This paper presents a freeway incident detection model that was developed using fractal dimension analysis of speed and occupancy data. The algorithms developed in this study were based on the notion that traffic parameters upstream of incidents and bottlenecks show substantial irregular behaviour when compared with downstream conditions. Fractal dimension analysis was used to provide a measure of the irregularity in traffic parameters. A number of fractal models based on a combination of smoothing and recursion tests on speed and occupancy data were developed in this study. The best performing model was identified as that implementing smoothed fractal speed and occupancy inputs based on data collected from dual loop detectors embedded in the pavement of the freeway. The results demonstrate the feasibility of using fractal dimension analysis for incident detection. An evaluation of the model's performance against a number of other models also show that smoothed fractal models outperform a comparative (California) incident detection model that was developed using the same data set. The model is, however, outperformed by a neural network model. a).
Samenvatting