Spatial Analysis of Accident Spots Using Weighted Severity Index (WSI) and Density-Based Clustering Algorithm

This study is based on evaluation of various factors that cause road accidents in Lagos Metropolis. Fourteen (14) factors/drivers were identified which tend to influence an occurrence of accident on Lagos roads. In order to study the pattern of accidents in the Metropolis, several different spatial and non-spatial datasets were collected, processed and analysed. Weighted Severity Index (WSI) was created based on these factors/drivers. Also, Density-based Clustering for Traffic Accident Risk (DBCTAR) was carried out to assist in ascertaining the distribution of Black Spots Severity (BSS). Results obtained include: shortestpath analysis, service area analysis, accident spot severity and vulnerability level across the Metropolis based on the Weighted Severity Index (WSI). The study reveals that, accidents are not always caused as a result of bad roads but by the smoothness of roads such as expressway with accident spots distance band of 300m-500m and 3.6% of the accidents is as a result of pedestrians avoiding the use of pedestrian bridges/aid even when they are available.Key Words: Accident Spots, Weighted Severity Index (WSI), Vulnerability, Pedestrians, Transportation

Improving data management through automatic information extraction model in ontology for road asset management

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry

A traffic accident risk mapping framework

Identifying traffic accident concentration area is important for road safety improvements. Previous spatial concentration detection methods did not consider the severity levels of accidents, and the final traffic accident risk map for the whole study area ignores the different users’ requirements. This thesis proposes an ontology-based traffic accident risk mapping framework. In the framework, the ontology represents the domain knowledge related to the traffic accidents and supports the data retrieval based on users' requirements. A new spatial clustering method, called DBCTAR (Density-based Clustering for Traffic Accident Risk), takes into account the numbers and severity levels of accidents is proposed for risk mapping. To demonstrate the framework and the new algorithm, the Ontology-based Traffic Accident Risk Mapping (ONTO_TARM) system and a web-based clustering service GeoClustering have been developed. Four case studies in the city of Calgary with final risk maps are presented and discussed