Understanding the relationship between road users and the roadway infrastructure in Ghana

• Published in: Accident analysis and prevention 2024-04, Vol.198, p.107475-107475, Article 107475
• Main Authors: Lawton, Brianna P., Hallmark, Shauna L., Basulto-Elias, Guillermo, Obeng, Daniel Atuah, Ackaah, Williams
• Format: Article
• Language: English
• Subjects: Africa; Crash fatalities; Driver behavior; LMIC; Road accidents; Road traffic safety; Rural road safety
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2024.107475

•Commercial vehicles were the most likely to make a full stop and stop at the stop bar.•Correlations between roadway infrastructure (e.g. lighting) illustrate countermeasures with more positive safety impacts.•Drivers were much more likely to stop at the stop sign/bar compared to junctions without any additional safety measures.•Driver behavior data helps build a robust road safety database, improve policy, and create innovative informed initiatives.•Stopping behavior likelihoods can avail in selecting tailored solutions and predict the economical effectiveness of them. Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding driver behavior at unsignalized junctions is particularly important since failure to stop or yield can seriously affect vulnerable road users. The study’s objectives were to develop relationships between driver behavior and junction characteristics. Understanding the characteristics that lead to determining what factors influence a driver’s behavioral response at rural junctions provides information for policy makers to determine the best strategies to address these behaviors. The study evaluated stopping behavior at rural junctions. Driver behavior was extracted from video views of ten junctions in the Ashanti Region of Ghana. A total of 3,420 vehicles were observed across all ten junctions during data collection before any analysis was conducted. The type of stop was selected as a surrogate measure of safety. Logistic regression was used to model stopping behavior at the selected junctions. The analysis showed drivers were more likely to stop when going straight (versus a left turn) and left turning vehicles were more likely to stop than right turning vehicles. Additionally, single unit trucks and tro-tros were more likely to stop than other vehicle types. Drivers were also much more likely to stop when channelization, intersection lighting, or speed humps were present. Drivers at junctions with 4-approaches were also more likely to stop than those with 3 approaches. The results from this research contribute valuable information about what factors contribute to positive safety behav