Intelligents Trafic Light Controller for Ambulance

The recent surge in car numbers has led to a dramatic increase in gridlock. Due to this, several individuals have perished while ambulances did not arrive in time. The primary goal of any rescue vehicle is to get there as fast as possible without compromising safety. Every second lost waiting at a red light or stuck in traffic decreases the likelihood of making it to safety. Because of this, it is crucial that emergency vehicles, such as ambulances, be given signal priority so that they may move quickly through congested areas. The reaction time of rescue vehicles is decreased and traffic flow is enhanced thanks to this innovation.

Emergency Vehicles (EVs) are of considerable significance in saving human lives and property damages. To promote the efficiency of emergency operation, signal preemption control could give priority to EVs heading toward the incident location. On the other hand, providing dynamic and precise route planning for EVs plays an important role in emergency rescue since traffic changes constantly. Furthermore, connected vehicle (CV) technology that incorporates advanced wireless communication technologies, offers a huge potential to promote the efficiency of EVs and maintain smooth traffic flow via collaborative optimization of routes and signals. This study presents a bi-level dynamic emergency route planning system considering signal preemption control, which builds on traffic flow combined with hierarchical bi-layer model predictive control (MPC), for more than one EV under partial CV environment. In this approach, the mobility of EVs is prioritized before decreasing the impact of EVs operation on normal traffic. In the upper layer, a road-level emergency route would be dynamically planned and updated after each time horizon, according to the network-wide traffic flow estimation under diverse CV market penetration ratios through loop detectors and Cellular-Vehicle-to-Everything (C-V2X) communication. In the lower layer, a lane-level emergency route that combined with signal preemption control would be planned to ensure the efficiency of EVs and reduce the adverse impact of signal preemption on normal traffic. In the end, a microscopic simulation environment for a real traffic network is carried out to test the effectiveness of the proposed system. The simulation results indicate that the proposed system provides reliable and practical emergency route planning and signal control services for EVs under different traffic flow conditions.

A multi-cell Cellular Automata model of traffic flow with emergency vehicles: Effect of a corridor of life and drivers’ behaviour

Two kinds of cellular automaton models are proposed for mixed traffic flow with emphasis on emergency vehicles. By analyzing the characteristics of ordinary vehicles in giving way to emergency vehicles, the rules for changing lanes are modified. Computer numerical simulation results indicate that an emergency vehicle without changing lanes can enhance speed with density lower than 0.1, while its speed can be enhanced by changing lane with density greater than 0.1. Meanwhile, vehicle speed and density within a certain range around emergency vehicles are lower than the road section average velocity and average density. The passage way of emergency vehicle that facilitate lane change causes less interference than that of an emergency vehicle which is unable to change lane. The study found that the physical characteristics of traffic flow when there are emergency vehicles are significantly different from routine traffic flow. Emergency vehicles can facilitate their passage by changing lanes at a medium or high density.

With the growing population, traffic congestion in cities is one of the most urgent concerns. Congestion not only causes additional delay and stress for the driver but also increases fuel consumption and pollution. The existing traffic management system does not offer priority when emergency vehicles are seen because it is a timer-based traffic control system. Ambulances, fire trucks, and other emergency vehicles are unable to arrive on time because of the traffic jams on the roadways. So, in order to reduce traffic congestion in daily life, our proposed methodology measures the density of vehicles at a 4-way junction. In the absence of emergency vehicles, the proposed system gives green light automatically based on the density of vehicles on the road. The traffic light changes according to the signal emitted by the sensor. Using an RF transmitter and receiver, the signal priority is switched to the lane in which an emergency vehicle is present. This system incorporates the entire traffic system and operates in emergency scenarios to ensure quick arrival at the desired location without any delay.

