Automation and the Human Factors Race to Catch Up

Automation has been rapidly developing into a pervasive part of our every day lives. Although I agree with Kaber’s original article, I argue that human factors as a discipline is not keeping up with the pace of technological change. Human factors researchers must rapidly embrace the development of richer automation models, more complex laboratory studies, and naturalistic studies in the field to generate relevant insights into human automation interaction. The corresponding development of massive data collection presents an opportunity for a more data-driven approach to understanding human automation interaction and human factors in general.

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High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽

10.3390/s21020464 ◽

2021 ◽

Vol 21 (2) ◽

pp. 464

Author(s):

Wei Ma ◽

Sean Qian

Keyword(s):

High Resolution ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Data Driven ◽

Traffic Information ◽

Flow Density ◽

Massive Data ◽

Market Penetration ◽

High Resolution Data ◽

Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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Determining the most influential human factors in maritime accidents: A data-driven approach

Ocean Engineering ◽

10.1016/j.oceaneng.2020.107588 ◽

2020 ◽

Vol 211 ◽

pp. 107588

Author(s):

Andrea Coraddu ◽

Luca Oneto ◽

Beatriz Navas de Maya ◽

Rafet Kurt

Keyword(s):

Human Factors ◽

Data Driven ◽

Data Driven Approach

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A data-driven approach to monitoring data collection in an online panel

Longitudinal and Life Course Studies ◽

10.1332/175795919x15694136006114 ◽

2019 ◽

Vol 10 (4) ◽

pp. 433-452

Author(s):

Jessica M.E. Herzing ◽

Caroline Vandenplas ◽

Julian B. Axenfeld

Keyword(s):

Data Collection ◽

Multilevel Models ◽

Data Driven ◽

Response Proportion ◽

Panel Surveys ◽

Online Panel ◽

Response Enhancement ◽

Daily Response ◽

Data Driven Approach ◽

Collection Phase

Longitudinal or panel surveys suffer from panel attrition which may result in biased estimates. Online panels are no exceptions to this phenomenon, but offer great possibilities in monitoring and managing the data-collection phase and response-enhancement features (such as reminders), due to real-time availability of paradata. This paper presents a data-driven approach to monitor the data-collection phase and to inform the adjustment of response-enhancement features during data collection across online panel waves, which takes into account the characteristics of an ongoing panel wave. For this purpose, we study the evolution of the daily response proportion in each wave of a probability-based online panel. Using multilevel models, we predict the data-collection evolution per wave day. In our example, the functional form of the data-collection evolution is quintic. The characteristics affecting the shape of the data-collection evolution are those of the specific wave day and not of the panel wave itself. In addition, we simulate the monitoring of the daily response proportion of one panel wave and find that the timing of sending reminders could be adjusted after 20 consecutive panel waves to keep the data-collection phase efficient. Our results demonstrate the importance of re-evaluating the characteristics of the data-collection phase, such as the timing of reminders, across the lifetime of an online panel to keep the fieldwork efficient.

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