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Background: In developing countries, the healthcare system is facing numerous challenges. One of the major challenges faced by the healthcare system is that the healthcare service providers are meager and geographically far from the densely populated area. Objective: To overcome the above challenge, the present research work proposes SHRMS (Smart Heart Rate Monitoring System) which provides the ad-hoc services to the patients who are in the transit mode in the emergency vehicle. Method: A pulse sensor is attached to the patient’s fingertip to fetch the heart rate of the patient. The patient’s data is further transmitted to the microcontroller which in turn transmits the data to the ThingSpeak cloud service. Result: SHRMS provides the real-time monitoring of the patient and helps to provide emergency aid as per the patient’s current situation. Conclusion: This device is beneficial for developing countries where the healthcare service providers are very less and geographically scattered.

Giving right of way green signal for the Emergency Service Vehicles (ESV) like ambulance and fire service rescue vehicle with minimum delay is a major challenge in Intelligent Transportation Systems (ITS). This challenge is currently achieved by various conventional Emergency Vehicle Pre-emption (EVP) Systems. These conventional systems fail miserably due to false triggering, line of sight issues, network delays, high cost and low reliability. In this paper, we propose a new decentralized Radio Frequency (RF) and Global Navigation Satellite System (GNSS) based Geo-fencing approach which allows smooth passage of ESVs through the junctions with minimum delays. The proposed system was field trailed in one of the junctions in Thiruvananthapuram, India. Pre and post-implementation travel time of emergency vehicles at the junction was evaluated by an external agency National Transportation Planning and Research Centre (NATPAC), Thiruvananthapuram. The evaluation result shows that there is a considerable reduction in the average travel time taken by the emergency vehicles to cross the cordon area in the post-implementation stage as compared to the pre-implementation stage. The reduction in average travel time is approximately 48%.

Priority to emergency vehicles, buses, high occupancy vehicles (HOVs), and priced low occupancy vehicles (LOVs) is critical to their efficiency and reliability, where multi-functional exclusive lane (MFEL) serves as a salient element with independent operation environment. To enhance MFEL utility and balance the demand between prioritized and non-prioritized vehicles, this research proposes an optimization model for MFEL design and management to comprehensively serve the trip modes with various priority levels and distinct operation patterns, considering the delay from lane access, bus stop dwelling, signalized intersections, and ride-sharing. Case study follows to calibrate and validate the proposed model under varying passenger demand and emergency vehicle frequency, finding that MFEL may reduce total travel time in all scenarios especially under high traffic demand. Sensitivity analyses test the effect of road lane count and bus occupancy on MFEL design and management, where the road with more lanes is more flexible to accommodate increased passenger demand without increasing HOV critical occupancy or LOV price rate. Moreover, higher bus occupancy assists in avoiding significant increase of LOV price rate to promote trip equity. This research may lay foundation to MFEL implementation to mitigate traffic congestion and promote transport sustainability.

Background: Emergency vehicles required a quick clearance so as to reach the destination with minimum delay and the human life could be saved. Emergency vehicle required a dedicated path for clearance. The dedicated path creates a chaos by blocking the entire route for others vehicles and it is not always possible to create a dedicated path. So there is an imperative need for a smart road navigation system which adapts to the traffic congestion in real-time velocity of vehicle, count of vehicle, number of lanes and distance from source to destination. Objective: The objective of this paper is to find an optimal route for providing a least congested optimal route for emergency vehicle with least delay considering various issues such as congestion, numbers of vehicles, average traffic flow on the roads and width of the lane between source and destination. Proposed approach: Real-time traffic data like number of vehicles, velocity of the vehicles, and the width of the road and distance of the route are used to determine the congestion factor on all possible route. Congestion factor is used for finding the shortest route to requesting emergency vehicle. Result: Experimental results establish that the travel time of a vehicle is reduced by approximately 26%, when the vehicle uses the optimized route. This is beneficial for any emergency vehicle as the optimal path is provided on a real-time basis. Conclusion: This research work proposes an analytical approach that provides the least congested optimal route on-demand based on real-time traffic.

Traffic lights are an important controlling factor in traffic flows, and good policies will facilitate traffic congestion. A car's waiting time is highly related to the period in which the traffic lights are green or red; thus, a proper controlling policy reduces the waiting time and speeds up the car movement. Emergency vehicles always have a higher priority than other cars and need to reach their destination faster. In the last decade, a lot of research has been done to solve the traffic light scheduling issue using artificial intelligence techniques. Reinforcement learning with simulating human learning has the outstanding performance to solve complex problems, such as traffic lights control. In this study, a deep reinforcement learning traffic light control was implemented at a crossroad using real-time traffic information with an emphasis on emergency vehicles. The agent learns to adjust its policies to prioritize the emergency cars over the other cars. The results show less waiting time and speed up the cars’ movement. It also indicates the emergency vehicles will cross the intersection with the least delay. With 200 to 600 cars and one emergency vehicle, the average delay decreases 2–16% in total using reinforcement learning. Considering more emergency vehicles, there is a 27–40% decrease in average delay for emergency vehicles.

