Performance degrades less under increased workload with the addition of speech control in a dynamic environment.

This research empirically evaluates the introduction of speech to existing keyboard and mouse input modalities in an application used to control aircraft in a simulated, complex and dynamic environment. Task performance and task performance degradation are assessed for three levels of workload. Previous studies have evaluated task performance using these modalities however, only a couple have evaluated task performance under varying workload. Even though speech is a common addition to modern control interfaces, the effect of varying workload on this combination of control modalities has not yet been reported. Thirty-six participants commanded simulated aircraft through generated obstacle courses to reach a Combat Air Patrol (CAP) point while also responding to a secondary task. There were nine conditions that varied the control modality (Keyboard and Mouse (KM), Voice (V), and Keyboard, Mouse and Voice (KMV)), and workload by varying the number of aircraft being controlled (low, medium and high). Results showed that KM outperformed KMV and V for the low and medium workload levels. However, task performance with KMV was found to degrade the least as workload increased. KMV and KM were found to enable significantly more correct responses to the secondary task which was delivered aurally. Participants reported a preference for the combined modalities (KMV), self-assessing that KMV most reduced their workload. This research suggests that the addition of a speech interface to existing keyboard and mouse modalities, for control of aircraft in a simulation, may help manage cognitive load and may assist in controlling more aircraft under higher workloads.

How does financial burden influence the crash rate among taxi drivers? A self-reported questionnaire study in China.

Objective: Taxis play an important role in the transportation system of China, but they have a relatively high accident rate. The current study discusses the driver's financial burden in the Chinese context and explores its correlation with working conditions, risky driving behavior, and other characteristics of taxi drivers who are involved in accidents.Method: A total of 2,391 taxi drivers from 29 companies in four Chinese cities were interviewed and then asked to complete a questionnaire concerning their socio-demographic characteristics, working conditions, risky driving behavior, and accident frequency during the previous two years. Given the increase in the management fee (measured in CNY) charged by taxi companies, the drivers were divided into three groups: the "less than 150" group, the" 150 to 180" group and the "over 180" group, where were named Group 1, Group 2 and Group 3, respectively. Finally, the zero-inflated Poisson model was used to investigate the factors that contributed to the accident rate for each group.Result: The significant factors that lead to accidents differed significantly for drivers with different levels of financial burden. First, most of the factors were weakly correlated with the crash rate among Group 1 drivers. Second, many factors related to working conditions and risky driving behavior were significant for drivers in Groups 2 and 3, while working hours and off-duty days were significant only for drivers in Group 3. Third, working hours were negatively correlated with accident rates for drivers in Group 3, and the drivers who suffered from the heaviest financial burden were most affected by fatigue and sleep problems.Conclusion: Financial burden is the root cause behind the propensity of taxi drivers to be involved in accidents. Taxi companies should find ways to reduce drivers' expenses, and new technologies, such as taxi-calling or location and navigation based on mobile applications, should be introduced into the traditional taxi industry.

Self-reports of workloads and aberrant driving behaviors as predictors of crash rate among taxi drivers: A cross-sectional study in China.

Objective: Taxis provide an important mode of public transport in China, but there has been very little in-depth research on the crash involvement propensity of taxi drivers. Thus, this study was conducted to develop a quantitative model for predicting the crash rate of taxi drivers. Methods: A total of 2,391 taxi drivers from 29 companies in 4 Chinese cities completed a structured and anonymous face-to-face questionnaire reporting their demographic information, workload conditions, aberrant driving behaviors, and crash history within the 2 years prior to the survey. Using the self-reported variables, a negative binomial model was implemented to predict taxi drivers' property damage only (PDO) and personal injury (PI) crash rates and identify the factors contributing to this risk. Results: Descriptive analysis of the survey data revealed that the workload of taxi drivers in China is relatively heavy. Seven significant predictors of PDO and PI crash rates were identified, including crossing red lights, dangerous overtaking, honking at a slow driver, failure to use an indicator lamp, driving while fatigued, stopping in forbidden areas to pick up or drop off a passenger, and driving with one hand. Taxi drivers' average off-duty days per week, aggressive driving behaviors, and preventing another driver from merging had significant effects only on PDO crash rate, and sleep problems were found to be significantly correlated with PI crash rate. Conclusions: To improve the safety of taxi drivers, considerable measures should be strictly implemented, ranging from periodic driver training and safety education to workload reduction, with the cooperation of government agencies and taxi companies. The findings of this study contribute to the design of potentially useful policy initiatives as well as targeted safety promotion programs.