Medan city is the third largest metropolitan city in Indonesia. Problems that occur in big cities, especially in the city of Medan itself, include the level of congestion and the level of violations of traffic signs which are quite high and the constraints of emergency vehicles on duty such as ambulances, fire engines and other emergency vehicles, especially on traffic lights. This paper described Emergency vehicles that will build a Security System Using Barriers on Zebra Crosses, especially in Microcontroller-Based Traffic Light. This system uses the RFID component RC522 as an emergency vehicle detector photodiode and laser diode as vehicle detection components above the zebra cross so that the barrier does not damage the vehicle. Servo motor as a barrier gate drive and Arduino Uno R3 as a component where each input command is received to be processed. Where if the emergency vehicle is passing through a traffic light with a red light, then the light will turn green. With this system, the emergency vehicle on duty is not late to its destination and the driver cannot break through the road divider when the red light is on and does not disturb pedestrians. feet to cross The application of the system is designed to be connected directly to Arduino so that the input signal can be processed and can produce the expected output on the performance of the system, namely the servo motor, and 6 LEDs on the traffic light 2 intersections.

This project presents the Design of Emergency RFID Traffic Light Systems using the Ultra High-Frequency RFID reader as a detector to control the traffic light system. This project focused on emergency vehicles such as ambulances, firefighters, and police to get on smooth journey at a congested area with traffic light. The proposed way of detection is with RFID tags located at the emergency vehicles to generate emergency traffic lights system. The project established communication between traffic signals and emergency vehicles so that traffic signals can respond to the arrival of these emergency vehicles and respond accordingly. The findings shows that the emergency vehicles recommended speed of around 50km/h while approaching the traffic light. In addition, the distance during approaching the RFID reader does not exceed 7 meters to allow the RFID tag of emergency vehicles can be read clearly. When traffic signals change allowing emergency vehicles to move quickly past traffic with less hassle.

This paper describes the development of a traffic controller using fuzzy logic, combined with the analysis of video sequences through machine vision techniques. The controller is able to automatically manage the traffic flow in a set of intersections, giving priority to the traffic lights of roads where there are emergency vehicles waiting. The system uses a classification algorithm, which is trained in order to detect any vehicle the scene and an image processing algorithm that identifies emergency vehicles within the previously detected vehicles. Using the information of video sequences acquired with CCD cameras installed on the intersections, the system choose the sequence of actions that improves the traffic flow, so as to increase the mobility in the road where the emergency vehicle is detected. Results show that the system is able to detect vehicles in real time. Also, the system adapts in an efficient and fast way to the changes in traffic flow in order to establish a priority road for emergency vehicles.

