Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach

Djahel, Soufiene; Smith, Nicolas P.; Wang, Shen; Murphy, John

doi:10.1109/isc2.2015.7366151

Cited by 51 publications

(24 citation statements)

References 9 publications

(9 reference statements)

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“…The delay of emergency vehicles, such as ambulances and firefighter trucks, is, no doubt, among the most critical consequences of traffic congestion as it may lead to substantial losses of assets and human lives. To overcome this issue, the authors of Reference [6] proposed an advanced adaptive traffic control system built upon a fuzzy logic controller that combines the observed road occupancy level and average speed to assess the current congestion level. This latter, along with the announced emergency level, are used to determine the most effective emergency response plan that helps the emergency vehicle to get to the emergency location with minimum delay.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Aleko

Djahel

2020

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Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

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Section: Related Workmentioning

confidence: 99%

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Aleko

Djahel

2020

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“…The strategy proposed in [11] and [12], to turn signal green which would further make situation go worse if two ambulance approach from different directions, hence it is not feasible in a country with second highest population In order to deal with the rising traffic on roads, the system sends the shortest path calculated for the ambulance to the Traffic Control Room. The system in TCR displays the route along with real time traffic data.…”

Section: • Traffic Control Strategymentioning

confidence: 99%

Intelligent Ambulance Fleet Management System

2016

International Journal of Research Studies in Computer Science a

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“…must wait in traffic at an intersection as depicted in Figure 1 which delays their arrival at their destination causing loss of lives and property. In Ireland, an average of 700 fatalities was noted every year due to late ambulance responses [1]. …”

Section: Introductionmentioning

confidence: 99%

Traffic Management for Emergency Vehicle Priority Based on Visual Sensing

Kapileswar

Hancke

2016

Sensors

106

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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. A VANET 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 IEEE 802.15.4, and Enhanced Back-off Selection scheme for IEEE 802.15.4 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.

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Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach

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Cited by 51 publications

References 9 publications

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Intelligent Ambulance Fleet Management System

Traffic Management for Emergency Vehicle Priority Based on Visual Sensing

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