Time pressure on ambulance drivers Essay Example for Free

Time pressure on ambulance drivers Essay Ambulance drivers are required to make decisions on which route to follow under time constraints because every second is counted to save a life. Timmons, 2007 indicated that â€Å"Decision makers are susceptible to cognitive biases when operating under stress, i. e. , high workload, time pressure, and information ambiguity†, pp4. A cognitive bias is defined by Haselton et al. (2005) as the tendency to search for information and alternatives that prove their preconceptions and to discount information that disproves their preconceptions. Kowalski-Trakofler et al. (2003) carried a study to discuss human judgement and decision making under stress. The authors selected recent literatures and carried out a field work to discuss the affect of stress on emergency responders. They examined coping with stress under time constrains on expert emergency teams. It was concluded from this research that â€Å"stress restricts cue sampling, decreases vigilance, reduces the capacity of working memory, causes premature closure in evaluating alternative options, and results in task shedding†, ( Kowalski-Trakofler et al. 2003, p282). They have mentioned a study that identifies emergency decision makers’ behaviours under stress. This study concluded that these people under stress â€Å"not only have the effects of their own stress response and its resulting consequences, the information they must base their judgments on is often unclear, faulty and incomplete†, p. 283. The over all conclusion of this study was that the stress under time pressure narrows the decision maker focus whether working individually or in groups Impact of traffic congestion on response time: 2. 7 Shortest path algorithm Shortest paths’ calculations are unavoidable in road network analysis applications including emergency medical services such as ambulance navigation systems, (Liang, 2005). The shortest path (or minimum weight path) is defined as calculating the least total distance weight or least time weight paths between two locations (Derekenaris et al. , 2000). The quickness of calculating the shortest route for EMS is essential to reduce the respond time needed to route the ambulance vehicles from the dispatch location to the incident scenes (Liang, 2005). Liang pointed out that the problem arises when finding the shortest routes in big urban cities which contain huge road networks that are associated with massive amount of real world roads information(such as traffic information, name of roads, etc) and associated with the available capabilities of the hardware for example the amount of memory used to run this algorithm. Engineer (2001) considered two systems that controls and calculate the shortest routes in EMS. First, a centralised system which runs by ambulance control centre personnel, while the other system is called decentralised system in which the shortest path is calculated on board of the vehicles and this system is usually have limited memory and storage capabilities. He mentioned that an optimal algorithm to find the shortest path in less time is essential especially for decartelised system Zhan and Noon (1998) distinguished three types of shortest path algorithms which are single pair, single source and all pairs shortest paths algorithms. Single pair calculates the shortest path between two points in a network, while single source algorithm calculates the shortest path from a point to all other points in a network and finally the all pairs algorithm calculates the shortest path between every pair of points. Borri Cera (2005) explained single pair shortest path algorithm by assigning a first point and second one on a road network. It is possible to calculate the shortest distance between these points by minimising the value of distance linked with each point on the network (also known as impedance) taking into consideration that the a distance variable is associated with each point on the network. They also pointed out that the shortest path can be calculated according to different variable rather than distance one, such as â€Å"time or monetary cost†, pp 954. There are many algorithms for solving shortest paths which have been formulated over the years as a result of different research studies in different fields such as geography, computer sciences and transportations (Goldberg Radzik, 1993). However, there are three main used algorithms, which are Dijkstras algorithm, Restricted Search Algorithm and A* Search algorithm. ArcView Network Analyst extension uses a modified Dijkstar’s algorithm that does not only finds the shortest path from one point not another but also it was built to facilitate quick access to the topology of the network data (ESRI help, 1992). In addition, the modification includes a custom memory to manage and deal with very large networks.