A recurrent problem that affects emergency vehicles is reaching their destination on time. This is caused by increasing traffic and congestion in modern cities.
Especially in urban areas, the mix of road users and the presence of traffic lights, makes driving at a higher speed without causing any harm to anybody even more difficult for the emergency vehicle drivers itself. Currently, ambulance services across UK are facing issues in meeting the Government’s target of responding to 75% of life-threatening calls within 8 minutes (the Guardian, 2015), and this is becoming more complex year on year.
Although critical emergency calls are time-sensitive (i.e. heart attacks, strokes etc.), the BBC in 2015 reported a 116% rise in ambulance delays over the past year alone, proving the situation is becoming increasingly critical (BBC News, 2015). An international best practice review suggested that the response tine should be approx. 5 min. to increase the survival rate by 12% (NHS England, 2015).
Considering the above, the objective of the LiFE project is to reduce Emergency Vehicles (EVs) travel time in a safe manner through a route-based pre-emption strategy that applies:
The TSC’s role in the project is “high level modelling” (WP3), with the aim of reproducing realistic traffic behaviour in presence of EVs in microsimulation traffic modelling software, implementing traffic control strategies triggered by the section of the EVs; and modelling and analysing travel times under different traffic conditions, pre-emption strategies and road type.
Since the vehicles within a traffic modelling software answer to some built-in rules and behaviour dictated by the model, the first challenge encountered was to model the ability for the EV to overtake standard traffic, but also for the standard traffic to implement manoeuvres that are not be default implemented in microsimulation software. The solution we developed was an external driver model capable of overriding the behaviour embedded into the software and realistically model the traffic in an emergency.
Such external driver model was developed using PTV Visit 8.12 software and was calibrated on a Central Milton Keynes traffic model, and later validated on a Liverpool corridor.
So far, the base case model has been developed which is widely capable of replicating an average real traffic situation as can be seen by the validation results below.
Develop a traffic control strategy that by detecting the ambulance and its route in advance will automatically develop a new traffic light planning along the entire route, dependent on traffic conditions and congestion on the network: basically, a route based pre-emption strategy instead of link-based (bus-priority nowadays).
Develop an algorithm to calculate in real-time the ambulant Travel Time (TT), since the EV TT does not reflect the standard speed-flow traffic relationship. This will help develop the pre-emption strategies, as well as accurately predict the Ambulant travel time in the future.