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Serving and saving lives are the most important and critical responsibilities that most of the government agencies are looking to improve. One of the most important issues is that the ambulance vehicles should reach the incident site in a shortest time to provide the help needed in order to reduce the loss of lives in our communities by reducing the response time. For example, if a person has a heart attack or any accident and the CPR(Cardiopulmonary Resuscitation) is started within four minutes, the patient’s chance of survival are almost four the times greater than if the patient did not receive the CPR until four minutes (Barr, 2005).

Ambulance vehicles must follow the fastest route rather than the shortest one to reach accidents’ areas because shortest path might not be the fastest. Many variables might affect the response time, for example the shortest path might pass a residential or commercial areas and the path might face traffic jams in certain hours (Echols, 2003) In Vehicles Navigation Systems IVNS, are used to guide the ambulance vehicles drivers by following the quickest route from the dispatch location to the incident location.

Some navigation systems are supported by real time data of the current traffic conditions of the roads for more accurate routing. Traffic sensors are used to collect the traffic data then transmit it to the control centre which is then processed and transmitted to the navigation systems mounted in vehicles by using other technologies such as wireless communication system (Yue & Yeh, 2005). According to them there are mainly two types of traffic sensors.

The first type is mounted on moving vehicles which collect the data as they are moving on streets. The second one is situated on both sides of a road, buried under the roads or sensors and cameras situated on traffic signals and on signposts Although real-time data are important but the equipments used are very expensive and each survey taken using such a data involves very high operating costs (Nual et al. 2002 & Balke et al. 2005).

Thomson (2003) states that the real time data that are collected from IVNS depends on fixed number of vehicles, and hence the data being collected only covers limited number of roads. Also the results provided by real time traffic collection might be biased towards the biased results of traffic condition. For example, buried and road side sensors can only provide information about the spots that they are situated on, thus the data collected does not represent the real traffic state on the entire road (Haas et al. , 2001).

Thompson (2003) also says that this type of system is labour intensive and requires trained technicians, for which extra money must be provided (Thompson, 2003). Finally, as Borri and Cera (2005), suggest it would be is difficult to record the change of traffic conditions throughout the day using this method. Jaons (1993) gave a valid issue when he pointed out that the ambulance vehicles’ drivers face stressful conditions due to time constraints, which often causes errors in decision making and affect their abilities of judgement.

Kowalski-Trakofler and Vaught, (2003) have said that this decision making could be related to which route to follow especially if there are many factors that would affect this decision. + 1. 2 Aims and Objectives The purpose of this research is to develop an intelligent routing system to find the quickest route using GIS for ambulance drivers. This system gathers expert knowledge from ambulance drivers and also from different literature sources, regarding the criteria of choosing a specific route rather than another. The system is important in two ways.

First, if compared to real-time data navigators, this system uses inexpensive data acquisition (labours, and equipments costs), thus it is affordable by all countries, and it is accurate (because it is based on the experiences of drivers facing such problems everyday). Second point is that this system is used to reduce the stress on drivers due to the time constraints which affect their decision making. This is done by integrating the choices of choosing the optimum route criteria by drivers with the shortest path algorithm within GIS.

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