Crossing at a Red Light: Behavior of Cyclists at Urban Intersections

To investigate the relationship between cyclist violation and waiting duration, the red-light running behavior of nonmotorized vehicles is examined at signalized intersections. Violation waiting duration is collected by video cameras and it is assigned as censored and uncensored data to distinguish between normal crossing and red-light running. A proportional hazard-based duration model is introduced, and variables revealing personal characteristics and traffic conditions are used to describe the effects of internal and external factors. Empirical results show that the red-light running behavior of cyclist is time dependent. Cyclist’s violating behavior represents positive duration dependence, that the longer the waiting time elapsed, the more likely cyclists would end the wait soon. About 32% of cyclists are at high risk of violation and low waiting time to cross the intersections. About 15% of all the cyclists are generally nonrisk takers who can obey the traffic rules after waiting for 95 seconds. The human factors and external environment play an important role in cyclists’ violation behavior. Minimizing the effects of unfavorable condition in traffic planning and designing may be an effective measure to enhance traffic safety

Social Influence and Different Types of Red-Light Behaviors among Cyclists

Accident analysis and studies on traffic revealed that cyclists’ violation of red-light regulation is one typical infringement committed by cyclists. Furthermore, an association between cyclists’ crash involvement and red-light violations has been found across different countries. The literature on cyclists’ psychosocial determinants of red-light violation is still scarce. The present study, based on the classification of cyclists’ red-light behavior in risk-taking (ignoring the red-light and traveling through the junction without stopping), opportunistic (waiting at red-lights but being too impatient to wait for green signal and subsequently crossing the junction) and law-obeying (stopping to obey the red-light), adopted an eye-observational methodology to investigate differences in cyclists' crossing behavior at intersections, in relation to traffic light violations and the presence of other cyclists. Based on the social influence explanatory framework, which states that people tend to behave differently in a given situation taking into consideration similar people’s behaviors, and that the effect of social influence is related to the group size, we hypothesized that the number of cyclists at the intersection will have an influence on the cyclists’ behavior. Furthermore, cyclists will be more likely to violate in an opportunistic way when other cyclists are already committing a violation. Two researchers at a time registered unobtrusively at four different intersections during morning and late afternoon peak hour traffic, 1381 cyclists approaching the traffic light during the red phase. The 62.9% violated the traffic control. Results showed that a higher number of cyclists waiting at the intersection is associated with fewer risk-taking violations. Nevertheless, the percentage of opportunistic violation remained high. For the condition of no cyclist present, risk-taking behaviors were significantly higher, whereas, they were significantly lower for conditions of two to four and five or more cyclists present. The percentage of cyclists committing a red-light violation without following any other was higher for those committing a risk-taking violation, whereas those following tended to commit opportunistic violations more often

An Accident Waiting to Happen: Cognitive Drivers of Unsafe Cycling Behavior

Bicycling is a popular method of transportation and recreational activity utilized ubiquitously around the world. In the United States alone thousands of active cycling clubs exist, in addition to the millions of riders who ride independently, and cycling has shown a continual steady increase for decades. As cycling becomes more and more popular, a commensurate increase in cycling accidents and fatalities has also occurred. Regardless of current safety interventions employed hundreds of cyclist fatalities and tens of thousands of cyclist injuries are recorded/reported annually. Cycling accidents are estimated to cost billions of dollars in damages, medical expenses, lost wages, and insurance. The current body of literature may not comprehensively take into account important factors associated with unsafe cycling behaviors and resulting cycling safety efforts may be predicated on this incomplete information. Thus, my doctoral research focuses on investigating cognitive drivers of unsafe cycling behaviors through multiple studies. Study 1 was a systematic review of the current unsafe cycling behavior literature utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Emergent themes from this review were incomplete representations of actual behaviors, shortcomings associated with the various methodological approaches employed, and scant understanding of why cyclists choose to ride unsafely. Study 2 utilized an observational approach to identify actual rates of unsafe cycling behaviors across different infrastructure design characteristics. Accident data in conjunction with laws governing cyclists drove the selection of behaviors observed (e.g., failing to stop at a stop light or making an illegal turn), and infrastructure design characteristics (e.g., enhanced pedestrian walkway or staggered t-intersection) were identified via established parameters according to the Department of Transportation. High rates of unsafe behaviors were consistently seen across locations including, for example, failing to stop at a stop light and failing to yield to traffic. Significant differences across locations were, for instance, making an illegal turn and riding in an unauthorized area. Study 3 employed questionnaires to quantitatively examine several cognitive drivers of unsafe cycling behaviors. Factors that impact cyclists’ decisions to ride unsafely, as well as unsafe behavioral outcomes, were analyzed using Analytic Hierarchy Process and Policy Capturing methodologies. Results indicated which factors were significant (e.g., if the cyclist is running late or has ample time to reach their destination) and which were not (e.g., the presence or lack of a dedicated bicycle path) within the decision making process to ride unsafely. Finally, the overall results of the studies were synthesized into a policy statement outlining major findings and recommendations to inform future legal, civil, and academic endeavors associated with cycling safety interventions

Crossing Reliability of Electric Bike Riders at Urban Intersections

This paper presents a crossing reliability model of electric bike riders at urban intersections using survival analysis approach. Riders’ crossing behavior was collected by video cameras. Waiting times in the red-light phase were modeled by reliability-based model that recognizes the covariate effects. Three parametric models by the exponential, Weibull, and log-logistic distributions were proposed to analyze when and why electric bike riders cross against the red light. The results indicate that movement information and situation factors have significant effects on riders’ crossing reliability. The findings of this paper provide an important demonstration of method and an empirical basis to assess crossing reliability of electric bike riders at the intersection

