Yiqi Zhang

The effects of sunshields on red light running behavior of cyclists and electric bike riders

Bicycles held an important position in transportation of China and other developing countries. As accidents rate involving electronic and regular bicycles is increasing, the severity of the bicycle safety problem should be paid more attention to. The current research explored the effect of sunshields (a kind of affordable traffic facility built on stop line of non-motor vehicle lanes (According to National Standard in China, e-bikes share the non-motor vehicle lane with regular bikes.) which was undertaken to avoid riders suffering from sunlight and high temperature) on diminishing red light running behavior of cyclists and e-bike riders. An observational study of 2477 riders was conducted to record and analyze their crossing behaviors at two sites across the city of Hangzhou, China. Results from logistic regression and analysis of variance indicated a significant effect of sunshield on reducing red light infringement rate both on sunny and cloudy days, while this effect of sunshield was larger on sunny days than on cloudy days based on further analysis. The effect of intersection type in logistic regression showed that riders were 1.376 times more likely to run through a red light upon approaching the intersection without sunshields compared to with sunshields in general. The results of MANCOVA further confirmed that rates of running behaviors against red lights were significantly lower at the intersections with a sunshield than at intersections without sunshields when other factors including traffic flow were statistically controlled. To sum up, it is concluded that sunshields installed at intersections can reduce the likelihood of red light infringement of cyclists and e-bike riders on both sunny and cloudy days. For those areas or countries with a torrid climate, sunshield might be a recommended facility which offers an affordable way to improve the safety of cyclists and e-bike riders at intersections. Limitations of the current sunshield design and current study are also discussed.

Online Prediction of Driver Distraction Based on Brain Activity Patterns

This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a drivers needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings

INTRODUCTION Under the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios. METHODS The present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios. RESULTS Maximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5s to 8s. Specifically, the controlled lead time ranging from 4s to 8s led to the optimal safety benefit; and the controlled lead time ranging from 5s to 8s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed. CONCLUSIONS The results indicated that the controlled lead time significantly affected driver performance. PRACTICAL APPLICATIONS The findings have implications for the design of collision warning systems.

Addressing the Safety of Transportation Cyber-Physical Systems: Development and Validation of a Verbal Warning Utility Scale for Intelligent Transportation Systems

As an important application of Cyber-Physical Systems (CPS), advances in intelligent transportation systems (ITS) improve driving safety by informing drivers of hazards with warnings in advance. The evaluation of the warning effectiveness is an important issue in facilitating communication of ITS. The goal of the present study was to develop a scale to evaluate the warning utility, namely, the effectiveness of a warning in preventing accidents in general. A driving simulator study was conducted to validate the Verbal Warning Utility Scale (VWUS) in a simulated driving environment. The reliability analysis indicated a good split-half reliability for the VWUS with a Spearman-Brown Coefficient of 0.873. The predictive validity of VWUS in measuring the effectiveness of the verbal warnings was verified by the significant prediction of safety benefits indicated by variables, including reduced kinetic energy and collision rate. Compared to conducting experimental studies, this scale provides a simpler way to evaluate overall utility of verbal warnings in communicating associated hazards in intelligent transportation systems. This scale can be further applied to improve the design of warnings of ITS in order to improve transportation safety. The applications of the scale in nonverbal warning situations and limitations of the current scale are also discussed.

The Effect of Lead Time of Collision Warning Messages on Driver Performance

Collision warning systems (CWSs) are in development in the intelligent transportation system domain to reduce collision accidents. The lead time of warning messages is a crucial factor in determining system effectiveness in the prevention of accidents. The present experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message was issued) and lead vehicle conditions (without vs. with lead vehicle) on driving behaviors in various collision scenarios. The results indicated the controlled lead time and lead vehicle conditions significantly affected driver performance. Maximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 4.5s to 6s. When the warning messages were relatively late, the existence of a lead vehicle brought greater safety benefits and more abrupt deceleration. Potential applications of the results in designing of CWSs are further discussed.

Psychometric Examination and Validation of the Aggressive Driving Scale (ADS)

Aggressive driving behavior is an important cause of traffic accidents and injuries. The Aggressive Driving Scale (ADS) analyzed in the present study consists of 24 items. A sample of 276 participants was analyzed to obtain the factor structure and reliability of the ADS and 67 of them participated in the behavioral experiment to validate the scale. Results indicated a three-factor structure (Interference with other drivers, Violations/Risk taking, and Anger/Aggression expression) with high item loadings. Reliability analysis reported excellent internal consistency and test-retest reliability. Construct validity was established as the ADS subscale scores correlated significantly with trait measures of anger and aggression. Predictive validity of the ADS was verified through multiple regression analyses as the ADS is a significant predictor of behavioral measures in the simulated environment and self-report measures of real world violations. These results suggest that the ADS is a reliable and valid tool in evaluating aggressive driving behaviors.