This paper presents an emergency vehicle priority control system based on connected vehicle technology, called MMITSS priority. Traditional preemption does not consider the effect of the current traffic situation, such as the presence of a freight vehicle in the dilemma zone, on an opposing movement and can have a significant negative impact on the minor movements of vehicles. A mixed integer linear programming model is developed which can consider the priority requests from multiple emergency vehicles and dilemma zone requests from freight vehicles that could be trapped in the dilemma zone. The optimization model provides an optimal schedule that minimizes the total weighted priority request delays and dilemma zone request, as well as some flexibility to adapt to other vehicles in real time. The flexible implementation of the optimal signal timing schedule is designed to improve the mobility of the non-emergency vehicles. The approach has been tested and evaluated using microscopic traffic simulation. The simulation experiments show that the proposed priority control method is able to improve the travel time of the vehicles on the minor street while ensuring safe passage of the freight vehicle at the dilemma zone without significantly delaying the emergency vehicles. The method is implemented at the Maricopa County SMARTDrive ProgramSM test bed in Anthem, Arizona.

The urban traffic congestion is being increased day by day due to large number of vehicles are used by dense people in cities. In the current model of the Traffic Control System, the time delay of each signal light is static which leads to lot of waiting time and was tage of fuel. To overcome this problem, intelligent traffic management system of controlling the traffic lights using the ARM 7 controller and camera sensor is proposed. The camera which is installed along the pavement captures the real time video of the road. The video is then processed indifferent stages to find the number of vehicles in that particular lane using Convex hull technique and accordingly the time delay of the traffic signals has been changed dynamically. Incase, if an emergency vehicle like ambulance is detected by RF434 in a particular lane then automatically this lane will be given the highest priority to clear the traffic. Another feature is if any vehicle violates the traffic line that can also be identified by the RFID reader and automatically and an alert message will be sent to registered mobile number through GSM module which is interfaced with LPC2148.

Every country's vehicular traffic is increasing, growing, and there is terrible traffic congestion at intersections. In the current case, most traffic lights have a fixed light sequence, so green light sequence is to determine with-out taking priority vehicles into account. As a result, priority crews such as police cars, ambulances, fire engines are still unable to perform, get stuck in traffic and come in late, which can result in the loss of valuable property and life, which does happen on occasion. The green light sequence is evaluated given the current state of traffic, without taking into account the existence of emergency vehicles. Our aim to this paper is to present a mechanism for scheduling emergency vehicles. It is provided to important such as access control protocol to convey emergency vehicle information to the Traffic Management Center (TMC) with time delay and to all alerts while using GPS techniques for acquiring emergency vehicle information. Only then is the emergency vehicle quickly dispatched, and the destination is reached on time. It would be helpful in the future for the prominence of casual vehicles.

Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV’s travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly.

Vehicular Ad Hoc NETworks (VANET) has turn up as a stand to support intelligent vehicles communication, transport safety and performance.Road accidents and traffic congestion are the significant issues of metropolitan territories. In spite of the fact that endless arrangements are given for these issues, still there is no fitting answer for street mishap recognition. Likewise, because of the deferral in arriving at the emergency vehicle to the mishap area and accordingly the gridlock in the middle of the mishap area and clinic expands the probabilities of the passing of the person in question. Thus, it is needed to give handheld answer for the general public.This paper introduces a handheld solution to reduce the loss of life due to accidents and the time taken by the ambulance to reach the hospital. First, the proposed system finds out the road accidents automatically with the help of sensors in intelligent vehicles. Second, an alert is sent to all nearby hospitals after the road accident. This system maintains a database server which holds all hospital details in around the city. A GPS or GSM gadget in the concerned vehicle will send the area character of the mishap to the close by medical clinics through primary worker and afterward a rescue vehicle from very closest clinic is shipped off the mishap spot. Alongside these, there would be an effect of traffic inside the way of the emergency vehicle utilizing RF correspondence. This will limit the hour of ambulances to arrive at the clinic.Third, this system maintains another database for all kind of mechanical services. This system provides support to the roadsiders while the vehicle got breakdown or any other service issues in the vehicle. It also saves the time of the driver and unnecessary delay. Altogether, this system provides the appropriate handheld solution to the vehicle drivers and the society.