Can i Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist\u2019s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types

Pedestrians\u27 Receptivity Toward Fully Autonomous Vehicles

Fully Autonomous Vehicles (FAVs) have the potential to provide safer vehicle operation and to enhance the overall transportation system. However, drivers and vehicles are not the only components that need to be considered. Research has shown that pedestrians are among the most unpredictable and vulnerable road users. To achieve full and successful implementation of FAVs, it is essential to understand pedestrian acceptance and intended behavior regarding FAVs. Three studies were developed to address this need: (1) development of a standardized framework to investigate pedestrians’ behaviors for the U.S. population; (2) development of a framework to evaluate their receptivity of FAVs; and (3) investigation of the influence of the external interacting interfaces of FAVs on pedestrian receptivity toward them. The pedestrian behavior questionnaire (PBQ) categorized pedestrian general behaviors into five factors: violations, errors, lapses, aggressive behaviors, and positive behaviors. The first four factors were found to be both valid and reliable; the positive behavior scale was not found to be reliable nor valid. A long (36-item) and a short (20-items) versions of the PBQ were validated by regressing scenario-based survey responses to the fiveactor PBQ subscale scores. The pedestrian receptivity questionnaire for FAVs (PRQF) consisted of three subscales: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and the reliability of each subscale was confirmed. Regression analyses showed that pedestrians’ intention to cross the road in front of a FAV was significantly predicted by both safety and interaction scores, but not by the compatibility score. On the other hand, acceptance of FAVs in the existing traffic system was predicted by all three subscale scores. Finally, an experimental study was performed to expose pedestrians to a simulated environment where they could experience a FAV. The FAV in the simulated environment was either equipped with external features (audible and/or visual) or had no external (warning) feature. The least preferred options were the FAVs with no features and those with a smiley face but no audible cue. The most preferred interface option, which instilled confidence for crossing in front of the FAV, was the walking silhouette

Modeling the Frequency of Cyclists' Red-Light Running Behavior Using Bayesian PG Model and PLN Model

Red-light running behaviors of bicycles at signalized intersection lead to a large number of traffic conflicts and high collision potentials. The primary objective of this study is to model the cyclists' red-light running frequency within the framework of Bayesian statistics. Data was collected at twenty-five approaches at seventeen signalized intersections. The Poisson-gamma (PG) and Poissonlognormal (PLN) model were developed and compared. The models were validated using Bayesian values based on posterior predictive checking indicators. It was found that the two models have a good fit of the observed cyclists' red-light running frequency. Furthermore, the PLN model outperformed the PG model. The model estimated results showed that the amount of cyclists' redlight running is significantly influenced by bicycle flow, conflict traffic flow, pedestrian signal type, vehicle speed, and e-bike rate. The validation result demonstrated the reliability of the PLN model. The research results can help transportation professionals to predict the expected amount of the cyclists' red-light running and develop effective guidelines or policies to reduce red-light running frequency of bicycles at signalized intersections

Modeling the Frequency of Cyclists’ Red-Light Running Behavior Using Bayesian PG Model and PLN Model

Red-light running behaviors of bicycles at signalized intersection lead to a large number of traffic conflicts and high collision potentials. The primary objective of this study is to model the cyclists’ red-light running frequency within the framework of Bayesian statistics. Data was collected at twenty-five approaches at seventeen signalized intersections. The Poisson-gamma (PG) and Poisson-lognormal (PLN) model were developed and compared. The models were validated using Bayesian p values based on posterior predictive checking indicators. It was found that the two models have a good fit of the observed cyclists’ red-light running frequency. Furthermore, the PLN model outperformed the PG model. The model estimated results showed that the amount of cyclists’ red-light running is significantly influenced by bicycle flow, conflict traffic flow, pedestrian signal type, vehicle speed, and e-bike rate. The validation result demonstrated the reliability of the PLN model. The research results can help transportation professionals to predict the expected amount of the cyclists’ red-light running and develop effective guidelines or policies to reduce red-light running frequency of bicycles at signalized intersections

Waiting Endurance Time Estimation of Electric Two-Wheelers at Signalized Intersections

The paper proposed a model for estimating waiting endurance times of electric two-wheelers at signalized intersections using survival analysis method. Waiting duration times were collected by video cameras and they were assigned as censored and uncensored data to distinguish between normal crossing and red-light running behavior. A Cox proportional hazard model was introduced, and variables revealing personal characteristics and traffic conditions were defined as covariates to describe the effects of internal and external factors. Empirical results show that riders do not want to wait too long to cross intersections. As signal waiting time increases, electric two-wheelers get impatient and violate the traffic signal. There are 12.8% of electric two-wheelers with negligible wait time. 25.0% of electric two-wheelers are generally nonrisk takers who can obey the traffic rules after waiting for 100 seconds. Half of electric two-wheelers cannot endure 49.0 seconds or longer at red-light phase. Red phase time, motor vehicle volume, and conformity behavior have important effects on riders’ waiting times. Waiting endurance times would decrease with the longer red-phase time, the lower traffic volume, or the bigger number of other riders who run against the red light. The proposed model may be applicable in the design, management and control of signalized intersections in other developing cities