A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.     

基于智能手机比色测定水环境中的六价铬

在酸性条件下,水样中的六价铬能够和显色剂二苯碳酰二肼反应生成紫红色络合物,该络合物溶液用自制的数字图像比色装置测定颜色值实现六价铬的现场快速检测。该方法在0.05 mg/L~1.00 mg/L范围内线性良好,方法检出限为0.02 mg/L,标准溶液10次测定结果的RSD为1.6%~7.9%,实际水样加标回收率为94.7%~102%。用该方法与国标法同时测定实际水样,结果无显著差异。     

On the potential of iPhone significant location data to characterize individual mobility for air pollution health studies

● We evaluated the accuracy of iPhone data in capturing time-activity patterns. ● iPhone data captured the most important microenvironments and time spent in them. ● iPhone data also accurately captured daily exposure to ambient PM pollution. ● A considerable fraction of the population in the USA may have iPhone data available. ● iPhone data has great potential in air pollution health studies. In many air pollution health studies, the time-activity pattern of individuals is often ignored largely due to lack of data. However, a better understanding of this location-based information is expected to decrease uncertainties in exposure estimation. Here, we showcase the potential of iPhone’s Significant Location (iSL) data in capturing the user’s historical time-activity patterns in order to estimate exposure to ambient air pollutants. In this study, one subject carried an iPhone in tandem with a reference GPS tracking device for one month. The GPS device recorded locations in 10 second intervals while the iSL recorded the time spent in locations the subject visited frequently. Using GPS data as a reference, we then evaluated the accuracy of iSL data in capturing the subject’s time-activity patterns and time-weighted air pollution concentration within the study time period. We found the iSL data accurately captured the time the subject spent in 16 microenvironments (i.e. locations the subject visited more than once), which was 93% of the time during the study period. The average error of time-weighted aerosol optical depth value, a surrogate of particle pollution, is only 0.012%. To explore the availability of iSL data among iPhone users, an online survey was conducted. Among the 349 surveyed participants, 72% of them have iSL data available. Considering the popularity of iPhones, iSL data may be available for a significant portion of the general population. Our results suggest iSL data have great potential for characterizing historical time-activity patterns to improve air pollution exposure estimation.     

基于智能手机的环境监测光学传感器研究与应用进展

传统环境污染物生物化学分析技术通常依赖于实验室设备和专业的操作技术人员,限制了此类技术在环境应急等现场分析中的应用。以手机内置光学功能模块作为信号接收器的智能手机光学传感器,通过分析光信号可实现对目标物的定性或定量分析,其开发和应用是当前环境污染物现场快速生物化学分析检测领域的研究热点。综述了比色、荧光和化学发光3种智能手机环境监测光学传感器的传感原理、实现路径、监测指标、研究现状以及所面临的挑战,探讨了其在环境监测领域的典型应用,展望了未来的发展前景,以期为智能手机光学传感技术在环境监测领域的进一步发展提供参考。     

Key beliefs influencing young drivers’ engagement with social interactive technology on their smartphones: A qualitative study

Objective: The main aim of this study was to identify young drivers’ underlying beliefs (i.e., behavioral, normative, and control) regarding initiating, monitoring/reading, and responding to social interactive technology (i.e., functions on a Smartphone that allow the user to communicate with other people).

Method: This qualitative study was a beliefs elicitation study in accordance with the theory of planned behavior and sought to elicit young drivers’ behavioral (i.e., advantages, disadvantages), normative (i.e., who approves, who disapproves), and control beliefs (i.e., barriers, facilitators) that underpin social interactive technology use while driving. Young drivers (N = 26) aged 17 to 25 years took part in an interview or focus group discussion.

Results: Though differences emerged between the 3 behaviors of initiating, monitoring/reading, and responding for each of the behavioral, normative, and control belief categories, the strongest distinction was within the behavioral beliefs category (e.g., communicating with the person that they were on the way to meet was an advantage of initiating; being able to determine whether to respond was an advantage of monitoring/reading; and communicating with important people was an advantage of responding). Normative beliefs were similar for initiating and responding behaviors (e.g., friends and peers more likely to approve than other groups) and differences emerged for monitoring/reading (e.g., parents were more likely to approve of this behavior than initiating and responding). For control beliefs, there were differences between the beliefs regarding facilitators of these behaviors (e.g., familiar roads and conditions facilitated initiating; having audible notifications of an incoming communication facilitated monitoring/reading; and receiving a communication of immediate importance facilitated responding); however, the control beliefs that presented barriers were consistent across the 3 behaviors (e.g., difficult traffic/road conditions).

Conclusion: The current study provides an important addition to the extant literature and supports emerging research that suggests that initiating, monitoring/reading, and responding may indeed be distinct behaviors with different underlying motivations.     

Drowsiness and distraction while driving: A study based on smartphone app data

Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction.     

Post-trip safety interventions: State-of-the-art,challenges, and practical implications

Introduction: Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip. Method: The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation. Results: The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform. Conclusions: Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.     

“Like it’s wrong,but it’s not that wrong:” Exploring the normalization of risk-compensatory strategies among young drivers engaging in illegal smartphone use

Introduction: Young drivers are the most vulnerable road users and most likely to use a smartphone illegally while driving. Although when compared with drink-driving, attitudes to illegal smartphone risk are nearly identical, smartphone use among young drivers continues to increase. Method: Four in-depth focus groups were conducted with 13 young (18–25 years) drivers to gain insight into their perceptions of the risks associated with the behavior. Our aim was to determine how drivers navigate that risk and if their behavior shapes and informs perceptions of norms. Results: Three key themes emerged: (a) participants perceived illegal smartphone use as commonplace, easy, and benign; (b) self-regulatory behaviors that compensate for risk are pervasive among illegal smartphone users; and (c) risk-compensation strategies rationalize risks and perceived norms, reducing the seriousness of transgression when compared with drink-driving. Young drivers rationalized their own use by comparing their selfregulatory smartphone and driving skills with those of “bad drivers,” not law abiders. Practical Applications: These findings suggest that smartphone behaviors shape attitudes to risk, highlighting the importance for any countermeasure aimed at reducing illegal use to acknowledge how a young person’s continued engagement in illegal smartphone use is justified by the dynamic composition of use, risk assessment and the perceived norms.