# Impact of road architecture and design on performance of city-based VANETs

Sourav Kumar Bhoi, Sanjaya Kumar Panda, Chittaranjan Mallick and Kalyan Kumar Jena
From the journal Open Computer Science

# Abstract

Vehicular communication is the communication between the vehicles to provide intelligent transportation systems (ITSs) services to the end users. It is the most advance and emerging wireless technology in ad hoc network. On the other hand, construction of roads has a great impact in forwarding the data to the destination. As vehicles are moving with high speeds, the architecture of roads can change the performance of routing and data forwarding in the vehicular ad hoc network (VANET). If the construction of the roads in a city area is planned with intelligent junctions, flyovers, multilane, etc., then the performance of the system increases. In this paper, we have analyzed the impact of road elements like intersections, flyovers, multilane, buildings, hills, etc., on VANET routing and find solutions for the problems related to the performance of the system. We also simulate the impact of these elements in VANET routing and analyzed the performance using OMNeT++ network simulator and SUMO traffic simulator. The performance is studied by comparing standard GSR and GPSR position-based routing protocols.

## 1 Introduction

### Figure 1

Impact of different road elements on vehicular communication.

Researchers are working mainly to enhance the performance of the VANET system by finding new solutions to the problems that are related to routing. Chang et al. [3] proposed geocasting protocols to send the messages to the geocasting regions by using a small flooding area in order to survive from the obstacles. Giordano et al. [12] have proposed a model, called CORNER, to estimate the obstacles in the city area by using the maps. Martinez et al. [13] have proposed radio propagation models to study the effect of the models in system performance. Mahajan et al. [14] have worked in developing simulation models for streets, multilane, traffic, etc. and analyzed the effect on packet delivery and delays. Boban and Vinhoza [15] have proposed a simulation model for vehicles as obstacles and studied the effect of vehicles in system performance. Khazaal et al. [16] have studied the effect of obstacles in video quality. Many such research works mainly based on VANET mobility models, data transmission models, packet error models, routing, security, automation, performance analysis, etc. are presented in ref. [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59].

From the literature survey and according to our knowledge, it is observed that no such work has been done in this area where the impact of road construction and design has been studied, to see the performance of VANETs. This work explored the areas in a simpler manner by highlighting all possible issues and provided solutions related to the issues.

The major contributions of this paper are stated as follows:

1. In this work, we analyze the impact of road elements in the performance of VANET. First, we consider the intersections to evaluate VANET performance. Here, we evaluate that if an intersection is intelligently constructed, then the vehicles communicate in a well manner and there is less number of communication gap between the vehicles. We also evaluate that if two intersections are far apart from each other, then there is a chance of link breakage between the vehicles.

2. Second, we evaluate that the flyovers over the roads reduce the distances (i.e., shortest path generation) to the destination, which enhances the VANET performance. Here, we discuss about flyover vehicle to flyover vehicle communication (FV2FV) and ground vehicle to flyover vehicle communication (GV2FV/FV2GV) and analyzed that it reduces the end-to-end delay.

3. Third, we evaluate the effect of singlelane and multilane on vehicular communication and analyzed that multilane has high density. As a result, there is less chance of link breakages in these areas.

4. At last, we evaluate the effect of obstacles, like buildings, hills, trees, cars, any objects, etc. in vehicular communication and analyzed that they reduce the performance of VANET.

5. The above scenarios are implemented using OMNeT++ network simulator and SUMO traffic simulator to study the performance. The performance is evaluated using two standard routing protocols such as GSR [2] and GPSR [2].

The paper is organized as follows. Section 2 presents the effect of road elements in VANET communication. Section 3 presents the simulation results of the effect of road elements. At last, we conclude in Section 4.

## 2 Impact of road construction on VANET performance

The performance of VANET mainly depends on the design and planned architectures of the roads in the city areas. The performance parameters, like end-to-end delay, packet delivery ratio, throughput, etc., depend on the way in which the roads are constructed. In this section, the network model and communication between the vehicles are discussed. Then, we analyze the effect of the road elements such as intersection, flyover, lane, and obstacles on VANET performance.

