Real traffic time data acquisition, currently used in vehicle navigation systems, can be very expensive, and also inaccurate and biased. Therefore, researchers have been motivated to develop alternative systems that are affordable by many countries, with acceptable accuracy especially in emergency medical services. This research, which has been developed in GIS, presents an intelligent navigation system for ambulance drivers, with an aim of finding the least travel time route that is independent of using real time traffic data.
This system helps the drivers to overcome decision making errors due to time pressure stress, about which route to follow. Here, the specific focus is on building a set of rules from the knowledge and experiences of various ambulance drivers, and is associated with factors that might affect the response time to reach the incident locations. This is done by weight roads according to such factors in order to calculate relatively accurate travel times along these roads. In addition, this system also considers the time of the day and locations.
The system in this research was implemented as a set of scenarios using ArcView’s network analyst extension, in order to calculate the fastest route from any hospital (as the dispatch centre) to any incident in the city of Leicester, UK. Research Paper 1. Introduction In Vehicles Navigation Systems (IVNS) – mounted in ambulance vehicles, are used to guide the drivers by following the quickest path from the dispatch location to the incident location. This navigation is presently supported by real time data of the current traffic conditions of the roads.
The real time traffic data inputs are collected using traffic sensors (detectors), which are either mounted on specially equipped moving vehicles or situated on the road sides. These sensors are either buried under the roads surfaces or are camera-based (Fisher, 2004). However, real-time data collecting equipments are very expensive which also includes the relatively high operating costs for each survey (Nual et al. 2002, Balke et al. 2005). Moreover, real time data collected from vehicles’ sensors depend on fixed number of vehicles, thus the data collected only cover limited number of roads (Thompson, 2003).
In addition, the results of the real time traffic collection can also be biased about the traffic condition. For instance, buried and road side sensors can only provide the information about the spots that they are situated in, thus the data collected does not represent the real traffic state on the entire stretch of the road (Haas et al. , 2001). Moreover, real time data collection is labour intensive and requires trained technicians (Thompson, 2003). Finally, it is also difficult to record the change of traffic conditions throughout the day (Borri and Cera, 2005).
Many Intelligent transportation systems (ITS) specialists and technology scientists have been working towards the problem, by either developing ways to overcome the problems mentioned above or by developing alternative methods to collect real time traffic data. For example, Thompson (2003) proposes using a relatively inexpensive technique that provides more accurate results by Integrating PDA, GPS and GIS technologies, in order to collect real time traffic volume data, as against the data collected by expensive techniques in previous instances using sensors.
In his technique, GIS is used to display the positions of vehicles on digital road network map and the positions of vehicles are updated every 10 seconds. Two prototypes had been developed in his study: the first one being a PDA-based prototype for collecting GPS data and the second one being a PC/GIS-based prototype to process and display the collected real time data. It could be argued that Thompson’s technique, outlined above, has three main drawbacks. First, each vehicle must have a GPS and should also be linked to the main control centre in order to make use of the collected data, which is hard to achieve.
Next, there will be a massive amount of collected data from this technique, and so the hardware should have a large amount of storage space and high processing speed. Moreover, powerful software is also needed which would be capable of updating and analysing a massive number of the tracked vehicles and positions every 10 seconds. Finally, this technique might also be associated with privacy issue for an individual driver because the movement of each car will be tracked and recorded. Derekenaris et al.
(2000) developed a navigation system technology to build an effective ambulance management system in Attica, Greece. This system uses GIS in routing the ambulance from the hospital to the incident site and also from that site to the closest hospital calculating the quickest path, which is based on implementing the cost weights associated with each segment in roads networks. The cost in this system is a function of the length of roads and real time traffic volume collected using sensors.
The optimum route is then displayed on a small PC screen in the vehicle transmitted from the operation centre. Derekenaris et al. system did not discuss whether it is possible to collect traffic volume data along each road especially in big urban cities such as Attica with an area of 3,808 sq. km and approximately 4 million people (population census March 18, 2001) ( Peppas et al 2006). In addition, this study did not consider the expenses of building such a system, which requires a huge number of traffic sensors and operators.
Finally, this system was only tested along sample roads of the city while the big barrier is how to collect, transmit and analyse real time data from a massive road networks In spite of the real time data collection techniques mentioned above that might be available for the ambulance drivers, reaching the incident locations within a shortest time puts the ambulance drivers under time pressure which often causes psychological stress on these drivers (Jains, 1993).
This stressful condition can affect the drivers’ judgments on which routes to follow, especially if there are many factors that can affect this decision (Trakofler and Vaught, 2003). Due to all such reasons as mentioned above, this research aims to develop an intelligent routing system by integrating expert knowledge in GIS. The system would be capable of taking into consideration most of the events and circumstances that could affect the speed of vehicles on roads and therefore would be able to reduce the response time of reaching the incident locations by the ambulance vehicles’ drivers.
Further objectives include: (i) identifying the navigation rules depending on times and places in order to find the optimal quickest route; (ii) build a database in GIS that consider these rules; (iii) adopting a weight method to prioritise the choice of one route than another by the ambulance drivers and; (iv) finding the fastest route between two points based on streets’ weights.
This research will mainly make use of the expert knowledge of ambulance drivers from previous literatures, in order to establish a set of rules and priorities of choosing one route rather than another by the drivers. These rules are then integrated into GIS system to produce an intelligent navigation system as can be seen in figure 1. This system will be efficient enough to be used in poor countries which can not afford traffic monitoring equipments.