Development and Validation of Warning Message Utility Scale (WMUS)

The evaluation of the warning message effectiveness is an important issue in improving communication safety in the system. The goal of the present research was to develop the scale to evaluate the warning message utility, namely, the effectiveness of warning message in preventing accident in general, and an empirical study was conducted to validate the Warning Message Utility Scale (WMUS) in a controlled laboratory environment. The reliability analysis indicated a good the split-half reliability for the WMUS with a Spearman-Brown Coefficient of .873. The predictive validity of WMUS was verified by the significant correlations between the WMUS scores and behavioral indexes of message utility (including reduced kinetic energy and collision rate). The results of regression indicated that the VWMUS is significant predictor of reduced kinetic energy (r2=.339, p<.001) and collision rate (r2=.363, p<.001), which further proved that the validity of WMUS in measuring effectiveness of the warning messages.

Psychometric examination and validation of the aggressive driving scale (ADS)

Aggressive driving behavior is an important cause of traffic accidents. Based on the recent view that aggressive driving is one way that trait aggression manifests itself, a growing research area has focused on the development of scales to assess aggressive driving. The aggressive driving scale (ADS) analyzed in the present study consists of 24 items. A sample of 276 participants was analyzed to obtain the factor structure and reliability of the ADS and 67 of them participated in the behavioral experiment in order to examine the construct and predictive validity of the scale. Results indicated a 3-factor structure (interference with other drivers, violations/risk taking, and anger/aggression expression behavior) with high item loadings. The ADS had high internal consistency and test-retest reliability. Construct validity of the ADS was established as the ADS subscale scores correlated significantly with trait measures of anger and aggression. Predictive validity of the ADS was verified as most items were significantly correlated with behavioral measures derived from a driving simulator. The ADS was a significant predictor of behavioral measures both in the simulated environment (i.e., frequency of driving off the road, red light running behavior, frequency of colliding with a vehicle, frequency and distance of over speeding, frequency and distance of central crossing) and reported real world situations (i.e., annual moving violations and accidents). These results suggest that the ADS is a reliable and valid tool in evaluating aggressive driving behavior as the current study provides behavioral support for the effectiveness of the ADS in measuring aggressive driving behavior. Aggr. Behav. 42:313-323, 2016.

Modeling the Effect of Loudness and Semantics of Speech Warnings on Human Performances

The quantitative prediction and understanding of human performances in the responses to speech warnings is an essential component to improve warning effectiveness. Queuing network-model human processor (QN-MHP), as a computational architecture, enables researchers to model dual-task information processing. The current study enhanced QN-MHP by modelling the effect of loudness and semantics on human responses to speech warning messages. The model predictions of crash rate were validated with two empirical studies in collision warning systems with resultant R squares of 0.73 and 0.77, respectively. The developed mathematical model could be further utilized in optimizing the design of speech warnings to achieve most safety benefits.

Head-up Display Graphic Warning System Facilitates Simulated Driving Performance

ABSTRACT This study aims to investigate the usability of a head-up display (HUD) in presenting warning messages during driving and create a new and effective vehicle early warning system for drivers. Two experiments were conducted. In Experiment 1, 36 drivers were randomly assigned to a group using HUD and a control group. The simulated driving performance of the two groups was compared to determine if the HUD graphic early warning system facilitates driving safety. Results revealed that the HUD-using group demonstrated better driving performance than the control group in terms of collision, mean deceleration, accelerator release reaction time, brake reaction time, reduced velocity, reduced energy, steering reaction time, mean reaction time, and minimum reaction time. We investigated the influence of the presentation mode of warning messages on simulated driving performance in Experiment 2. Forty-eight drivers were randomly assigned to an HUD warning group, an audio warning group, and an audiovisual group that integrated HUD and audio warning. The drivers in the HUD warning group performed better than those in the two other groups in terms of mean deceleration. The audiovisual group that integrated HUD and audio warning showed an advantage in reduced velocity. The findings indicated that HUD technology has the potential to promote safe driving by improving the early warning system.