### 2.1 Network model

We consider the following assumptions that are related to the whole system. The city area is considered as a graph, where the roads connecting the two networks are assumed as an edge and the intersections are assumed as the vertices. Figure 2 shows the architecture of city-based VANETs. The roads also consist of road side units (RSUs) which are fixed nodes for transmission of data to the cloud or used for advertisements. Each vehicle beacons at a particular interval of time to broadcast its information (such as, speed, location, and identification (ID)) at a rate of 1 beacon/100 ms. The neighboring vehicles maintain a neighbor table and update data. The vehicles use global positioning system services with maps to know their own positions. We also assume that the time to send a data from one vehicle (V 1) to another (V 2) is ( τ t + τ o ) V 1 V 2 , where τ t is the transmission delay and τ o is the other delays, like processing delay, propagation delay, and queuing delay.

### Figure 2

City-based VANET architecture.

### 2.2 Effect of intersection

In a road network, intersections play an important role to connect the roads and help the vehicles to select a new path. At the intersection region, the density (λ) of the vehicles is very high than the vehicles between the two intersections. This is because at the junction region, from all directions vehicles arrive or wait to choose a new path. However, between the junctions there may be less vehicles at some time when the density is less. We divide the intersections as simple intersection and intelligent intersection, respectively. Simple intersection is a type of intersection in which vehicle spends too much time, because of lack of planned architecture. Intelligent intersection is another type of intersection in which the vehicles spend less time, because of its planned architecture. In Figure 3, vehicles want to move to a region (x × y), but due to simple traffic architecture, they stay there for a t time, which creates a communication link breakage region (x × y). If a data packet reaches at the intersection and the vehicle carrying the data has calculated the shortest path, then it has to stay for t time. This architecture increases the time delay due to the generation of the communication gaps. The vehicle waits until a vehicle from north side to east side or when the traffic from the intersection is released. In Figure 4, an intelligent intersection is shown in which the vehicles are moving without a traffic light system. This is because of the small flyover, which reduces the traffic congestion. Here, the density of vehicles is also higher and they can pass to any direction, without staying at the intersection. As the roads are busy, if a data packet reaches at the intersection, then it can be easily routed to any routes in order to reach the destination. This is due to no communication gaps at the intersections. This reduces the end-to-end delay by quickly transmitting the data to the destination.

### Figure 3

Simple intersection.

### Figure 4

Intelligent intersection.

### Theorem 1

If data are forwarded through an intelligent intersection, then end-to-end delay reduces.

Proof: Let us consider Figure 3 in which the number of vehicles in the region (x × y) at any time t is less (for example, no vehicles). If there are n number of vehicles (V 1, V 2,…, V n ) moving through the intersection, then there is a chance of communication gap. If data are carried by a vehicle at the intersection and the route is calculated in the eastern direction, then the vehicle carries the data for τ c time until a new vehicle moves in that direction. This increases the end-to-end delay. Let us assume that we have m simple intersections (i 1, i 2,…, i m ) and destination (D). At a particular instant of time, if a vehicle V 1 carries the data and it has determined the route to be V 1i 1i 2−…−i m D. Then the delay (T 1) to transmit the data in this route is calculated as follows.

(1) T 1 = ( τ t + τ o ) V 1 i 1 + τ c + ( τ t + τ o ) i 1 i 2 + τ c + + ( τ t + τ o ) i m D

where, τ c is the carry and forward delay.

From Figure 4, we observe that the vehicles are moving, without spending any time. As a result, there is a less chance of communication gap generation. Therefore, delay (T 2) is calculated as follows.

(2) T 2 = ( τ t + τ o ) V 1 i 1 + ( τ t + τ o ) i 1 i 2 + + ( τ t + τ o ) i m D

From equations (1) and (2), we conclude that T 2 < T 1.

### Theorem 2

If the intersections are far away from each other, then there is a chance of link breakage between the vehicles.

Proof: As we know intersection connects the roads and there is a chance of high density in that region. If the intersections are far away from each other, then the driver behavior (especially, those drivers drive the vehicle at a high speed) changes and there is chance of link breakage between the vehicles. Let us consider Figures 5 and 6 in which there are four (i 1, i 2, i 3, i 4) and two (i 5, i 6) number of intersections. Let the path length from i 1 to i 4 is denoted as PL i 1 i 4 and this path has more intersections. As a result, the density of vehicles (λ) is also more. Let PL i 1 i 4 has m number of vehicles and PL i 5 i 6 has n number of vehicles, respectively, where m > n. Here, we assume that λ 1 > λ 2 and the path lengths are same (i.e., PL i 1 i 4 = PL i 5 i 6 ). The probability of getting m vehicles in path i 1 to i 4 is given as follows.

