Predictive Screening of Accident Black Spots based on Deep Neural Models of Road Networks and Facilities: A Case Study based on a District in Hong Kong

Abstract

The screening of road accident black spots is to predict accident prone locations in the road network, with the aim of preventing further accidents with remedial measures. As black spots are linked to a location, certain features of the location and its nearby branches of the network should be capable of explaining the black spots. Several open data sources now provide feature-rich road network and facilities datasets. This paper proposes a data-driven machine learning solution for black spot screening using features of road network and facilities. The accident neighborhood is a concept introduced in the paper that represents the nearby locations associated with the happening of accidents. The concept has been realized as graph embeddings of road network, which, together with a deep neural network classifier, are the two major components of the solution. An evaluation of the solution using data from a Hong Kong district indicates that recognition of both the surrounding road network structure and the local features near accident sites can yield accurate models for black spot prediction.