When disasters happen, victims suffer from lots of miseries. Vehicles are used to carry relief supplies for these victims. However, these vehicles are subject to threats as roads and bridges are often destroyed by these disasters. To dispatch vehicles safely and minimize the misery of victims, a disaster emergency vehicle path planning model is proposed by introducing the deprivation cost function and two constraints. One constraint is the risk of the route, and the other is the angle of the vehicle. A differential evolution algorithm based on the exponential selection is developed to optimize the model. The proposed algorithm uses the exponential ranking method, making individuals have exponential probability as parents enter into the mutation stage. The approach improves the exploitation capacity while maintain the exploration ability of the algorithm. Three disaster scenarios in three-dimension are used to do simulations. The experimental results have verified that our proposed model can deal well with the vehicle path planning problem in disaster emergency management. The proposed algorithm has acquired superior performances compared with differential evolution variant algorithms.

Purpose We aimed to identify evidence supporting emergency vehicle technology in out-of-vehicle systems and in-vehicle-systems that can be used by people with hearing impairment. Methods We conducted a systematized review to determine what technologies, at any readiness level, are currently being developed in the area of emergency vehicle detection technology. The studies and websites of technologies for emergency vehicles that are currently available to the general public were identified by searching in one electronic database, Scopus and grey literature using Google, respectively. Results A total of 359 studies were retrieved from Scopus. The grey literature search identified 189 websites related to the search terms under study. Technological devices have included satellite, out-of-vehicle and in-vehicle technology. The analysis shows that most of the technologies are at the stage of laboratory testing. Knowing the availability and the level of readiness of devices, as well as their effectiveness, provides rehabilitation professionals with tools for making evidence-based recommendations to clients with hearing impairment. Knowing which devices are available also provides information to individuals living with hearing impairment so that they can play an active role in the decision-making process of acquiring the existing technologies. Conclusions So far, few technologies have been identified, and most of them are in the laboratory development stage (i.e., low product readiness level). These results indicate a justified need for the development of new in-vehicle technologies to detect emergency vehicles and actions to transform the new devices into products that are available for people with hearing impairment. IMPLICATIONS FOR REHABILITATION There is a need to design new assistive technology devices that are intended to detect emergency service vehicles, which in turn may reduce stress and anxiety related to driving for people with hearing impairment. The majority of the included articles discussed siren detection, with only three discussing the necessary next step of communication to humans. Due to the research being in its early stages, the evidence for emergency vehicle technology on health and participation-related outcomes in people living with hearing impairment is unknown. Most websites do not include information on where to purchase emergency vehicle technology or how much the devices cost, thus making it difficult for people with hearing impairment or rehabilitation professionals to make informed decisions about acquiring these technologies.

With the increasing number of traffic vehicles and urbanization, the frequencies of traffic accidents are growing exponentially. The research for routing emergency vehicles has gained significant attention in Vehicular Ad hoc Network (VANET) topology. The path planning for an emergency vehicle can avoid congestion and minimize the travel time to prevent accidents. Thus, this work proposes a dynamic path planning scheme for routing emergency vehicles. At first, the VANET is simulated and the dynamic path planning is performed after the arrival of emergency vehicles. The proposed dynamic path planning includes two steps, namely travel-time forecasting, and bi-model routing. The travel time is predicted using the Support Vector Regression model and the bimodal is employed to route the emergency vehicles optimally. The bimodal consists of two steps, such as K-path discovery and optimal path selection process. Initially, the possible K shortest path is detected from which the optimal path is selected by the proposed Exponential Bird Swarm Optimization Algorithm (Exp-BSA). The proposed Exp-BSA algorithm is designed by integrating the Exponentially Weighted Moving Average concept in Bird Swarm Algorithm (BSA). Thus, the proposed Exp-BSA determines the optimal paths based on the devised fitness function, and the selected paths are suggested to emergency vehicles for faster routing. The performance of the proposed dynamic path planning for an emergency vehicle based on Exp-BSA is evaluated using three simulation setups based on the metrics, such as distance, average traffic density, average speed, and travel time. Also, the performance of the proposed method is compared with the existing methods, such as SFLA-KC, K-Path, and BSA. From the results, it is exposed that the proposed Exp-BSA method achieves the overall best performance than the existing methods with a minimal distance of 48.404 m, an average traffic density of 0.015 min, an average speed of 1.392 m/s, travel time of 19.608 s.