(3) P ( m ) = ( ( λ 1 A 1 ) m / m ! ) e λ 1 A 1

where, λ 1 is the density of the vehicles in PL i 1 i 4 and A 1 is the area of the road between i 1 and i 4. Note that we use the Poisson distribution to represent the probability.

### Figure 5

Path with four intersections.

### Figure 6

Path with two intersections.

The probability of getting n vehicles in path i 5 to i 6 is given as follows.

(4) P ( n ) = ( ( λ 2 A 2 ) n / n ! ) e λ 2 A 2

where, λ 2 is the density of the vehicles in PL i 5 i 6 and A 2 is the area of the road between i 5 and i 6. As m > n and P(m) > P(n) (from equations (3) and (4)), it is clear that the link breakage problem depends on the density of the vehicle. Hence, if the number of vehicles is more, then there is a high chance of getting vehicle in the communication range and there is no link breakage.

### 2.3 Effect of Flyover

Flyovers are constructed to reduce the traffic in which there is a free flow of vehicles. Flyover passes over a road and connects to a new road. The vehicles which are moving in the flyover are called flyover vehicles (FV) and the vehicles moving in the ground are called ground vehicles (GV). If data communication occurs between the vehicles, then the communication can be GV2GV (ground vehicle to ground vehicle), FV2FV, and GV2FV/FV2GV (ground vehicle to flyover vehicle/flyover vehicle to ground vehicle). This communication helps in reducing the end-to-end delay by reducing the distance. Figure 6 shows the flyover communication.

### Theorem 3

Forwarding data through a flyover can reduce the path length to the destination.

Proof: From Figure 7, if we are not considering the flyover, then the path length to reach D from source S (PLSD) is given as follows.

(5) P L 1 = S i 1 + i 1 i 2 + i 2 i 5 + i 5 i 8 + i 8 D

### Figure 7

Flyover communication.

If the flyover is used, then the PL 2 is given by taking the two-intersection points p and q and it is shown as follows.

(6) P L 2 = S i 1 + i 1 p + p q + q i 8 + i 8 D = S i 1 + i 1 p + a b ( 1 + ( d y /d x ) 2 ) 1 / 2 d x + q i 8 + i 8 D

where, curve (pq) length is shown in the equation (6). From Figure 8, we assume that the curve tends to a straight line and the length pi 2 and i 2 q are straight lines. According to Pythagoras theorem, (pi 2 + i 2 q) > (pq). From equations (5) and (6), PL 1 > PL 2. As distance reduces, the time delay also reduces.

### Figure 8

City area with flyover.

### Theorem 4

FV2FV and GV2FV/FV2GV communications reduce the link breakage problem.

Proof: From Figure 9, we see that V 1 encounters no vehicle in its communication range and the link breaks with V 3 as it moves too fast. For the link survivability, V 1 communicates with V 2, which is a FV (GV2FV). Then V 2 sends the data to V 3 in its range (FV2FV). After this, V 3 sends the data to V 4 (FV2GV) and this process continues until D is reached. Previously, the link is broken with V 4, but the above communications make the data transmission and avoid link breakage problem.

### Figure 9

Flyover communication with no link breakage.

Let the link created in Figure 8 is V 1V 2V 3V 4V 5. The time delay T 1 to send the data through this continuous link is calculated as follows.

(7) T 1 = ( τ t + τ o ) V 1 V 2 + ( τ t + τ o ) V 2 V 3 + ( τ t + τ o ) V 3 V 4 + ( τ t + τ o ) V 4 V 5

Let T 2 is the time delay in which the GV carries the data until a new GV is encountered and the time delay is calculated as follows.

(8) T 2 = τ c + ( τ t + τ o ) V 1 V 4 + ( τ t + τ o ) V 4 V 5

From equations (7) and (8), there is no guarantee of finding a vehicle, which may increase the τ c time. Therefore, T 2 > T 1 and we conclude that FV vehicles help in transmitting the data rapidly to the destination.

### 2.4 Effect of lane

Lanes are designed to manage the traffic by marking lines on the roads. It helps the vehicles to pass in a single line. By designing multilane two-way, a huge number of vehicles can pass from one intersection to another. In this scenario, the density of vehicles increases between the intersections. Therefore, sending data packet in a route having highest connected lanes increases the performance of the system. There is a less chance of link break between the vehicles, which helps a source vehicle to send the data packet to the destination in an efficient rate. In Figure 10, we see that some of the intersections are connected by a 2-lane or 3-lane (referred as multilane) and the data are transmitted in a route, which is highly connected. Here, the route, which is highly connected with the lanes, is Si 1i 4i 5i 6i 9D.

