LA LLET I ELS DERIVATS LÀCTICS

In this study, it is very important to select different cities features to test the system proposed, this is because the ability to demonstrate the intelligent disaster management system and represent the significant importance acquired while taking into account many city features, different contexts. Consequently, the ability to generalize the system usage is tested.

Some factors are defined and considered where they directly affect the implementation. When choosing the cities to test the project objective, several differences static characteristics are considered such as population, city dimension, people attitude, no. of zones and no. of trips, see Table ‎6.1. These differences eventually affect the no. of evacuation strategy selections and the evacuation time for both scenarios. Consequently, Table ‎6.8 shows the significant difference between two cities selected for this study (after implementing different disaster management system including our intelligent proposed system).

Table ‎6.8 Comparison between Al-Ramadi and Al-Huseneya main results

Al-Ramadi city Al-Huseneya city

Total time of evacuation using traditional

system, static mode, minutes 330 310

Total time of evacuation using our

proposed system, static mode, minutes 160 220

Total time of evacuation using traditional

system, dynamic mode, minutes 135 180

Total time of evacuation using our

proposed system, dynamic mode, minutes 115 120

Note Table ‎6.8, it is noticed that there are obvious differences between the two cities and for both static and dynamic implementation. This is because we employ different strategies and models, the traditional static model causes high delays due to many obstructions happened in

DISASTER MANAGEMENT SYSTEM

the traffic network, long response time of both the management agents and vehicle drivers. This delay decreases when the dynamic mode is applied which implies the O-D matrix required every time segment and all the blockages are entered the transportation network after few minutes from its appearance. Our proposed system gives the opportunity of fast response time where the drivers will aware immediately about any changes in the network. When the dynamic system which imply the dynamic changes in the O-D matrix, the total evacuation time will be decreased rapidly and consequently reduce the potential causality of losses.

6.4

Average Vehicle Occupancy (AVO)

To enhance the damage reduction, there is an opportunity to use the public transportation vehicles to enhance the evacuation process. For example, Al-Ramadi public transportation system consists of buses only. The public transportation vehicles will be involved, where possible, in the cases of emergency, although it will need a decision from the city council authorities.

Since public transportation vehicles have a high passenger capacity, these will be useful in the evacuation process. According to this level, we investigate and present here a very important factor which affects the disaster management system operation; it is called Average Vehicle Occupancy (AVO). Vehicle occupancy treatment takes a great part in the evacuation process; the higher rate of car occupancy means fewer trips required in the evacuation process, this may have the following benefits:

 Reducing any unnecessary movements of cars

 Increasing vehicle utilization.

 Increasing the transportation supply and enhance the management and coordination of traffic movement.

 Thus, decreasing overall the conflicts between vehicle movements and reducing the number of the expected traffic accidents.

DISASTER MANAGEMENT SYSTEM

A study has contributed effectively in this investigation by providing the minimum estimation evacuation time for each evacuation area. It can be determined by dividing the number of vehicles in the evacuation area by the total roadway capacity yields a high-level approximation of the minimum evacuation time of the area [219].

( ⁄ )⁄ ( )

Where

Minimum evacuation time, hour Pop: Population of evacuation area, person

AVO: Average vehicle occupancy, person per hour

Roadway capacity, vehicle per hour

For example, the Al-Ramadi city has a population of approximately 230,000 people, 30% of people have access to one or more cars; 20% of households have access to one or more cars [210]. The mode of travel is private car and the buses; this will lead to an average 4 people per vehicle according to the high car ownership in the city households.

Therefore, the number of trips needed is,

_{⁄ = 57500 trips}

The population is to be carried by 57500 vehicle trips on the paths that lead to the evacuation areas. Typically, the government has encouraged people to use the public transport mode in order to reduce the chaos condition that can arise in such situations.

There is however a plan in place for emergency events in the Al-Ramadi city that 50% of the public vehicles will be involved in transporting people to safe areas [209]. Since public transportation vehicles have a high passenger capacity, these will be useful in the evacuation process.

DISASTER MANAGEMENT SYSTEM

Utilizing the population data collected for each evacuation area and assuming average vehicle occupancy between 1 and 3 persons per vehicle, the minimum time to evacuate each area can be calculated.

In case of this city, and in order to evaluate the AVO contribution, we use the minimum evacuation time equation above which has taken the AVO factor into account and estimate the minimum evacuation time. Here, a range of AVO can be applied; between 1 and 3. Meanwhile, we assume different roadway layouts in this evaluation as we have different road types in the city network, as shown in Table ‎6.9.

Table ‎6.9 Minimum evacuation time with different AVO

Average Vehicle Occupancy,

AVO

Time, hrs

Minor Road Major Road

1 Lane C = 900v/h 2Lanes C = 1800 v/h 2Lanes C = 2800 v/h 3Lanes C = 4200 v/h 4Lanes C = 5600 v/h 1 255.555 127.78 82.142 54.761 41.071 1.5 170.37 85.518 54.762 36.507 27.381 2 127.777 63.888 41.071 27.381 20.535 2.5 102.222 51.111 32.857 21.904 16.428 3 85.185 42.592 27.38 18.254 13.69

No one can underestimate the importance of this factor in a disaster situation. To help people who have no access to the assembly point or the assembly point is too far from where they are of this time. They are already in the road network when the disaster occurred, to be able to use their private vehicles rather than waiting for the public transportation to carry as many passengers as they can with them to the safe place.

DISASTER MANAGEMENT SYSTEM

Updating the information about the situation is extremely important to the evacuation strategies and processes. It is important to collect the disaster data periodically; this can be done via VANETs, Cloud computing and other sensing technologies (if applicable and still operational after the disaster hit). In case of testing the AVO value in this situation, applying equation (5.1), and assuming different AVO to different road layout. The result shows that less time is needed to evacuate the vehicles in the case of applying higher AVO on the same capacity of the roads.

The trip generation models have a great contribution to the disaster management system. In the ordinary situation the trip generation models may depend on the land use and the socioeconomic factors but in the case of an event like a disaster the trip generation models may depend on type of population at the time of the event like the home residents, employees, shoppers, people at the hospitals, and people using the transportation network.

Beside all the socioeconomic characteristics have a major effect on the evacuation time, one of the important factors in the evacuation process is the average vehicle occupancy (AVO) which is mainly included in the transportation model and affecting the number of trips required to finish the evacuation process. Our contribution is to study the effect of AVO in conjugation with applying the new communication technology to reduce the delay in the evacuation process and save lives.