Emerging mobility services (e.g., ridehailing, e-commerce, micromobility, etc.) are generating novel and rapidly growing demands to use curbside space, with potentially large impacts on mobility, energy consumption, and related outcomes. This presents both opportunities and challenges to municipal agencies responsible for managing this interface between the road network and adjacent land uses, as legacy practices require updating. In this study, we employ a semistructured interviewing approach to establish how municipalities are adapting to these new pressures on their curbside. We interviewed senior staff responsible for curbside policy of ten large U.S. municipalities with populations ranging from ~250,000 to ~5,000,000, the majority of which are the central cities of their respective metropolitan regions. We document a trend of organizational restructuring to more formally include curbside management teams, with the majority of interviewees also reporting increased staffing. Respondents reported that operational failures at their curbside (e.g., demand in excess of capacity) have impacts on safety, capacity, and emergency vehicle mobility, with impacts highly concentrated spatially and temporally (e.g., late evenings in nightlife districts). We document a diversity of data flows between ridehailing operators (e.g., Uber, Lyft) and municipalities, with some cities reporting obtaining types of data that other cities report not receiving despite requesting such data. Finally, respondents consistently expressed a desire for new data streams and methodologies to help manage the curbside of the future. It is hoped that establishing the state of the practice in this rapidly changing context will be of use to practitioners facing similar pressures as those of our interviewees.

Emergency vehicles (EVs) are significant in disseminating sensitive information across the road-side communication networks. This amalgamation of vehicles and communication networks improves the reachability and accessibility of sensitive data along the driving scenario. However, the communication network experiences data and traffic congestion due to wireless medium and varying vehicle densities. In order to address the problem of data and traffic congestion, this paper introduces message dissemination with re-route planning (MDRP) method. This method initiates a dissemination boundary for selecting neighbors and weight for selecting the re-routing path. The weight is based on the traffic conditions of the road segment along with consideration of the timeout of the EV message. The joint process of rerouting and data transmission is supported by dependent queue management for improving the message delivery and reducing the impact of delaying instances in the travelling path.

Considering that the time of meeting the demands is very important for emergency vehicle and emergency vehicle can’t reject any request, we introduce a more realistic cost form into online traveling salesman problem(OL-TSP). When a new request arrives, if the salesman can’t serve the request immediately, per-unit-time cost will be generated. The goal is to minimize server’s total costs(travel makespan plus the per-unit-time costs). We consider the server is a non-zealous server and show that neither deterministic nor randomized online algorithms can achieve constant competitive ratio for OL-TSP on general metric space. While on truncated line segment and uniform metric space, we prove lower bounds, and present competitive online algorithms. Especially for the case with uniform metric space, we prove an optimal Greedy algorithm.

The aim of this study is to suggest ways to capitalize on the golden period for timely response to fire and emergency victims. To implement a system to effectively capitalize on the golden period, there is a need for policy measures as well as legal and social consensus. Precedent research has relentlessly mentioned the need for and feasibility of the priority traffic system for emergency vehicles. To this end, the present study involved an analysis of pilot cases of the priority traffic signal system for emergency vehicles introduced in a city during 26 days, from April 3 to 28, 2017. Out of 58 cases registered with the local 1-1-9 fire house, the number of cases that took advantage of the priority traffic system stood at 16 in response to fire and 11 to first-aid operations. Owing to the system, on average, the response time reduced by 3 min and 50 s in case of fire and by 3 min and 30 s for first aid. There were four complaints registered owing to traffic congestion, and the number of car accidents declined to one from six over the same period in the previous year. Based on these findings, it is safe to say that the priority traffic signal system would be effective for emergency vehicles to capitalize on the golden period if issues identified during the pilot run are resolved.

This study proposes an integrated driving control strategy by taking advantage of the automated driving technology at the individual vehicle level and the traffic signal preemption strategy at the traffic infrastructure level. This aims to facilitate an automated driving-based emergency vehicle control and ultimately to achieve efficient and safe control of emergency vehicles. To this end, this study developed the integrated emergency vehicle control logic, implemented the logic in the microscopic traffic simulation environment using the simulation software’s application programming interface capability, and evaluated the impacts of the proposed emergency vehicle control logic in the aspects of mobility and safety with different driving aggressiveness and preemption initiation settings. The study’s results show that the proposed emergency vehicle control logic achieved benefits on mobility and safety and the benefits of emergency vehicle control strategy can be maximized when the signal preemption and the automated driving control operate in collaboration. Therefore, the proposed integrated approach of automated driving controls and signal preemption will be a great reference for enhancing automated driving technologies supporting a safe and fast mobility solution.

Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.