AIM. The primary aim of the research presented in this thesis was to establish if emergency vehicle markings and warning systems influenced the way in which either a civilian or emergency driver responded when interacting during emergency driving situations. This was achieved by utilising a variety of research methods and a wide range of data types, including self-report questionnaires, Police collision reports, and real-world video data. The intention was that the findings could be used to inform approaches towards improving the on-road interaction between civilian and emergency drivers. BACKGROUND. Numerous emergency vehicle interactions occur without incident, yet some result in near misses, and collisions – both minor and serious in nature. Previous research (Shultz et al. 2009) has reported that civilian drivers often act in an adverse manner such as a panicked reaction (Gormley et al. 2009), due to poor vehicle salience, or modern vehicle soundproofing and technology distractions, when interacting with a responding emergency vehicle. Consequences of these negative interactions include feelings of frustration by the emergency driver, blame apportionment, and financial and reputational damage to the organisations themselves. Following an extensive review of the literature, research therefore firstly established the opinions of emergency and civilian drivers. Subsequent analysis of data, involving use of both marked and unmarked Police vehicles then helped to establish whether near misses and collisions occur as a result of marking type, through poor conspicuity (salience and warning systems) or as a result of behavioural change in the drivers themselves. METHODS. The research was conducted through four studies, using a multi-methods approach, to establish i.) The attitudes and opinions of emergency service drivers towards the public through questionnaire survey. ii.) A comparison between both marked and unmarked Police vehicle collision data and the effect of emergency warning systems on collision liability, over a 4.5 year time frame utilising telematics data from both vehicle marking types. iii.) Analysis of Police real world video footage observing the interaction between a civilian driver and a responding Police vehicle. iv.) Civilian drivers’ perceptions of how they interact with the emergency vehicle when allowing for its presence on the road through questionnaire survey. RESULTS. Important findings identified through self report questionnaires showed that Police drivers believed they were the least aggressive drivers, in comparison to their emergency service driver peers. Ambulance drivers were the most frustrated with other road users but were more willing to discuss their feelings, whilst Fire Service drivers were more likely to take risks in order to arrive more quickly at an emergency situation. Evidence gathered and reviewed showed that the public reacted in two distinct ways when giving ease of passage to an emergency vehicle. On high speed roads, civilian drivers showed an initial delayed reaction, but having observed the approaching vehicle, moved away in a calm manner. For interactions within an urban environment the civilian driver was more likely to commit a driving violation such as mounting a pavement or travelling through a red light. However, this was mainly due to the Police drivers making rapid progress when near to other vehicles, forcing the civilian driver into their subsequent actions. Analysis of telematics data showed that Police drivers in marked vehicles were more likely to be involved in ‘blameworthy’ collisions when the emergency warning systems were operational compared to when they were switched off. For ‘blue light’ use, this increased Police collision liability by 69%, for ‘flashing headlights’ use by 78%, and for ‘sirens’ use by 120%. However, importantly this behavioural effect was not seen in an unmarked Police vehicle as collision liability was found to be due to operational tasks such as stopping offenders. As a result, this study suggests that poor conspicuity was not a factor in collisions/events but was an outcome of Police driver behaviour and driving style. CONCLUSION. The results are highly informative in providing an understanding of collisions between marked and unmarked Police and civilian vehicles as driver behaviour influenced the collision liability. It is anticipated that the results from this thesis could be used to influence emergency driver training through increased focus on emotion management in driving, and active occupational policies which engage with drivers. Training should focus on the increased risks of driving a highly conspicuous emergency vehicle, compared to its unmarked counterpart, and the influence warning systems have on collision outcomes. This would lead to an increase in the safety of all parties. In addition, it could potentially lead to reduced organisational costs whilst aiding the development of public education campaigns including advice on appropriate civilian driver response to approaching emergency vehicles through use of government literature and social media.

The tram has been known as a modern, environmentally friendly, quick and safe means of transportation; hence, giving the signal priority for trams on arterial streets at signalized intersections is crucial to improve tram service efficiency. However, tram priority can impact the movement of emergency vehicles (ambulances, police cars, fire engines, etc) which required quick response to save lives and properties. Therefore, to organize the traffic road, it is necessary to make the urban road network more efficient by considering both tram and emergency vehicles priority. In this paper we propose a new approach, a system that can adapt efficiently with a priority signal control. Thereof, we have used Transit Signal Priority (TSP) techniques to adjust signal phasing in order to serve emergency vehicles and manage the tram priority in a coordinated tram intersection. The system used in this study provides acyclic signal operation based on a real time control approach including: - An optimization process using passing vehicle search algorithm (PVS) to control the flow through the network. - A database to manage the sensor data from detectors for real time predictions of emergency vehicle and tram arrival time. - A traffic road simulation module to evaluate best timing plans at the intersection.

One of the traffic problems in the transportation system is the emergency vehicles management such as fire trucks, ambulances, and so forth. It is necessary to know the characteristics of the traffic flow on a single road when the road appears to the presence of emergency vehicles. This paper presents an analysis of the characteristics of the traffic flow on a single road when traffic passing through the roads there is an emergency vehicle. Or in other words the traffic flow on the single road is distorted by the emergency vehicles. The analysis is based on the queuing theory through simulation. The road segment observed is a single lane road. Construction of the model is a FIFO queue models, a single server with one service rate level and two-level arrival rate. Traffic conditions are modelled with M/M/1, M/G/1 and G/G/1. The simulation experiment result shows the presence of the emergency vehicles on the road do not affect too large the travel time of the normal traffic. Traffic with the smaller variance will result in the travel time of the normal traffic lower than the higher variance. The G/G/1 model shows the characteristics of the normal vehicles travel time and the emergency vehicles travel time lower than the M/M/1 model and M/G/1 model. The G/G/1 models with lower variance will result in the emergency vehicle travel time is closer to that expected.

