A stochastic concept for highway capacity analysis is presented. The capacity of a highway facility is regarded as a random variable instead of a constant value. Thus, the stochastic approach provides new measures of traffic flow performance based on aspects of traffic reliability. A method for the estimation of capacity distribution functions from empirical data based on statistical methods for lifetime data analysis is introduced. This method is derived for the analysis of freeway capacity. However, the stochastic approach also is shown to be applicable to intersections. Results of the analysis of data samples from freeway sections in Germany indicate that freeway capacity is Weibull-distributed with a considerable variance. A Monte Carlo technique based on the stochastic description of capacity is proposed to quantify freeway traffic performance over a whole year. This technique also provides a quantitative assessment for oversaturated conditions.

Motorways represent seven per cent of the urban arterial road network in Melbourne yet carry 40 per cent of the urban arterial road travel in terms of vehicle kilometres travelled and this percentage is growing. The number of casualty crashes on metropolitan Melbourne motorways has increased over the decade at a faster rate than on other urban roads in metropolitan Melbourne. Police crash reports more often attribute crash cause to traffic conditions and vehicle interactions rather than infrastructure. As urban motorways are generally built to the highest standards, a new way of looking at motorway safety is needed. This led to the formulation of a hypothesis that the dynamics of the traffic flow are a significant contributor to casualty crashes on urban motorways. To test this hypothesis, in-depth analysis was undertaken on metropolitan Melbourne motorways. Crash data was linked to traffic data including vehicle occupancy (a proxy measure for density), vehicle speed and flow. Occupancy was used to categorise the ‘traffic states’ ranging from free flow to flow breakdown (congestion). Applying a Chi Square Goodness of Fit Test to the linked showed a statistically significant association between traffic state and crashes, with a higher than expected crashes in the traffic states where flow breakdown is relatively certain or has occurred. The results of this analysis can be used to improve safety on urban motorways through the development of Intelligent Transport System strategies to keep the motorway operating at conditions that minimise flow breakdown risk.

The purpose of this article is to present insights into the relationship between complex traffic flow phenomena on urban motorways and crash risk. Unstable or congested flow can trigger low speed/high density clusters (e.g. nucleations or shockwaves) creating ‘surprise elements’, therefore sharply increasing the cognitive workload for motorists. When combined with reduced road space and freedom to perform needed manoeuvres (e.g. lane changes), conditions can exceed the physical or mental capability and hence increase the likelihood of human error. There is overwhelming evidence that high traffic density drastically increases the crash risk. Some density concentrations can be avoided through appropriate planning and real-time traffic control, resulting in a reduction in crashes. Modern measurement devices allow for the analysis of individual vehicle behaviours such as ‘Brake’, ‘Speed alert’ or ‘Lane change’ events and show promise in providing robust data to further exploring what makes dense traffic complex. This allows establishing relationships between “events as elementary units of exposure” and crash occurrence resulting in a new way of understanding crash rates. These relationships are important to predict crashes, identify high-risk locations, and establish suitable measures for crash reduction.

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