Vehicular traffic is endlessly increasing everywhere within the world and may cause terrible traffic jams at intersections. Traffic congestion and tidal flow are major facts that cause delays to emergency vehicles. Fire brigade officials, ambulances, and police officers often get delayed due to such congestion and traffic. With the use of the right technology, like Artificial Intelligence and real-time monitoring of the traffic, such a predicament can be moderated thereby saving the lives of the needy. The solution presented allows emergency Vehicle (EV) drivers to select the route of commute on the mobile application. Modules on that route selected are activated for the adaptive traffic control system. The driver’s GPS is dynamically updated on the cloud and fetched by the Raspberry Pi module. As the vehicle enters a given radius, the module checks for other EVs in the vicinity for priority assignment. The module starts traffic density detection and changes the traffic light states to clear the traffic if the density is above a certain threshold. After the EV comes closer to the signal, the light turns green irrespective of the traffic density. The camera cross validates if the EV has passed. Once it does, traffic signals switch to regular operation. This will allow the emergency vehicles to reach the destination on time and save the lives of those in need without delay. The entire solution is robust and reliable and can manage traffic efficiently keeping in mind the imperativeness of the emergency vehicles.

Being able to estimate future velocity on a road network has applications from vehicle navigation systems to emergency vehicle dispatching systems. The existence of traffic congestion can severely impact travelers’ travel time and in this paper we explore methods to take it into account in velocity forecasting models. Using a data approach, different traffic observations can be classified into classes with and without congestion. Our research shows that using congestion as an attribute can reduce the prediction error when implementing machine learning models, such as random forest or multi-layer perceptron. Furthermore, training separate models for each class performs better than using congestion as an extra attribute. A methodology for the congestion pattern identification is proposed, based only in the velocity and volume values.

The vehicular industry is going through an evolutionary phase of rapid scientific and technical development. It provides assistance for C-ITS (Cooperative Intelligent Transport Systems) that connects vehicles and roadside infrastructure i.e., Road Weather stations (RWS) and pedestrian. This is to offer an efficient and safe road transport system. Globally, the incorporation of ITS using DSRC (Dedicated Short-Range Communications) standard and ETSI (European Telecommunications Standards Institute) standard known as ITS-G5. ITS-G5 offer diverse feature of location-based services that were missing previously in cooperative vehicles. In this paper, we have presented the deployment of cooperative vehicle scenarios that aims to prevent road accidents and avoid casualties. It has been done by delivering time-critical road safety services to vehicles using ITS-G5. The deployment of cooperative vehicle scenarios uses different instruments for vehicles (i.e., Radars, LIDAR’s, friction sensors, weather sensors etc.): These different sensors and devices permits numerous advanced road services including snow/windstorms, heavy rain or road slippery identification. In this paper, we have identified the packet loss, vehicle locations, network latency and vehicles distance in dangerous situations i.e., emergency brake light, slow vehicle and emergency vehicle approaching. The C-ITS scenarios will support the wide-scale implementation and usage of advanced road weather safety and maintenance services with enhanced ITS-G5 networking capabilities.

Emergency Siren Detection is a topic of great importance for road safety. Nowadays, the design of cars with every comfort has improved the quality of driving, but distractions have also increased. Hence the usefulness of implementing an Emergency Vehicle Detection System: if installed inside the car, it alerts the driver of its approach, and if installed outdoors in strategic locations, it automatically activates reserved lanes. In this paper, we perform Emergency Siren Detection with a Convolutional Neural Network-based deep learning model. We investigate acoustic features to propose a low computational cost algorithm. We employ Short-Time Fourier Transform spectrograms as features and improve the classification performance by applying a harmonic percussive source separation technique. The enhancement of the harmonic components of the spectrograms gives better results than more computationally complex features. We also demonstrate the relevance of the siren harmonic contents in the classification task. The reduction of the network hyperparameters decreases the computational load of the algorithm and facilitates its implementation in real-time embedded systems.

Traffic congestion is the major consideration in the present traffic control system. Traffic density keeps on increasing day by day therefore new technologies are needed to address this issue. The traffic present in each and every lane should be monitored accordingly and the vehicles should be allowed to control the traffic. An analysis is needed to monitor the traffic and to set the time for each lane and to allow vehicles sequentially without affecting the functionality. But these control systems are not depending on the traffic density, it functions irrespective of the traffic density. In order to foretell the real time traffic jams in the lane, the status of the traffic should be monitored continuously at various time period. With the help of RFID scanner, the emergency vehicles are prioritized and by using graphical user interface the data is collected and loaded on to the server and this helps to identify the emergency vehicle automatically and the traffic light should be adjusted accordingly. This system is helpful for emerging traffic control lanes and it is highly effective.

