This project focuses on the risk to the operatives and the public arising from hard-shoulder incursions on motorways, which are defined as the temporary violation of this lane by a vehicle travelling on the nearside lane. Even though interest has been raised around safety when stopping on the hard-shoulder, there is no significant research conducted to investigate and quantify this risk.
In this EngD project, motorway hard-shoulder accidents were investigated individually from the main traffic lanes to explore the factors affecting their severity and likelihood and identify potential differences using discrete choice and time-series modelling techniques. Based on the safety triangle theory, it was assumed that eliminating the contributory factors for injury accidents would also minimise the risk of hard-shoulder incursions, which were used as a risk indicator. An observation-based survey was conducted to gain initial knowledge on the frequency of incursions within a motorway stretch and also basic conditions that may affect the severity as well.
Further to the survey, in order to collect hard-shoulder incursion data automatically, potential vehicle detection solutions were investigated. A radar sensor-based system was identified as the most suitable for this purpose and was adapted to suit the project s requirements. The sensor was installed on a motorway site, following a series of requirements to ensure safe and effective deployment. The data collected from the radar sensor were processed to minimise the errors and then corresponded to the traffic related and environmental data available for the same period of time.
Using the Generalised Linear Autoregressive Moving Average model, the final models developed provided the factors that mostly affect the occurrence of hard-shoulder incursions. The main factors are temperature, humidity, traffic composition and average speed on the main carriageway. Using these models it is possible to quantify the risk and forecast when this will be minimised at a particular motorway section at any time. The risk is estimated according to the explanatory variables proposed, by inputting the predictions of these conditions in the model. This model is a tool that may then allow the operatives to be deployed on the network in the safest manner, according to the levels of tolerable risk
