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Importance of GIS in vehicle navigation systems

It is important to integrate GIS in navigation systems to maximise their efficiency by enriching them with digital road networks which are used as a spatial reference to locate the vehicle (Forssell et al. , 2002). It is also used to calculate the optimal route between two points using shortest path algorithm. Furthermore, it is used to construct real world properties of roads (roads lengths, hospital location, etc) linked with huge spatial database into the VNS in which Vehicle routing will be based on this network representation and data (Kwan and Speigle, 1997).

They also mentioned that using GIS allows VNS to provide a more realistic illustration of elements of the road network and to provide easy ways that people perceive them. The different scaled digital road network map that is built using GIS serves as the interface between the driver and the navigation system being used (Quddus , 2006). Moreover, the accuracy of these networks reflects the accuracy of the vehicle navigation (Forssell et al. , 2002). Nual et al.

(2002) added that GIS is also important in address-match geocoding within the navigation systems by converting street addresses to map coordinates and then displaying the locations on the navigation system screen automatically. The real big step attempt in integrating GIS in navigation system was discussed in GPS&GIS 1991 conference at Yellowstone, USA, (Abler,1992). The use of GIS along with IVNS has shown a vital importance in emergency management after hurricane Katrina hit New Orleans in the US (Pinnacle system, 2006).

This study shows the importance of GIS in geo-coding the addresses because this disaster destroyed most of the street signs and the emergency units were unfamiliar to the area. The IVNS was used to assist the emergency units to locate the flooded areas using GPS and to find the optimum evacuation routes. These instructions are transmitted from the control centre in which they do not pass flooded and destroyed areas with the help of other technologies such as satellite images and wireless communication (pinnacle, 2006).

Aurangabadkar et al. (2007) built a navigation system to assist the routing of ambulances in the highly populated city of Chennai, India. This system uses enabled GPS/GIS handheld devices to assist the ambulance drivers to find the quickest route from the incident location to the closest hospital or emergency medical centre. This system was also used to display the nearest hospital along with other information that might help the driver to choose the best hospital depending on injuries types or health cases.

This information included for example, hospital contact phone numbers and details of the emergency facilities available in the nearest hospital or any closer medical centre. Traffic congestion is the main reason for not reaching the destination on time (Bertini, 2005). Different measurements of congestion have been used in literature or in practice depend on “type of transportation facility, geographic location, time of day and trip purpose”, (Sinclair, 2000, p17).

There are two commonly used methods (Sinclair, 2000). Firstly, travel time, speed or travel delay measurements which are based on time. Secondly, volume to capacity ratio (V/C) which is the ratio between road travel demand (V) and road capacity (C). As demand exceeds supply (V/C=0. 75 or greater), congestion occurs. Many researchers and studies recommended measurements which are based on travel time such as, travel time/speed (DoT, 2005). The U. S.

Department of Transportation (DoT) mentions that Intelligent Transportation System (ITS) is used to provide real time information about the traffic conditions, incidents occurring in roads, expected work zones and other information to either the control centre or directly to vehicle drivers in order to help the drivers to avoid the delay that happens because of congested roads or other factors and therefore reduces response times. Real time traffic data are collected using special sensors or/and cameras (Huang and Zhang, 2006).

The real time traffic sensors situated either on trucks or on non-moving objects such as road signs and traffic lights (Huang and Zhang, 2006). Two types of sensors are mainly used in collecting real traffic data. The first type is called Roadway Loop Detector which is a wire buried beneath the road surface with a current flowing into it (Haas et al. , 2001). They explained this technique that whenever a vehicle passes on this wire, a surge of current is induced through this loop. He concluded that traffic flow and density of cars are measured by counting the number of surges.

Another type of sensors is called Automated Vehicle Identification (AVI) sensors which are situated on roads’ signposts or traffic lights or on sides of the roads that detect sample of the number of cars and their speeds. Kaufman &. Smith (1993) carried a study to build a system that formulates a new real time shortest path algorithm in order to find time dependent fastest paths for intelligent Vehicle Highway Systems routing which can be used for emergency services such as ambulance vehicle routing to reduce respond time to the incident locations.

This study aimed to find the shortest paths using modified version of Dijkstra’s algorithm that are linked to travel time, taking into consideration quick real time change which are often categorizes traffic network due to varying travel time (e. g. Rush-hour and off-peak). This study also took into consideration lane-blockages which might be caused by accidents and can cause delay to the targeted incident locations.

They concluded that an evaluation of the system should be implemented in order to find the benefit that is given to the guided vehicles. In one hand, “Real time traffic information for all major roadways in an urban area is one of the most important pieces of information necessary to produce a dynamic route guidance”(Nual et al. 2002, p. 1). On the other hand, using such data can be very expensive, inaccurate and not available to all communities (Balke et al. 2005 and Nual et al. 2002). Du et al.

(2007) discussed the problems that are associated with processing the received dynamic data to optimise the routing of vehicles. They stated that real time data traffic systems should have highly computational ability and to consider uncertain data then process it, while it should also consider current decision and finally reacts and generates new solutions and plans efficiently. The UK Parliamentary Office of Science and Technology (2002) addressed two main issues of using real time data.

Firstly, financing and management issues because different applications rely on using real time data collection sensors, communication equipments, digital UK maps and other technologies, thus it is difficult to quantify the infrastructure cost for each application individually. Moreover, extra costs are needed to upgrade these systems frequently because these technologies which are associated with these technologies are rapidly developed. As a result of this issue the “Decision-makers may be reluctant to deploy intelligent transportation systems without quantitative data on costs and benefits”, pp4.

The second issue is the process and efficient use of the large amount of real time data which is another challenge. For example, there is no benefit if the transmitted information from automated emergency call system to the local emergency service is not transmitted rapidly. Moreover, collecting accurate real time data for traffic conditions is not accurate, and requires much resources and staff (Thompson, 2003). Thompson mentioned that there are two major techniques used to collect real time traffic data which have some drawbacks.

He pointed out that the first technique collects the accelerations of vehicles from specific times and points, which affects the accuracy of estimating the acceleration of the cars. The second technique is not accurate either because it relies on using moving vehicles that are mounted with speed detectors to collect speed data by tracking sample cars randomly. In addition, collecting real time traffic data also requires special trained survey technicians and expensive equipments.

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