Impact of Driver Age and Experience in Software Usage on Driving Safety and Usability of Car-Sharing Software

Introduction

As the modern world moves toward a more futuristic and technologically advanced society, it is not uncommon to see various businesses thrive with the guidance of different software. A prevalent example of this phenomenon is the rise of car-sharing applications such as Uber, Grab, Lyft, and other titles with similar concepts. Although, the emergence of these services also brought upon issues of driving safety and usability. There are possible driving safety concerns emphasized by the trend of car-sharing software, particularly on the usage of mobile phones while driving a car. An example of a safety concern is how the youth are more prone to using mobile phones while driving than the elderly. To emphasize this, the source journal discusses the fact that young drivers are significantly more vulnerable to car accidents [1].

The previously mentioned scenario brings a concern to driving, particularly on the car-sharing businesses that recently emerged. Since the importance of age and familiarity with technology had been identified as an issue, the study by Chunhui Jing et al. analyzes how the age and experience of the drivers would have significant effects on the usage of the application. The research aims to spread awareness and address the drawbacks and problems regarding driving safety that have surfaced because of the car-sharing economy.

The researchers have decided to focus on this study, for it affects large amounts of people, especially those who rely on these car-sharing applications as a means of transportation. The risk of accidents and the more vulnerable youth had already been declared more prone to using mobile phones while driving, thus leading to accidents. The researchers have decided to focus on how the elderly aged drivers perform and compare the experienced to the inexperienced elderly drivers in using the car-sharing software.

Methodology

A simple 7-step procedure for Hypothesis Testing is used to test if the difference between the actual mean interaction time for the old-exp population and old-inexp population at a significance level of \(\alpha = 0.05\). The acquired data from the research article is the following: the sample size for both of the Old-experienced and Old-inexperienced sample is \(12\), the sample mean of the Old-experienced for T3(driving) is \(\bar{x_1} = 23.13\) with a standard deviation \(s_1 = 6.60\), while that of the Old-inexperienced is \(\bar{x_2} = 12.20\) with \(s_2 = 2.91\).

T3 (driving) - destination search: the participant immediately checked the route information on the phone screen, and data collection began when driving as he heard “Look.” The destination was off-screen, so the participant should move the marked destination icon to the screen (peeping window) by dragging or zooming (based on personal habits). After confirming the information, the participant reported “OK,” and the data collection was finished. A relatively high level of finger and visual interactions is required by this task.

Since two different samples are being compared and the population variances are not given, the researchers implement a statistical inference test for the difference in means when the variances of the corresponding populations are unknown and not assumed to be equal [2].

1. Parameter of Interest: The parameter of interest is the difference in interaction time of the Old-experienced sample and the Old-inexperienced sample under the T3(driving) trial, and that \(\Delta_0 = 0\).
2. Null hypothesis: \(H_0 : \mu_A - \mu_B = 0\) or \(\mu_A = \mu_B\)
3. Alternative hypothesis: \(H_1 : \mu_A > \mu_B\). The \(H_0\) is rejected if the Old-inexperienced population has a lower or faster interaction time of finding their passenger’s destination than the Old-experienced population under identical conditions.
4. Test Statistic: The formula for the test statistic is given as: \[\begin{aligned} t_0^* = \displaystyle\frac{\bar{x_1} - \bar{x_2} - \Delta_0}{\sqrt{\displaystyle\frac{s_1^2}{n_1} + \displaystyle\frac{s_2^2}{n_2}}} \end{aligned}\]
5. Reject \(H_0\) if: The degrees of freedom on \(t_0^*\) are found to be \[\begin{aligned} v = \displaystyle\frac{(\displaystyle\frac{s_1^2}{n_1} + \displaystyle\frac{s_2^2}{n_2})^2}{\displaystyle\frac{(s_1^2/n_1)^2}{n_1-1} + \displaystyle\frac{(s_2^2/n_2)^2}{n_2-1}} \end{aligned}\] with values: \(n_1 = n_2 = 12\), \(s_1 = 6.60, s_2 = 2.91\) Substituting these values: \[\begin{aligned} v = \displaystyle\frac{(\displaystyle\frac{6.60^2}{12} + \displaystyle\frac{2.91^2}{12})^2}{\displaystyle\frac{(6.60^2/12)^2}{12-1} + \displaystyle\frac{(2.91^2/12)^2}{12-1}} = 15.1210758 \approx 15 \end{aligned}\]

With a significance level of \(0.05\) and \(15\) degrees of freedom, the resulting critical \(t\)-value is \(1.752129\). Therefore, the \(H_0\) is rejected if \(t_0^* > t_{0.05, 15} = 1.752129\)

Results

Interaction Time

Table 1: Descriptive statistics of mean and standard deviation of interaction time among different ages and experience. Chunhui Jing et, al, “Impact of Driver Age and Experience in Software Usage on Driving Safety and Usability of Car-Sharing Software”, Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/6633379

The researchers gathered the data from Old-experienced and Old-inexperienced driver’s Interaction Time in the Category T3(Driving). The mean of the interaction time of Old-experienced participants is \(\overline{x_1}=23.13\) and a standard deviation of \(s_1=6.60\) which is higher than the mean of the interaction time of the Old-inexperienced participants \(\overline{x_2}=12.20\) with a standard deviation of \(s_2=2.91\).

