Novel application of red-light runner proneness theory within traffic microsimulation to an actual signal junction.
Building on previous research a conceptual framework, based on potential conflicts analysis, has provided a quantitative evaluation of 'proneness' to red-light running behaviour at urban signalised intersections of different geometric, flow and driver characteristics. The results provided evidence that commonly used violation rates could cause inappropriate evaluation of the extent of the red-light running phenomenon. Initially, an in-depth investigation of the functional form of the mathematical relationship between the potential and actual red-light runners was carried out. The application of the conceptual framework was tested on a signalised intersection in order to quantify the proneness to red-light running. For the particular junction studied proneness for daytime was found to be 0.17 north and 0.16 south for opposing main road approaches and 0.42 east and 0.59 west for the secondary approaches. Further investigations were carried out using a traffic microsimulation model, to explore those geometric features and traffic volumes (arrival patterns at the stop-line) that significantly affect red-light running. In this way the prediction capability of the proposed potential conflict model was improved. A degree of consistency in the measured and simulated red-light running was observed and the conceptual framework was tested through a sensitivity analysis applied to different stop-line positions and traffic volume variations. The microsimulation, although at its early stages of development, has shown promise in its ability to model unintentional red light running behaviour and following further work through application to other junctions, potentially provides a tool for evaluation of alternative junction designs on proneness. In brief, this paper proposes and applies a novel approach to model red-light running using a microsimulation and demonstrates consistency with the observed and theoretical results.
School of Civil Engineering and Geosciences, Newcastle University, Cassie Building, Newcastle Upon Tyne NE17RU, UK. margaret.bell@ncl.ac.uk
Margaret Carol
MC
Bell
Fabio
F
Galatioto
Tullio
T
Giuffrè
Giovanni
G
Tesoriere
D001334:Automobile Driving / Q000523:psychology*; D001336:Automobiles; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D008960:Models, Psychological; D008962:Models, Theoretical; D016012:Poisson Distribution; D015203:Reproducibility of Results; D012309:Risk-Taking; D012449:Safety; D014505:Urban Population; D014796:Visual Perception / Q000502:physiology*