Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
D2S2T3: Socio-technical Systems
Time:
Thursday, 24/Feb/2022:
1:30pm - 2:30pm

Session Chair: Hendrik Stern

External Resource:
Show help for 'Increase or decrease the abstract text size'
Presentations

Work Condition Analysis for Driving Professions with Big Data and Artificial Intelligence

Milke, Viola2; Strau, Sarah3; Debbing, Christina3; Keil, Maria1; Severin, Benedikt4; Hesenius, Marc4; Ruiner, Caroline3; Hagemann, Vera2; Klumpp, Matthias1

1Georg-August-University of Göttingen, Germany; 2University of Bremen, Germany; 3University of Hohenheim, Germany; 4University of Duisburg-Essen, Germany

Driving professions make up about 1.5 million employees within Germany alone. Though there are ample data especially regarding traffic, location and driving situations, estab-lished links towards the perceived working conditions are missing so far. The BMAS re-search project KARAT is addressing this by conceptualizing and testing big data analytics and the use of artificial intelligence for about 100 truck and train drivers in 2021 and 2022. Included data are for example Cortisol, EEG, heart rate, breathing and HRV - in order to combine this with driving and traffic as well as weather and event data for example.

This study is aiming to identify stress factors in order to analyzed and mitigate the per-ceived work strain of drivers. Preliminary finding includes the fact that a large number of factors and circumstances have potential impacts on the work and mental stress and strain situation of driving professions. This includes for example external customer rela-tions like delivery time windows and waiting times, corporate processes like shift and tour planning or the balancing of work regulation and driving regulation time require-ments for breaks and total work time. Time of day plays an important role as found in a first artificial intelligence model run for German truck accident data for example.

The research approach is to apply artificial intelligence for mental stress evaluation with driving professions. The long-term goal is to enable easy-to-use stress prediction tools in order to alleviate the work situation of one of the most hazardous work groups in logistics and in societies.



Assessing driver fatigue during urban traffic congestion using ECG method

Gyulyev, Nizami1; Galkin, Andrii1,2; Schlosser, Tibor2; Capayova, Silvia2; Lobashov, Oleksii1

1O. M. Beketov National University of Urban Economy in Kharkiv, Ukraine; 2Slovak University of Technology in Bratislava, Slovakia

The research paper analyse the level of stress and functional state of the drivers in urban traffic congestion. The model comprising of influence of traffic congestion on the functional state of the average driver, allows us to predict changes to the driver’s state depending on the age, the duration of the traffic congestion and initial state prior to congestion. The value of the initial functional state affects the driver's functional state during his/her stay in a traffic congestion in different ways. The rising of tension during staying in traffic jam is 10-12% after 7-10 minutes. The research uses system analysis for data analysis; electrophysiological methods in determining the functional state of the driver and mathematical statistics methods were used during the development of model for analysis of the functional state of the driver.



Technology review for guiding persons in airports and other hubs

Börold, Axel1; Broda, Eike2; Jathe, Nicolas1; Schweers, Dirk2; Sprodowski, Tobias1; Zeitler, Waldemar1; Freitag, Michael2

1BIBA; 2University of Bremen

The COVID-19 pandemic brought public life to a near standstill. Precautionary practices, such as face masks and safe distance, were established to protect people. In addition, various tracking methods were developed to detect possible contacts. In this paper, we review suitable technologies to indicate a solution for a people guidance system, which actively prevents these contacts by suggesting routes through large areas (e.g. airport terminals or train stations). By tracking the people and using destination information, e.g. from tickets, the system should be capable of calculating routes and visualise the suggestion to each person individually.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: LDIC 2022
Conference Software - ConfTool Pro 2.6.144
© 2001–2022 by Dr. H. Weinreich, Hamburg, Germany