Figure 1: Vertical Error Bar of the Means of the Interaction Time of Old-experienced Drivers and Old-Inexperienced Drivers

Figure 1: Vertical Error Bar of the Means of the Interaction Time of Old-experienced Drivers and Old-Inexperienced Drivers

The formula for the hypothesis test for difference in means and variances unknown is used for this study.

6.Computation

\(n_1 = n_2 = 12\)

\(s_1 = 6.60, s_2 = 2.91\)

\(\bar{x_1} = 23.13, \bar{x_2} = 12.20\)

\(\Delta_0 = 0\)

Substituting these values to get \(t_0^*\): \[\begin{aligned} t_0^* = \displaystyle\frac{23.13 - 12.20}{\sqrt{\displaystyle\frac{6.60^2}{12} + \displaystyle\frac{2.91^2}{12}}} = 5.249184066 \end{aligned}\]

Therefore, using \(\alpha=0.05\) and a fixed significance level test, the researchers reject the \(H_0 : \mu_A - \mu_B = 0\) if \(t_0^{*}>t_{0.05,15}\) in this case: \[t_0^{*}>t_{0.05,15}\] \[5.249184066>1.752129\]

7. Conclusion:

Recalling the condition for rejecting \(H_0\), \(t_0^* > t_{0.05, 15}\), the \(t\)-value for \(\alpha = 0.05\) with \(15\) degrees of freedom is computed to be \(1.752129\).

Since \(t_0^* = 5.249184066\) is greater than \(t_{0.05, 15} = 1.752129\), there is sufficient evidence to reject the null hypothesis that there is no difference between the mean interaction times of the Old-experienced and Old-inexperienced.

Discussion

Based on the findings that that were gathered by the researchers through the data taken from the original research, the mean interaction times of the old-experienced and the old-inexperienced have differences between them. From the graph, the drivers from the old-experienced group have higher interaction times than those from the old-inexperienced group. This shows that the old-inexperienced group are better equipped when dealing with car-sharing applications. This group also has the better ability in performing the tasks intertwined with finding the passenger’s destination. From these, it can also be inferred that the car-sharing applications are not as efficient for the population of drivers due to the long interaction time. The optimization of these would greatly benefit the interaction process of drivers, especially the elderly.

Conclusion

The study held and conducted by Chunhui Jing et al. shows the impact of car-sharing applications on people’s safety while driving. Analyzing this study further would benefit people in the current generation especially for those who manage businesses regarding these car-sharing applications as well as those who often depend on these platforms as their daily means of transportation. With this, the researchers concentrated on specific data from the study to compare the driving performance between elderly drivers who were experienced and inexperienced in using the car-sharing software. The researchers used different statistical methods such as hypothesis testing and getting the t-value to further analyze the data.

Through these methods, the researchers were able to conclude that there was enough evidence to reject the null hypothesis made. Rejecting the null hypothesis meant that there was a significant difference between the mean interaction times of the old drivers who are inexperienced and experienced in using the car-sharing software.

These new findings from analyzing the specific data shows how one’s experience in using car-sharing applications do affect the entirety of their performance in driving. Despite this, the results discussed have also shown that the inexperienced group was able to perform better in terms of their main tasks and finding the passenger’s destination. In its entirety, what’s most practical is to focus more on arriving safely to the right destination while maintaining proper and warm reception between the drivers and the passengers. There is still more room for improvement to possibly see a more significant difference between the data. Further studies regarding this topic can be recommended– the influence of gender or hospitality of the drivers–can help create widely accurate results that may help those who are in scope of the car-sharing services and community.

References

[1] M. F. Lesch and P. A. Hancock, “Driving performance during concurrent cell-phone use: are drivers aware of their performance decrements?” Accident Analysis & Prevention, vol. 36, no. 3, pp. 471–480, 2004.

[2] D.C. Montgomery & G.C. Runger, 10 Statistical Inference for Two Samples. In Applied Statistics and Probability for Engineers (7th ed.), 2019.

Research Article: C. Jing, J. Zhi, S. Yang, and W. Wang, “Impact of Driver Age and Experience in Software Usage on Driving Safety and Usability of Car-Sharing Software,” Journal of Advanced Transportation, 21-May-2021. [Online]. Available: https://www.hindawi.com/journals/jat/2021/6633379/. [Accessed: 29-Jul-2021].