Traffic lights are located on the road intersections to control and manage the competing traffic flows. Several algorithms have been proposed considering the real-time traffic characteristics of each competing traffic flow at the road intersection. Emergency vehicles such as ambulance, fire truck and police vans should have higher priorities to cross any road intersection first. Whenever an emergency vehicle appears close to any road intersection, all vehicles on the competing flows should stop and allow that vehicle to proceed first. However, this may cause a hazards situation in the case that any driver miss-behaves or insists to follow the current traffic light phase. In this paper, we aim at designing a context-aware traffic light self-scheduling (CA-TLS) algorithm. This algorithm uses the traffic characteristics of the traffic flows and the emergency vehicles presence on the competing flows at any signalized road intersection. First, these parameters are gathered using periodic advertisement messages of traveling vehicles. Then, the CA-TLS algorithm sets the phases of each traffic light cycle according to the traffic gathered data. The green phase of any traffic flow can be interrupted to enable the fast proceeding of the appeared emergency vehicles. An extensive set of experiments have shown that this algorithm decreases the delay time of emergency vehicles at the signalized road intersections.

Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely it would help save people's lives and reduce property loss. To reduce the travel time of EMVs, prior work has used route optimization based on historical traffic-flow data and traffic signal pre-emption based on the optimal route. However, traffic signal pre-emption dynamically changes the traffic flow which, in turn, modifies the optimal route of an EMV. In addition, traffic signal pre-emption practices usually lead to significant disturbances in traffic flow and subsequently increase the travel time for non-EMVs. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real-time as it travels through the traffic network. The decentralized RL agents learn network-level cooperative traffic signal phase strategies that not only reduce EMV travel time but also reduce the average travel time of non-EMVs in the network. This benefit has been demonstrated through comprehensive experiments with synthetic and real-world maps. These experiments show that EMVLight outperforms benchmark transportation engineering techniques and existing RL-based signal control methods.

This paper explores the notion and usefulness of nonstationary velocity profiles for high priority emergency vehicle transit on automated highways. These profiles are intended for use in the link layer of the hierarchical control architecture defined by the California Partners for Automated Transit and Highways (PATH) program. A non-stationary velocity profile which can be used to circulate traffic around a faster moving emergency vehicle is introduced. The effects on traffic flow are illustrated. A traffic flow controller that sustains nonstationary velocity profiles on fully automated highways is designed. Controller stability is discussed stressing the usefulness of nonstationary velocity profiles in creating a moving area of low vehicle density. Simulation results obtained using SmartCap, a traffic flow simulation program, demonstrate the fast circulation of an emergency vehicle in AHS while high traffic flow is maintained.

Air pollution is considered a global concern due to its impacts on human life and the urban environment. Therefore, precise modeling techniques are necessary to predict air quality in congested areas such as megacities. Recently, machine learning algorithms such as Neural Networks show significant possibilities in air quality studies. This paper proposes a model to estimate air quality in a congested urban area in Baghdad city using Artificial Neural Network (ANN) algorithm and Geospatial Information System (GIS) techniques. Carbon Monoxide (CO) gas is selected as the main air pollutant. The model parameters involve; CO samples, traffic flow, weather data, and land use information collected in the field. The proposed model is implemented in Matlab environment and the results are processed after entering ArcGIS software. Using its spatial analysis tools, the outputs are presented as a map. The final findings indicate the highest value of CO emissions that reached 34 ppm during the daytime. The most polluted areas are located near congested roads and industrial locations in comparison with residential areas. The proposed model is validated by using actual values that are collected from the field, where the model's accuracy is 79%. The proposed model showed feasibility and applicability in a congested urban area due to the integration between the machine learning algorithm and GIS modeling. Therefore, the proposed model in this research can be used as a supportive model for decision making of city managers.