile delivery of goods in urban and suburban areas. Within the PNRR Next Generation EU program, the Sustainable Mobility Center aims to design innovative e-cargo bikes considering technical features along with rider behaviour and infrastructure characteristics. The market offers plenty of solutions, which may differ in technical and design features. Nowadays a standardized terminology, widely shared among researchers for those light mobility vehicles does not exist. Several authors employ different terms or introduce classifications based upon the vehicle type of use or even on the vehicle layout (e.g., payload position or number of wheels). Concurrently, EU regulatory framework is undergoing rapid changes and appears unclear. Certain categories of light mobility vehicles might be ruled out from the type-approval system established by the regulation EU 168/2013. For those vehicles, harmonised standards are under drafting or approval. This situation requires a considerable effort among manufacturers in the identification of the proper product directives or regulations and the associated mandatory requirements and testing procedures for the introduction of a new vehicle model in the market. Within this context, an overview of the EU regulatory framework for these light mobility vehicles is presented. A literature evaluation with a structured and coded methodology of scientific databases, company websites and commercial reports is performed, evaluating commercial and prototype solutions of e-cargo bikes from a technical and legislative perspective. Furthermore, a case study is addressed, deepening the national legislation which rules the use of e-cargo bikes in Italy. The information provided in this work may be useful among manufacturers as a starting point for the design process of these types of vehicles. Moreover, this paper lays the foundations for a widely recognized taxonomy of e-cargo bikes based upon distinctive features.

Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness

This paper reconsiders the On-Demand Multimodal Transit Systems (ODMTS) Design with Adoptions problem (ODMTS-DA) to capture the latent demand in on-demand multimodal transit systems. The ODMTS-DA is a bilevel optimization problem, for which Basciftci and Van Hentenryck proposed an exact combinatorial Benders decomposition. Unfortunately, their proposed algorithm only finds high-quality solutions for medium-sized cities and is not practical for large metropolitan areas. The main contribution of this paper is to propose a new path-based optimization model, called P-Path, to address these computational difficulties. The key idea underlying P-Path is to enumerate two specific sets of paths which capture the essence of the choice model associated with the adoption behavior of riders. With the help of these path sets, the ODMTS-DA can be formulated as a single-level mixed-integer programming model. In addition, the paper presents preprocessing techniques that can reduce the size of the model significantly. P-Path is evaluated on two comprehensive case studies: the midsize transit system of the Ann Arbor – Ypsilanti region in Michigan (which was studied by Basciftci and Van Hentenryck) and the large-scale transit system for the city of Atlanta. The experimental results show that P-Path solves the Michigan ODMTS-DA instances in a few minutes, bringing more than two orders of magnitude improvements compared with the existing approach. For Atlanta, the results show that P-Path can solve large-scale ODMTS-DA instances (about 17 millions variables and 37 millions constraints) optimally in a few hours or in a few days. These results show the tremendous computational benefits of P-Path which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand. History: Accepted by Andrea Lodi, Design & Analysis of Algorithms—Discrete. Funding: This work was partially supported by National Science Foundation Leap-HI [Grant 1854684] and the Tier 1 University Transportation Center (UTC): Transit - Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) from the U.S. Department of Transportation [69A3552047141]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0014 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0014 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility <200 m, where the severe weather and visibility <200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two-wheeled vehicles in China.

Self-reported cycling behavior and previous history of traffic accidents of cyclists

BackgroundCyclists are vulnerable traffic users and studying the cycling behavior of professional and elite cyclists, their previous history of traffic accidents combined with the current knowledge on high-risk behaviors of this group can be a useful basis for further studies on ordinary cyclists. This study aimed to determine the relationship between cycling behavior and the previous history of traffic accidents among members of the Cycling Federation of Guilan province in 2022.MethodsA descriptive-analytical study was performed in which the Bicycle Rider Behavior Questionnaire (BRBQ) constructed in the Porsline platform was distributed using the WhatsApp social network. All participants were asked to self-report their cycling behavior. The final analysis was performed by using STATA software (version 14).ResultsThe study subjects included a total of 109 cyclists with a mean age of 38.62 ± 10.94 years and a mean cycling experience of 13.75 ± 11.08 years. Using the logistic regression model, the relationship between gender (P = 0.039), years of cycling experience (P = 0.000), and education level (P ≤ 0.00), with previous traffic accidents, was found significant. There was also a significant relationship between stunts and distractions (P = 0.005), signaling violation (P = 0.000), and control error (P = 0.011) with previous traffic accidents. A significant association existed between stunts and distractions (P = 0.001) and signaling violation (P = 0.001) with a previous history of traffic injury within the last 3 years.ConclusionsThe findings of this study can be used to establish cyclist safety and preventative planning in society. In behavior change intervention programs, it is best to target male cyclists with higher-level education. In addition, the behavior of the cyclists whose predominant term of signaling violations must be corrected should be targeted. It is necessary to shape information campaigns and educational programs aimed for cyclists with common high-risk behaviors, especially signaling violations.

What factors contribute to in-role and extra-role safety behavior among food delivery riders?

Food delivery riders face significant road safety risks globally. Previous studies have primarily focused on the direct relationship between job demands, job resources, and road safety behaviors, often neglecting the potential indirect paths through dual processes, namely the health impairment process and the motivational process, which may lead to dangerous riding behaviors. Additionally, the impact of organizational citizenship behaviors on the safety of food delivery riders within the industry remains unexplored. This study aims to address these gaps by applying the dual processes of the Job Demands-Resources model (JD-R) to identify factors influencing the safety behaviors of food delivery riders in Vietnam. Data were collected through face-to-face surveys with 410 riders in public places. The findings show a nuanced interplay between job demands, resources, and safety behaviors. Specifically, job burnout acts as a bridge, mediating the influence of job demands on in-role safety behaviors. On the other hand, job resources-encompassing facets like job autonomy, performance feedback, technology support, and work support-indirectly shape both in-role and extra-role safety behaviors via the conduit of motivation. These resources not only bolster motivation but also serve as pillars in fostering a culture of safety among riders. This research not only deepens our understanding of the determinants of food delivery riders' safety behaviors but also highlights the pivotal role food delivery companies play in shaping these behaviors. By prioritizing the holistic well-being of their riders and offering robust support, these companies can foster an environment conducive to safety. Additionally, by integrating additional dimensions of job demands and resources into the JD-R model, this study paves the way for more comprehensive and nuanced future research in this domain.