Network-level accident-mapping: Distance based pattern matching using artificial neural network.
The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods.
School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom. Electronic address: l.deka@lboro.ac.uk.
Lipika
L
Deka
School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom. Electronic address: m.a.quddus@lboro.ac.uk.
Mohammed
M
Quddus
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D004779:Environment Design; D006801:Humans; D008377:Maps as Topic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011336:Probability; D018570:Risk Assessment / Q000706:statistics & numerical data
Accident-mapping; Artificial neural network; Pattern-matching