### Figure 10

City area with lanes.

### Theorem 5

Multilane reduces the effect of link failure.

Proof: From Figure 11, we see that a 2-lane structure is constructed, which survives the network from link failure. V 1 encounters null vehicle in its communication range. The area (x × y) has no vehicle. But, the data are transmitted in a multilane road. As a result, V 1 transmits the data to V 2, which is present in its communication range. However, this vehicle is moving in the third row. The link is created as follows. V 1V 2V 3V 4V 5, where → is the link between the vehicles. If T is the delay to transmit the data from V 1 to V 5, then it is presented as follows.

(9) T = ( τ t + τ o ) V 1 V 2 + ( τ t + τ o ) V 2 V 3 + ( τ t + τ o ) V 3 V 4 + ( τ t + τ o ) V 4 V 5

### Figure 11

2-lane structure.

From equation (9), we see that there is no carry and forward mechanism and this helps the system to reduce the end-to-end delay.

### 2.5 Effect of obstacle

The obstacles are the physical things in the environment, which block the communication between the vehicles in a VANET implemented area. When roads are constructed in a region with obstacles, then obstacles have great effect in communication between the vehicles. Obstacles are of many types like buildings, hills, trees, towers, lakes, vehicles, etc. In city areas, there is a chance of encountering the obstacles. This obstacle blocks the signal from the sender to the receiver [30,31]. This results in packet error, retransmissions, packet discard, etc.

### Theorem 6

If number of hop count increases, the delay increases and packet delivery ratio decreases.

Proof: There are two cases in which the obstacles block the systems communication and reduce the performance of the system. The two cases are discussed as follows.

Case 1: At the beaconing phase, the vehicles send their information (ID, speed, and location) to the neighboring vehicles. But, due to the obstacle (like Figure 12), V 1 is unable to send the current information about itself to V 3 and vice versa. Here, V 1 wants to send the data to V 3, which is in its communication range, but it is not visible in the range (beaconing fails). This may increase the delay by sending the data to a next hop, which is nearer to V 1 or sends the data to a vehicle in different route, which is far away from the destination. Let T 1 is the delay to send the data directly to V 3 and T 2 is the delay to send the data via V 2. The delays are mathematically represented as follows.

(10) T 1 = ( τ t + τ o ) V 1 V 3

(11) T 2 = ( τ t + τ o ) V 1 V 2 + ( τ t + τ o ) V 2 V 4

### Figure 12

Obstacles in city area.

From equations (10) and (11), T 2 > T 1. This is due to increase of hop counts, which also leads to increase in the distance and delay.

Case 2: At the data transmission phase, there may be dropping of packets, which increases the time delay. Sending and resending the packet continuously reduces the performance of the system. Let the packets to be received is m and some of the packets are dropped due to obstacles. At last, n packets are received. As a result, the packet delivery ratio is reduced to (n/m).

## 3 Simulation and results

In this section, the simulation and results are presented. Initially, simulation setup and assumptions taken during the simulation are discussed. The parameters considered for performance evaluation are end-to-end delay and packet delivery ratio. We have considered four scenarios where the impact of road elements such as intersections, flyovers, multilanes, and obstacles are studied. In this simulation to study the performance comparison, we have considered two standard routing protocols, GSR [2] and GPSR [2], for data transfer from source to the destination.

To carryout simulation, we have used the Veins hybrid framework simulator. This simulator uses IEEE 802.11p standard for communication. Since the framework is hybrid one, it uses OMNeT++ and Simulation of Urban Mobility (SUMO) as network and road traffic simulator, respectively [60,61]. These simulators are integrated using a Traffic Control Interface (TraCI). This interface provides the TCP connection between the network and road traffic simulator and maintains real time interaction between them. We simulate our work in Grid Map scenario. The configuration parameters for SUMO and OMNeT++ are provided in Tables 1 and 2, respectively. The results are the average of 20 simulation runs. The source and the destinations are randomly selected.

