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Traffic Management for Emergency Vehicle Priority based on Visual Sensing

ABSTRACT

Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic congestion at intersections. Most of the traffic lights today feature a fixed green light sequence, therefore the green light sequence is determined without taking the presence of the emergency vehicles into account.

Therefore, emergency vehicles such as ambulances, police cars, fire engines, etc. stuck in a traffic jam and delayed in reaching their destination can lead to loss of property and valuable lives. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated for comparison using Euclidean distance, Manhattan distance and Canberra distance techniques.

The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques. Along with visual sensing techniques to collect emergency vehicle information, it is very important to have a Medium Access Control (MAC) protocol to deliver the emergency vehicle information to the Traffic Management Center (TMC) with less delay. Then only the emergency vehicle is quickly served and can reach the destination in time. In this paper, we have also investigated the MAC layer in WSNs to prioritize the emergency vehicle data and to reduce the transmission delay for emergency messages.

We have modified the medium access procedure used in standard IEEE 802.11p with PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. AVANET model for the UTMS is developed and simulated in NS-2. The performance of the standard IEEE 802.11p and the proposed PE-MAC is analysed in detail. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption.

The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE802.15.4, and Enhanced Back-off Selection scheme for IEEE802.15.4 protocol [EBSS, an existing protocol to ensure fast transmission of the detected events on the road towards the TMC] and the comparative results have proven the effectiveness of the PE-MAC over them. Furthermore, this research work will provide an insight into the design of an intelligent urban traffic management system for the effective management of emergency vehicles and will help to save lives and property.

RELATED WORK

With an increasing amount of vehicles on the road, traffic congestion and transportation delays are increasing worldwide. Emergency vehicles, such as ambulances, fire engines and police cars, should be capable to react to emergency calls with minimum delay. The excellence of the emergency service depends on how fast the emergency vehicles can reach the incident location.

If the emergency vehicle gets stuck in a traffic jam and its arrival at the incident location is delayed it can cause loss of lives and property. There is a need for smart traffic management systems based on priority and traffic density to improve the transportation efficiency and response times of emergency services.

PROPOSED METHODOLOGY

Figure 2. Architecture of an urban traffic management system

Figure 2. Architecture of an urban traffic management system

WSNs and VANETs for smart cities are becoming a reality with increased options for area coverage and connectivity stemming from machine-to-machine communication and the Internet-of-Things. An Urban Traffic Management System (UTMS), depicted in Figure 2, refers to a system that integrates sensing technologies, data processing techniques, wireless communications and advanced technologies to reduce traffic congestion, travel time, fuel consumption and provide priority-based signaling. On obtaining the data of emergency vehicles from sensors, the Traffic Management Centre (TMC) follows the distance-based emergency vehicle dispatching (DBEVD) algorithm and provides signals to the emergency vehicle immediately.

PE-MAC PROTOCOL

Figure 5. IEEE 802.11p (a) Superframe structure; (b) CSMA/CA process

Figure 5. IEEE 802.11p (a) Superframe structure; (b) CSMA/CA process

The IEEE 802.11p protocol has been developed by the IEEE 1609 working group as a key communication standard for vehicular networking. TheIEEE802.11p basically adopts the carrier sense multiple access with collision avoidance (CSMA/CA) with exponential back-off mechanism for packet access control. When a station wants to send a packet, first it has to listen to the channel, which is referred to as the carrier sensing. If the channel is free for a time known as the distributed inter frame spacing (DIFS) time, the station will transmit a request to send (RTS) to the destination.

The destination will respond with a clear to send (CTS) if it is available to receive data. When the source station receives the CTS, it will transmit its data. The network allocation vector (NAV) indicates the time amount the channel is busy. All the packets sent in the network hold this NAV information.

After the data has been correctly received at the destination station, it will send an acknowledgment (ACK) back to the sender station. At this point,if the sender has more data to transmit, it will again begin its back-off and repeat the process. The frame structure and the CSMA/CA process are demonstrated in Figure 5a,b, respectively. The short inter frame spacing (SIFS) is used as the wait time between the RTS, CTS, DATA and ACK frames. The SIFS ensures that the other node does not wrongly determine that the channel is idle during the handshake.

RESULTS AND DISCUSSION

Figure8. Impact of number of nodes on average end-to-end delay: proposedPE-MACvsIEEE802.11p

Figure8. Impact of number of nodes on average end-to-end delay: proposedPE-MACvsIEEE802.11p

The simulation of the VANET model for the urban traffic management system working with the proposed protocol is performed in NS2 with the number of nodes varying from 5 to100. The simulation results show the performance of the PE-MAC and IEEE802.11p. The main parameters to be evaluated in this simulation are the average end-to-end delay, throughput and residual energy. The histogram plotted in Figure 8 compares the average end-to-end delay of the proposed PE-MAC and the IEEE 802.11p, protocol under varying number of node conditions.

Figure 17. Measured data

Figure 17. Measured data

After obtaining the Euclidean distance, we measured the speed of the emergency vehicle (using ∆d/∆t) and counted the vehicles moving along with the emergency vehicle towards next intersection. The measured values of vehicle count, Euclidean distance and speed are shown in Figure 17. The traffic management center can utilize this information in a traffic signal control module, resulting in an efficient emergency traffic management process.

All the existing works depend on some kind of infrastructure and require extra cost equipment. Our scheme utilizes ultrasonic sensors, RSUs and existing surveillance cameras. The image processing-based approach cuts the installation and maintenance costs compared to existing emergency vehicle pre-emption (EVP) systems.

CONCLUSIONS

This paper has presented an approach to schedule emergency vehicles in traffic. The approach combines the measurement of distance between the emergency vehicle and intersections using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated from visual data using Euclidean distance, Manhattan distance and Canberra distance techniques for comparison. The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques and is suitable for real-time applications.

A complete description of the use the use of visual sensing techniques in vehicle detection and counting is also presented. The measured information like vehicle count, distance and speed are very useful for a traffic management center to manage emergency traffic efficiently. After obtaining the measured information, how fast it is delivered to the TMC is very important. For that, we have proposed a PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. A VANET model for the urban traffic management system used in this work is developed and simulated in NS-2. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption.

The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE 802.15.4, and EBSS and the comparative results have proven the effectiveness of the PE-MAC over them. The improvement achieved by PE-MAC is almost 31% over IEEE 802.15.4, 11% over EBSS and 28% over IEEE 802.11p in the case of a transmission interval of 5 s, and increases to about 45% over IEEE 802.15.4, 16.6% over EBSS and 42% over IEEE 802.11p when the transmission interval is 60 s. The results have proven that the proposed PE-MAC achieves lower end-to-end delay compared to the considered schemes.

Source:  University of Pretoria
Authors: Kapileswar Nellore | Gerhard P. Hancke

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