Dan Podjed and Saša Babič published an article, entitled 'Crossroads of Anger: Tensions and Conflicts in Traffic', in a special issue of ‘Ethnologia Europaea’ on rage and anger. The article focuses on drivers involved in various modes of personal transport in Ljubljana, and describes their interactions and conflicts, often resulting in verbal or nonverbal expressions of anger. Through an analysis of driving habits and reflections on daily language and the media, the article sheds light on some key questions, which have, so far, only briefly been discussed by anthropologists: How do people habituate their driving? How have vehicles become integral to our identity? And how do drivers express their feelings and emotions on the road?
COBISS.SI-ID: 39325997
Saša Babič and Dan Podjed prepared an article on the social meaning of vehicles for the Bulletin of Slovene Ethnological Society. As it is explained in the article, the vehicles are more than mere means of transportation, and they play an important role in the creation of stereotypes about individuals and communities. They define our social position and install us within the network of social connections and cross-gender relationships. The paper discusses some stereotypes about vehicles in connection to social power and cross-gender relationships, and compares attitudes to cars, bicycles, public transportation, and other forms of mobility in the cities of Ljubljana and Belgrade.
COBISS.SI-ID: 40102189
As is the case with most cities, Ljubljana is faced with numerous traffic-related problems that overwhelm the city’s infrastructure and represent a threat to the environment as well as people’s health. The paper, prepared by DriveGreen's researcher Tatiana Bajuk Senčar fort he Bulletin of the Slovene Ethnological Society, presents a short study on conceptions of comfort in light of the changes made to parking infrastructure and parking rates, focusing on the introduction of the P+R (Park and Ride) system and its influence on drivers’ mobile practices.
COBISS.SI-ID: 42407981
In the article 'Smart Driving: Influence of Context and Behavioral Data on Driving Style’, published in Internet of Things, Smart Spaces, and Next Generation Networks and Systems (Conference Proceedings), the authors present an approach to determine stress level in a non-invasive way using a smartphone as the only and sufficient source of data. They also present the idea of how to partly transfer such approach to the determination of the driving style, as aggressive driving is one of the causes of car accidents. For determination of the driving style a variety of methods are used including the preparation movements before maneuvers, identification of steering wheel angle, accelerator and brake pedal pressures, glance locations, facial expressions, speed, medical examinations before driving as well as filling out of the questionnaires after the journey. In this paper, the authors present a methodology for estimation of potentially unsafe driving (in the meaning of more intensive acceleration and braking compared to average driving) and discuss how to estimate such unsafe driving before it actually takes place. They present sensors and data which can be used for these purposes. Such data include heart rate variability from chest belt sensor, behavioral and contextual data from smartphone, STAI short questionnaire to assess personal anxiety and anxiety as a state at certain moment, and initial interaction with car during opening and closing of the car doors. To determine intensive acceleration and braking they analyzed GPS data like speed, acceleration and also data from accelerometer inside the car to avoid interference in GPS-signal. The long term goals of the study are to provide feedback about potentially unsafe driving in advance and thus strengthening driver’s attention on the driving process before the start.
COBISS.SI-ID: 11566676
The paper 'Estimation of the Driving Style Based on the Users’ Activity and Environment Influence’, published in Sensors journal, describes an experiment which includes the evaluation of a self-assessment of the driving style, and a prediction of aggressive driving style based on drivers’ activity and environment parameters. Sixty seven hours of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users’ activity. The driving style was predicted from the user’s environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user’s environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts.
COBISS.SI-ID: 11867476