Road traffic congestion and pedestrian accidents are one of the major issues being faced worldwide. The reasons behind these accidents are mainly due to the risk when crossing or walking on road in urban and rural areas under heavy traffic. In order to avoid such circumstances a new idea is proposed-“Automatic uplifting of pedestrian crossing platform using traffic congestion monitoring”. The pedestrian and traffic congestion is constantly monitored using an IR (Infrared) sensor module. At the time when pedestrian density is more, the traffic signal turns red for the vehicles and let the pedestrian walk through uplifted crossing platform. The proposed system consists of motorized platform placed on the zebra crossing line which automatically uplifts when the infrared sensor senses and count the crowded pedestrians at the signal point. This method ensures safety in pedestrian crossing and will also not let drivers to break the rules, which may lead to accidents. This work also includes a technology to allow emergency vehicle such as ambulance to cross the signal point even when the traffic signal is red for vehicles, so that it does not create any delay in reaching the destination.

In this paper, we document a novel method for emergency vehicle preemption at an intersection controlled by traffic lights. The method relies on wireless vehicle-to-infrastructure (V2I) communication between the emergency vehicle and the traffic lights controller, availability of an accurate estimate of the number of vehicles in the queue, and a mathematical model of dynamics of discharging of the queue. Unlike some occasionally deployed methods that trigger the preemption the instant that the emergency vehicle appears at a prespecified distance from the intersection, the proposed method adapts the activation moment to the actual traffic conditions so that the preemption is as short as possible, thus reducing the impact on the other traffic. The method has been finetuned using numerical simulations in SUMO simulator and experimentally verified in real urban traffic.

The article is devoted to the issue of the negative effectof delays in the movement of special rescue vehicles on the effectiveness of their mission. The dependence of the area of fire on the delay of the arrival of firefightersusing a fire-rescue vehicleis shown. The cascading graph of route options of specialvehiclemovementto the place of an emergencycall isgiven. The algorithm of the optimal routechoice ofthespecial vehiclemotionwith given projected delays is offered. Based onthegraph theory, probability theory,and the basic principles of traffic organization, the article proposes a new way to determine the optimal route.

Emergency vehicles demand quick and safe passage as it aims at saving the life of a patient. But with rising bureaucracy and flaunting behavior of our leaders, the vehicles in which they travel in aspire for a green corridor. It is becoming a serious problem in our society and an optimal solution where normal traffic flow is least disrupted as well as emergency vehicles too could move quickly is highly desired. This research work proposes a framework for creating a dynamic real-time dedicated green corridor using Internet of Things (IoT) devices. The simulation results record the time taken for any vehicle to reach from source to destination during normal traffic flow and congestion hours. Later time is taken for an emergency vehicle to reach its destination when a green corridor is created and the impact of this on normal traffic flow is assessed. Finally, a dynamic real-time one hop and two-hop green corridor are created and its impact on general traffic movement is assessed. The results are able to establish that if an emergency vehicle is of very critical nature, one should go for a dedicated green corridor that reduces travel time by 2.76 times. If the emergency is not critical in nature, one can opt for the two-hop dynamic real-time green corridor. But, optimally if one demands the least disruption of normal traffic and at the same time creates a safer green corridor, results establish that one-hop dynamic green corridor is an optimal solution. In order to analyze the impact of various green corridors on the routes that intersect this corridor, simulation results are obtained for four different vehicles and the results are averaged. Only one intersection is considered in this simulation. With one hop dynamic real-time green corridor in consideration, the average travel time increases on an average by 129% as compared to normal traffic flow. It is maximum with a dedicated green corridor.

This work investigates how to detect emergency vehicles such as ambulances, fire engines, and police cars based on their siren sounds. Recognizing that car drivers may sometimes be unaware of the siren warnings from the emergency vehicles, especially when in-vehicle audio systems are used, we propose to develop an automatic detection system that determines whether there are siren sounds from emergency vehicles nearby to alert other vehicles' drivers to pay attention. A convolutional neural network (CNN)-based ensemble model (SirenNet) with two network streams is designed to classify sounds of traffic soundscape to siren sounds, vehicle horns, and noise, in which the first stream (WaveNet) directly processes raw waveform, and the second one (MLNet) works with a combined feature formed by MFCC (Mel-frequency cepstral coefficients) and log-mel spectrogram. Our experiments conducted on a diverse dataset show that the raw data can complement the MFCC and log-mel features to achieve a promising accuracy of 98.24% in the siren sound detection. In addition, the proposed system can work very well with variable input length. Even for short samples of 0.25 seconds, the system still achieves a high accuracy of 96.89%. The proposed system could be helpful for not only drivers but also autopilot systems.