Scenario 1: In the first scenario, we have simulated the vehicles passing through simple intersection and intelligent intersection and analyzed the results by considering end-to-end delay. In the simple intersection, the vehicle will wait at the intersection (traffic delay). But, in the intelligent intersection, we have designed the route SUMO in such a manner that no vehicles are in waiting condition at the intersection, so data are forwarded in a faster manner. Figures 13 and 14 show that if the data are forwarded in a route with simple intersections, then the end-to-end delay is higher than the route with intelligent intersections because the vehicles wait at the intersections. However, if the density of vehicles increases, then the data will be transmitted quickly. From the two figures, it is observed that GPSR performs better than GSR routing protocol by showing less delay.

Scenario 2: In the second scenario, we have considered a scenario where in the simulation we connect two diagonal nodes or intersections and assumed it as flyover (as assumed in Theorem 3). In this simulation, we connect intersection 1 with intersection 9 and intersection 7 with intersection 3. The simulation is performed and the results for normal scenario (grid scenario) are compared with the above scenario. From Figures 15 and 16, it is observed that the data are transmitted in the shortest path by taking the flyover path. So, flyover communication takes less delay than normal road communication. Here also, as number of vehicles increases, the delay reduces due to high availability of vehicles. GSR also shows high delay than GPSR.

Scenario 3: In this scenario, we have taken two cases: first is single lane simulation and second is multilane simulation. From Figures 17 and 18, it is observed that single lane shows less delay as compared to multilane. This is because multilane has high number of vehicles and a vehicle has more options in selecting the next forwarder node. It is also observed that as number of vehicles increases, the delay decreases. GSR also shows high delay as compared to GPSR routing protocol.

Scenario 4: In the last scenario, we have added a basic error model using path loss model for generating packet error rate and assumed the error happens due to obstacles in the city areas. From Figure 19, it is observed that GPSR shows less packet delivery ratio as compared to GSR. The packet drops due to packet error which is discarded at the destination. As the number of vehicles increases, the links between the vehicles are strong and a vehicle selects the node with good packet delivery ratio, hence it becomes stable.

### Table 1

Simulation setting for SUMO

Sl. No. Parameters Values
1 Area 3,000 × 3,000 m2
2 Number of lanes 3 directions
3 Maximum speed of vehicles 30 m/s
4 Allowed maximum speed on edges 30.556 m/s
5 Maximum acceleration 3.0 m/s2
6 Maximum deceleration 6.0 m/s2
7 Driver imperfection 0.5
8 Number of vehicles 10–100
9 Vehicles type 3
10 Length of vehicle type 5, 7, 12 m
11 Number of intersections 9

### Table 2

Simulation setting for network simulator

Sl. No. Parameters Values
1 Simulation time 300 s
2 Bitrate 6 Mbps
3 Packet generation rate 10 packets/s
4 Communication range of vehicle 300 m
5 Communication range of RSU 500 m
6 Update interval 0.1 s
7 IEEE 802.11p
8 Sensitivity −80 dBm
9 Number of simulations 20

### Figure 13

End-to-end delay in seconds for GSR in simple intersection and intelligent intersection scenario.

### Figure 14

End-to-end delay in seconds for GPSR in simple intersection and intelligent intersection scenario.

### Figure 15

End-to-end delay in seconds for GSR in flyover communication and normal road communication scenario.

### Figure 16

End-to-end delay in seconds for GPSR in flyover communication and normal road communication scenario.

### Figure 17

End-to-end delay in seconds for GSR in single lane and multi-lane scenario.

### Figure 18

End-to-end delay in seconds for GPSR in single lane and multi-lane scenario.

### Figure 19

Packet delivery ratio comparison for GSR and GPSR.

## 4 Conclusion

In this paper, we have analyzed the effect of road construction in city-based VANETs. To get a congestion-free traffic, the intersections should be constructed in an intelligent way, so that many vehicles can pass without any communication gap. This enhances the system performance. The flyovers and interchanges should be constructed in a planned way, so that the vehicles can perform communication in order to send data through a shortest path. As vehicles are increasing, the vehicles should use multilane to travel. Moreover, it can transfer the data easily, because of high density of vehicles. As obstacles exist, the vehicles should smartly select the neighboring vehicles so that the packet delivery is high. Simulation results better support the work for evaluating the performance of city-based VANETs. This analysis can help civil works better to construct an intelligent roadway network and design of smart city. This analysis will be a better solution for providing ITSs services to the users.

1. Conflict of interest: The authors declare that they have no conflict of interest.

2. Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

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