Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.
The technology of autonomous vehicles is gaining more and more emphasis these days. In the near future the technological developments will make it possible for vehicles to travel on the roads without human intervention. However, downstream users have differing views on this new mode of transport. The aim of our research was to explore the opinions of different social generation groups and traffic groups about fully autonomous self-driving (SAE Level 5). In our research, we conducted an online self-report questionnaire survey. The questionnaire was completed by 223 people. The results were analyzed from several perspectives. The results showed that opinions and expectations in the field of autonomous vehicles differed by generation group, gender and primary mode of transport.
International data reports indicated that underreporting is a frequent phenomenon in the case of cyclist accidents. However, determining the exact volume of underreported accidents or injured cyclists is a difficult task. Our research focused on a comprehensive examination of the personal injury accident data of cyclists. The primary aim was to elaborate a model determining the proportion of injured cyclists ‘missing’ from the official accident database and to explore the possible causes and processes leading to that. With the use of various data sets (accident database, data from hospitals and survey of cyclists), the model can alleviate the distortion effects arising from conclusions reached using only one data source. To demonstrate applicability, the surveys and examinations were carried out in relation to Hungary. The results showed that only 8.3% of injured cyclists were reported in the official accident database. The majority (62.6%) of injured cyclists received neither police action, nor medical treatment. The volume of the underreporting varied depending on the injury outcome and type of accident.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.