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This scenario assumes that there is only one lane in the previous re-routed path and this street has two speed bumps thus it is expected that the speed of vehicles are reduced along this street. A weight of 1. 1 was given to this street. An alternative shortest route is calculated to avoid the routes that can cause time delays. In this case, the system has selected the same route as the one in scenario 2 because all alternative routes have longer travel time than the route in scenario 2 as seen in figure 7.

All other rules which have been acquired from the experts can also be added to the system in the same way as shown in the scenarios. Different weights are assigned according to the priorities of the rules suggested by the experts. Then each Weight field is multiplied by the travel time field to find weighted travel time costs. As a result of this procedure the least travel time route will be re-routed depending on the weights giving to the roads. This evaluation compares between the results from this system and another norm navigation system such as Dell/Navteq Navigation System embedded with Dell Axim X51v PDA as seen in figure 8.

The fastest (quickest) route between to points which are collected from both systems will be compared. The start point in Dell system represents the hospital while the destination point represents the incident. The start and destination points were added at intersection of streets in Dell navigation system. The quickest route from Dell was added to ArcView in order to visually compare between the two routes. The response time for Dell was approximately 3 minutes while the expert system was 3.

5 minutes as seen in figure 9. The response time difference might be due the different speed limits assigned to roads and/or different accuracy of road networks because of different road network providers. It can be seen from the results that the developed system being based on the expert’s knowledge is more practical and applicable to regions where real time communication infrastructure is not feasible like the poor and some developing nations where real time collection equipments are not affordable or not available.

This knowledge based intelligent system gives the choice to the user to choose a separate route rather than the defined one which would help in overcoming any errors of decision made in choosing a route due to stress resulting from time constraint in emergencies. Moreover, the system is much cheaper and more sustainable option than real time traffic flow sensor systems which can be afforded by most of the countries. The system is primarily based on the assignment of different weights to different roads depending on various rules of a particular time and place.

These weights can be assigned according to the expert’s knowledge. The basic considerations, that govern the assigning of these weights, are the reasons that affect the swift movement of ambulance vehicles and in turn affecting the response time to an accident. The values of weights are based on prioritising which reasons could be affecting the vehicles’ speed more as per the experts’ point of views. Some other common road network rules which were added in to this system were speed limits, restricted access, and one way streets.

As described in section 4 above, many of the scenarios were listed in order to examine this system within ArcView Network Analyst environment. The response and weight for each street within the system were taken as the basis for calculating the fastest route. The results of the considered scenarios have clearly shown that based on the prediction of congested streets in certain times and/or place, the fastest route between the two points was rerouted to other fastest route. The dell Navigation system was taken as the bench mark for evaluating the difference in choosing the optimal route.

Evaluation of our system did not gave us the ideal comparison with a real data from the roads which would be more accurate, but it did gave a general review of different methodologies of choosing the quickest route in both the systems. Limitations and future directions There are some constraints in the given methodology and evaluation which could not be taken care of mostly due to time constraints in completing this project. The system used only a few road network rules and some more rules could be added such as restricted turns on intersections and setting underpasses and overpasses for the whole network.

By this addition speed limits which have been assigned to each street could be accurately collected in so that accurate travelling times through each street could be accurately found. The sources of acquiring knowledge for the system were literature and interviews especially in prioritising the criteria in choosing one route over another. For the purpose of this research many attempts were made to meet the ambulance drivers from East Midlands Ambulance Service (EMAS), but unfortunately there was no response.

Interviewing ambulance and taxi drivers from the city, for which the system is being designed , is essential to give accurate and more useful rules that could help in the ambulance navigation methods within that city. Comparison of the system with the real traffic sensors would give a better evaluation than the norm system which was used in this project. This evaluation along with real field driving would give an accurate comparison between the two systems. One more main thing the system lacked was, that it was not tested and evaluated by the drivers for whom it is intended.

Their feedbacks would be crucial in design of the system. In terms of software, the system could be developed better by using C++ programming or other similar languages mainly for two reasons. First, it would be possible to update the attributes that would be active at a certain time, by linking the time of the PDA to the system in order to find which route is predicted to be congested. Secondly it would be easier to combine the rules from the expert knowledge by using IF and THEN in C. That would make the calculation of the shortest paths by using Dijkstra’s algorithm possible which is used in ArcView’s Network Analyst.

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