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Discussion and future directions

According to the results, it is seen that the developed system is mainly based on expert’s knowledge, which can be very useful in places where the real time communications infrastructure is not a practical reality or not affordable. This intelligent navigation system supports the user with all the criteria of choosing one route rather than another which helps in overcome any error of decision making when choosing a route due to the stress resulted from time constrain.

Furthermore, this system is cheaper and more sustainable option than real time traffic flow sensor systems, thus it can be affordable by most of the countries. An evaluation of this system and other navigation systems which are supported by real time traffic data should be carried for more accurate comparison. Furthermore, this system should also be evaluated by the drivers themselves in order to test it and get feedbacks. It is mainly based on assigning different weights to roads according to various rules of times and places conducted from the experts’ knowledge.

These weights consider the reasons that affect the speed of the ambulance vehicles and thus affect the response time to an accident. The different values of weights are assigned by prioritising which reason might affect more on the vehicles’ speed from the experts’ point of views. Other road network rules were also added to this system which includes speed limits, restricted access, and one way streets. Several scenarios were set in order to examine this system within ArcView.

The results of these scenarios show that the fastest route between two points is re-routed to follow other fastest routes in order to avoid predicted congested streets in certain times and/or places. The fastest route calculation in this research is based on multiplying the response time and weight for each street in the network within ArcView Network Analyst. The fastest route from this system was compared with Dell Navigation System as a norm system, in order to find the differences in choosing the optimal route.

Although this evaluation did not provide us with information as to which of these systems is more accurate, it did however provide an overview of choosing the quickest routes in both systems. The speed limits which were assigned to each street should be accurately collected in order to find accurate travel times along each street. This system collected the speed limits from literatures which do not provide the exact speed limits for each road. Another limitation is that this system used few road network rules.

For example, more rules should be added such as restricted turns at an intersections and setting overpasses and underpasses for the whole network. This system has acquired expert knowledge only from the literatures while interviews should be adopted along with literature especially for the prioritising procedure of the criteria in choosing one road rather than another. For the purpose of this research many attempts to meet ambulance drivers from East Midlands Ambulance Service (EMAS), which includes Leicestershire, were made but unfortunately there was no response.

The system is better evaluated by comparing it to systems supporting with real time traffic sensors rather than a norm system which was used in this project. This evaluation along with real field driving tests would provide an accurate comparison between the two systems. Furthermore, this system should be also evaluated by the drivers themselves in order to test it and getting true feedbacks. This system would be more efficient if was programmed with C++ or other programming languages mainly for two reasons.

First, it would be possible to update which travel time cost field to be activated in certain time by synchronising the time of the developed intelligent system to the PDA operating system timing in order to find which route is predicted to be congested. Second, it would be easier to integrate the rules from the expert knowledge by using IF and THEN in C++ or as called rule based navigation system. After this, the quickest path can be calculated using Dijkstra’s algorithm that is used in ArcView’s Network Analyst. This system shows that using ambulance drivers’ knowledge assists in building an intelligent system using GIS.

Interviews of the interviews of ambulance drivers as the primary source of expert knowledge and existing literature as the secondary source, helps for building rules to explore the methods for choosing the optimal routes in their opinions. The weighted travel time of each road can be calculated by giving different weights to the streets depending on different factors that affect the choice of one route over the other. The fastest route is then calculated using ArcView’s Network Analyst which makes use of the rules which were set up using the expert knowledge.

This system helps the ambulance drivers in overcoming the decision making errors due to the stress because of time constraints to reach a location, by considering all the factors that a drivers usually think about while choosing one route over another. The present intelligent system would be more convenient when build in C++ programming language and integrated within a ArcView, especially when applying time rules by synchronising the time between the expert system and the operating system of the hardware such as PDA.

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