Exploring the Informal Communication of Driver-to-Driver on Roads: A Case Study of Durban City, South Africa

Abstract

Road traffic fatality is rated as one of the ten causes of death in the world and with various preventive measures on a global level, this prediction is only placed on flat terrain and didn’t reduce. Nevertheless, road users’ communication is an essential key to traffic safety. This communication, be it formal or informal between the road users is an important factor for smooth traffic flow and safety. Communication language on roads can be categorized into; formal device-based signal (formal signal), formal hand signal (formal signal), informal device-based signal (informal signal), and informal gesture-based signal (everyday signal). However, if the intent of the message conveys is not properly understood by the other road user, mistakes and errors may set in. Overall, the formal signal is based on explicit learning which occurs during the driving training and the license testing process and the informal, implicit learning occur during the actual driving process on the road unintentionally. Furthermore, since the informal signal is not a prerequisite to driving or taught in driving schools, novice drivers are clueless and thus, might have contributed to errors and mistakes which leads to traffic fatalities. Therefore, this study seeks to document the informal means of communication between drivers on South African roads. Consequently, a qualitative semi-structured interview questionnaire would be used in the collection of informal signals, which were predominantly used on South African roads from driving instructors and thereafter, a focus group of passengers’ car, commercial and truck drivers will be used to validate the availability and their understanding of these informal signals using a Likert-type scale for the confidence level. In conclusion, the information gathered from this study will help improve road safety and understanding of road users especially drivers on the necessity of communication and possible adaptation for other developing countries.

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

“…Also, road traffic accidents contribute to huge financial losses in term of infrastructure damage, loss of productivity, road accident fund payouts for any country and the individual involved [4][5][6]. With so many losses, the reduction/prevention of traffic fatalities or accidents is one of the factors in attaining global sustainability goals and becoming a priority in transportation management [7][8][9]. At any road accident, an accident report is collected; this report covers different accident attributes that can be used to further investigate the possible cause of the accident at that particular road section.…”