Emergency vehicles (EVs) play an important role in public service. And the EV’s timeliness and safety are very important. Focusing on the switch of the phases in a four-phase intersection, this paper presents a novel phase-changing model based on timed Petri nets (TPNs) for the traffic light system and proposes a method that uses TPNs to model the preemption of emergency vehicles in such an intersection. Our model ignores many actual details. By controlling the phases, our method ensures that the EVs can pass through the intersection with no or less delay, and the safety of the traffic model on emergency scene can be ensured. To our knowledge, we are the first to employ TPNs to model EV system on a four-phase intersection. The liveness and reversibility of such a TPN model are also verified through the reachability graph of the TPN.

Emergency vehicle management (EVM) is one of the highly significant tasks in the traffic control system. Accurate path prediction system can be very helpful to emergency vehicles like ambulance, fire engine, and police vehicles, military vehicles etc. In this work, multiple path emergency vehicle management using Multiple classifier with maximum voting (MP-EVM-MCMV) technique is proposed to predict the highly accurate route. The primary objective of this work is finding appropriate path and reach destination as possible using the MP-EVM-MCMV system. MP-EVM-MCMV system works on the basis of three classifiers based on maximum voting. Artificial neural network (ANN), k-nearest neighbors (KNN) and support vector machine (SVM) are used as classifiers. The analysis results show that the EVM-MCMV attains an ACC of 96.5% average for various path from different area.

There is a problem of coordination in encounters with an emergency vehicle when an automated driving vehicle travels on public roads. Effectiveness of the pre-deceleration running based on the earlier recognition of emergency vehicle using vehicle to vehicle communication is studied from the viewpoint of safety and ride comfort. Experimental data of the driver’s maneuver and vehicle’s status are collected and analyzed using the driving simulator. Recognition by vehicle to vehicle communication (V2V for short) leads to pre-deceleration and improves deceleration and jerk as compared to siren sound and red light of emergency vehicles.

Traditional emergency management studies have made resource allocation decisions to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future events can be anticipated, previous studies follow an assumption that events over a time interval are independent. This study follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents on freeways and the resource availability depends on service times of each request. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This study is the pioneer in relaxing the structural assumptions of independency during the assignment of servers and approaching the challenge from an operational perspective, online optimization. The main objective is to minimize the time needed to respond to a sequence of requests. We introduce online dispatching strategies with visualization applied in different network sizes, number of requests, and service times to provide insights on model behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. We envision a new era in which an optimal resource allocation adapts to external events effectively and anticipates the future learning from the past to produce effective solutions.

Background There is a paucity of data defining safe transport protocols for children treated with hip spica casting. Although restraint devices for casted children are available, all federally mandated testing uses a noncasted anthropomorphic test device (ATD or crash dummy). The purpose of this study was to evaluate current restraint options in simulated frontal crash testing using a casted pediatric ATD to determine injury risk to the head, cervical spine, chest, and pelvis. Methods Using a 3-year-old ATD, dynamic crash sled tests simulating frontal crash were performed in accordance with government safety standards. The ATD was casted in a double-leg spica and the following restraint devices were tested: a seat designed for spica casted children, a restraint vest-harness, a traditional booster seat, and 2 traditional forward-facing car seats. Results Although the presence of the cast increased many of the injury metrics measured, all seats passed current federal guidelines for the head and chest. No single seat performed best in all metrics. The greatest magnitude of neck loading and second-highest head injury criterion values were observed for the booster seat. The vest-harness produced the highest head injury criterion and the chest compression exceeded proposed federal limits. Conclusions The results suggest safe transport in commercially available seats is possible with the child properly restrained in a correctly fitting seat. However, parents should not assume a child restraint system is appropriate for use just based on fit as, for example, seats with harnesses outperformed an easy to fit booster seat. Clinical relevance Each child and the position of the child's cast are unique and discharge planning involves consideration of safe transportation. Although this study suggests several seats used to transport spica casted children pass the federal head and chest injury prevention requirements, it is important to recognize that some children may still require emergency vehicle transport.

Nowadays, the improvement of the emergency vehicles' priority is required to improve their response time. Presently, the emergency vehicles alert the road users using the sirens and lights, which might cause disturbances in the traffic or lead to accidents due to the unpredictability of the road users' behaviors. This paper introduces the priority indicator, which calculates the priority of several emergency vehicles as they approach an intersection and sort them in a way that allows granting priority to each one without causing delays neither for the emergency nor for the normal vehicles. The priority indicator is then introduced in a predictive traffic actuation program (DIRECTOR) that adjusts the signal timing in a dynamic way. Thus, this research aims to create a generalized priority form that simplifies granting the priority for emergency vehicles in a harmonized way in all over the Netherlands, making sure that it takes into consideration the several conditions of the intersection.

