Dynamic VANET based traffic sensing and control

The previous implementation sections presented a stepwise approach to the intelligent VANET Cloud based traffic control for evacuation management during major disasters. The approach is stepwise, static, because the cloud based intelligence layer computes the disaster strategy only once and propagates the devised control plan to the vehicles so that the vehicles

0 10 20 30 40 50 60 70 80 90 100 0 20 40 60 80 100 120 140 160 Ev ac u e e s ( % )

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could find their routes to the evacuation areas based on their locations. In this section, we enhance our earlier approach by extending the earlier static methodology to a dynamic one. In addition, the possibility to introduce a dynamic system is promoted; the traffic performance will be examined and evaluated dynamically, per a fixed time period; say every 10 -15 minutes, the traffic situation will be checked and hence the decisions updated according to the new situation (consistently be able to collect the data and propagate the decisions). In other words, the dynamic approach exploits the fact that the road and disaster conditions can be sensed periodically, in real time, through vehicular networks and other sources, such as road traditional sensors (inductive loops, traffic counters etc.), cameras, social networks, traffic authorities etc. (if these are still functional after the disaster).

Before starting the assessment, very important factor should be clarified; the time interval. There is no observed either assumed evidence have been utilised leading to the choice of the time interval for announcing people to evacuate from diverse zones. In disaster problem, it has been noted that some challenges are faced during selecting a time-step at the same period for both short and long links. To model the short links in a very short time step is essential, although this will accelerate the problem size. However, the sensitivity of travel time is the core concern to the selected time-step. To elaborate the time sensitivity, for example if 1 minute is applied to a short interval time it may be worthy for the short links opposite to the effect shown by the long links. On the other hand, to predict the travel time between two nodes that are a thousand miles apart, with accuracy of 1 minute is almost impossible. In this state, to achieve the effectiveness time interval sensitivity, 1 hour to 30 minute unit is more applicable [218]. For example, 1 minute intervals is applicable for simulating very small size road networks, however it is not ideal. The reason being is, the study area is very small and they conducted short period time to clear the road network.

To sum up, to achieve the set goal; which is setting a suitable time-step to evacuate people in such problem, the evacuation scenarios can be duplicated many times through applying different time-steps, so establishing a suitable interval time can be started.

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Here, the traffic condition periodically can be assessed every 10 -15 minutes, giving sufficient time to sensing the traffic, compute a suitable traffic assignment strategy, and propagate the computed strategy through vehicular networks, traffic control signals (if these are functional) and other dissemination and control sources.

All the other settings, except dynamic sensing and control, described in the previous section remain the same. This approach allows any transient effects in the city traffic to be taken into account in real-time (every 10 - 15 minutes in this case but the time can be decreased or increased to suit the disaster situation) and have a real-time automated control over the evacuation plan. An example of the results for the dynamic modelling approach is presented including figures between Figure ‎6.5 and Figure ‎6.6.

Figure ‎6.5 shows the cumulative number of vehicles against time on the Al-Am Street in the east side of the city (see map and streets in Figure ‎6.1). In addition, Figure ‎6.6 shows the cumulative number of vehicles against time on the Ceramic Street in the west side of the city.

Figure ‎6.5 The cumulative number of vehicles against time in the east side of the city 0 1000 2000 3000 4000 5000 6000 7000 8000 0 20 40 60 80 100 120 140 160 180 Cu m m u lativ e N u m b e r o f Veh ic le s

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Figure ‎6.6 The cumulative number of vehicles against time in the west side of the city

6.2

Al-Huseneya city

Al-Huseneya city is a major district in the Karbala Governorate, and is situated 15 km to north east of Karbala city, and 70 km at the south of Baghdad city, the capital of Iraq. Most of the population of Al-Huseneya is rural, according to censuses at the year 2007. The overall population of Al-Huseneya is estimated around 125,000 capita [210], the city map can be seen in Figure ‎6.7.

Figure ‎6.7 shows the transportation network map of Al-Huseneya city, the network consists of zones, nodes, and links. The city is divided into 2 traffic zones. Zone 1 is on the west side of the major street which divides the city into two parts. The east part of the city contains Zone 2 represents the old city centre which attracts high number of trips in the morning peak hour. The surrounding areas around the city are major agriculture areas and the connections of roadways in these areas are very poor.

Also, we quantify transportation trends of the city in terms of an O-D matrix between the two city zones in Table ‎6.5. The numbers of trips in the O-D matrix shown are in the mid-week period with natural conditions. These trips are calculated using the Fratar model.

0 1000 2000 3000 4000 5000 6000 0 20 40 60 80 100 120 140 160 180 Cu m m u lativ e N u m b e r o f Veh ic le s

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Table ‎6.5 An O-D matrix of the transportation network in Al-Huseneya city

Zone 1 Zone 2 Total

Zone 1 0 3050 3050

Zone 2 7395 0 7395

Total 7395 3050 10445

Most effective emergency plans should consist of specifying the evacuation area location in advance so it becomes possible for people at risk to be directed to those safe areas. Note in Figure ‎6.7, the evacuation area, its purpose is to provide an appropriate and safe location for the population in case a major disaster strikes the city and people need to be moved out of the city, so the evacuation area was chosen in the south of the city, because there is a direct roadway that joins the evacuation zone with Karbala city, the capital of Karbala Governorate. The surrounding areas around the city are major agriculture areas and the connections of roadways in these areas are very poor. The data received from various sensors; such as video recording, intra-vehicular sensor networks and user interfaces, goes through an internal validation layer before it is accepted by the modelling and analysis layer.

A disaster event which could happen in the business district area is selected and could be any one of the potential risks listed earlier in Section ‎6.1.2, e.g. fire or explosion of hazardous materials in the market areas, disaster area is determined in Figure ‎6.7.

CHAPTER SIX ANALYSIS AND EVALUATION OF THE PROPOSED DISASTER MANAGEMENT SYSTEM

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Now, the fundamental transportation information are listed, which are used in the disaster management operations for Al-Huseneya city; they are summarized in Table ‎6.6.

Table ‎6.6 Al-Huseneya city basic information

Measurement Value

Population of the city (people) 125,000

No. of zones 2

Peak period 8:00 – 9:00 am

No. of trips (veh/hr) 10445

No. of evacuation area 1

Next, the comparison results between the implementation of our proposal, intelligent disaster management system, and the conventional system are presented.