This paper discusses the design and evaluation of connected and cooperative vehicle in-vehicle sign designs displayed on a mobile phone: Emergency Electronic Brake Lights (EEBL), Emergency Vehicle Warning (EVW), Traffic Condition Warning, and Road Works Warning. Appropriateness and comprehension of each design alternative were assessed using quantitative (i.e. Likert scales) and qualitative (i.e. open-ended questions) methods. Forty-four participants took part in the study and were shown twelve dashboard camera videos presenting a total of eleven designs alternatives, displayed with or without a legend. Despite their appropriateness, EEBL and EVW signs displayed with a legend were better comprehended and less ambiguous than those displayed without a legend. Moreover, displaying a legend below the signs to warn drivers of an emergency braking ahead was efficient in low visibility condition and could potentially increase safety in critical situations.

Several emergency cases negatively affect the traffic safety and efficiency over highway roads. Un-expected areas of traffic congestion are usually caused by the occurrence of an accident, existence of a damaged vehicle, or the appearance of an emergency vehicle. These scenarios have safety considerations regarding the involved vehicles in the event of emergency. However, the effect of these cases on surrounding vehicles is usually restricted to the parameters of traffic fluency and efficiency. However, in extreme scenarios, nearby vehicles may become involved in the emergency, such as in a chained accident. In this paper, we first aim to investigate the possible emergency cases over the highway roads. Secondly, we study the effects of these cases on the surrounding traveling vehicles, based on the relative location of each vehicle from the detected case, and the speed and moving direction of each vehicle. Third, we introduce a recommendation protocol that provides the most appropriate response to each vehicle in the vicinity of the detected emergency case. Reducing/increasing the vehicle’s speed, changing the traveling lane, and completely stopping the vehicle are the most popular responses in these scenarios. From the experimental results, we can see that the proposed protocol has increased the traffic fluency in the vicinity of the emergency cases over highways. It decreases the delay in vehicles’ traveling time and increases the throughput of each lane compared to the typical highway protocol. Moreover, it has increased the safety conditions for vehicles and decreased the percentage of vehicles to become involved in accidents.

In this rapidly developing technological world, effective automated traffic systems are becoming a requirement. The present-day traffic signals require an efficient communication system for increased efficiency in response time to emergency vehicles. The proposed system not only eliminates the waiting period of emergency vehicles but also reduces the response time to emergency vehicles. Each node consists of a traffic signal system employed at a junction having a unique identification number which in turn is connected to a centralized network architecture constructed around a single server. This server in turn is connected and contains information of all nodes in a specific region. The traffic signal system is designed such that it tackles the delay in response and communication interruption that may occur with the help of the centralized server connected to them. The server plays a vital role in calculating the distance between the approaching emergency vehicle and the traffic signal and instructs the corresponding traffic signal which thereby turns the signal at the respective lane green. The range and the type of indicators in the emergency plan can be altered depending on the signal junction requirements. In case of interruption in communication between the server and the traffic signal due to climatic conditions or system failure, the traffic signal could be controlled via a mobile application developed. The various methods implemented to control the traffic signal are discussed in this paper.

In today's world, traffic jams are major concerns for emergency vehicles like Ambulances, Police cars and Fire Brigade trucks which get stuck in traffic and unable to reach their destinations in time, resulting in a possible loss of human lives. A better control over the transportation system can be achieved through the V2X based smart transportation. To deal with such emergency situations, this dissertation proposes a framework for automatic “Emergency Corridor” creation for speedy clearance of emergency vehicles. The traffic signal controller dynamically suspends the routine movement of traffic flow to provide the green phase to the approach containing the emergency vehicle using V2I communication while V2V communication makes the vehicles in the front to make way for the emergency vehicle. The work is simulated in the VEINS framework i.e. combination of SUMO and OMNeT++. We have compared the end to end travel time of Emergency vehicle with and without the proposed algorithm in two different traffic demand situations.

Constant spike in the types of vehicles, absence of efficient road traffic management, and technical failures in traffic light management devices all cause redoubled traffic congestion, emissions, and noise pollution in smart cities. Conventional traffic control systems do not handle the complex traffic flow at the junctions, whereas existing traffic control systems work on fixed time-based techniques. In this paper, a real-time traffic management system to control the traffic flow by calculating percentage match using Canny Edge detection technique, which is an image processing method, along with a digital image processing tool in MATLAB is proposed. The proposed system which is designed and verified using Proteus software offers improvement in response time and overall efficiency of the existing traffic control system. The traffic control system with advanced features like emergency vehicle situations is also incorporated into the proposed image processing based traffic control system to improve its resilience and reliability.