In this paper, we proposed an Implementing Intelligent Traffic Control for Congestion, Ambulance clearance, and Stolen Vehicle Detection. This system was implemented based on present criteria that tracking three conditions in those one is heavy traffic control and another one is making a root of emergency vehicle like ambulance and VIP vehicle and finding theft or crime vehicle. Here each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. The systems also update the traffic information on internet which is helpful to the travelers and traffic control department.
In this project, there are conditions of Congestion Control, Ambulance Clearance and Stolen vehicle detection. It will perform as per presence of either of these conditions. It also can be extended to the level that when there is a presence of Ambulance in a particular junction wants to go either of the ways in case of halt condition so here the control system should already know the movement and willing direction of ambulance vehicle in case of emergency purposes.
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The aim of the project is intelligent traffic control system to pass emergency vehicles smoothly and stolen vehicles and easy monitoring the traffic signals . All the devices such as 16X2 LCD, Zigbee, lpc2148,rfid reader, GSM are being interfacing to microcontroller which forms the control unit of the project. The uniqueness of this project is, it sends a caution SMS to mobile number as well as it post the values in by using zigbee and gsm technology. This project Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection” is used as whenever the Rechargeable Battery and solar system can be connected to this system to enable it to work in power failure conditions also.
switch is pressed, it will transmit the signal. The signal contains unique id and security code. The transmitter contains PIC16F877A microcontroller and ZigBee module. The microcontroller sends the commands and data to the ZigBee via serial communication. Second part is the receiver, which is placed at trafﬁc pole. It also contains PIC16F877A microcontroller and ZigBee module. The receiver compares the security code received to the security code present in its database. If it matches, then it will turn the green light on. For testing purpose, we used short range RFID reader in our prototype. First, the receiver part is turned on. The red and green signal will be on for 10 seconds duration and orange light will be on for 2 seconds duration one after the other. Secondly, we bring the RFID of stolen vehicle into the range of RFID reader. Then the signal will turn to red for duration of 30 seconds and a SMS is received.Thirdly,we bring 12 RFIDs into the rangeof RFID reader,andthen the greenlight duration will change to 30 seconds. Fourthly, we bring an emergency vehicle carrying ZigBee transmitter into the range of ZigBee receiver, and then the trafﬁc light will change to green till the receiver receives the ZigBee signal as shown in Figure 4. Figure 5 shows the images of different components and highlighted features of the proposed work. Figure 5.a shows the signal pole installed in junction. In the default condition, red and green light will set for 10 seconds. The time period will be varied according to the trafﬁc conditions, stolen vehicle, and emergency vehicle. Figure 5.b shows the transmitter part is placed in the ambulance. It transmits ZigBee signal continuously. Figure 5.c shows the LCD display status at different conditions (in that ﬁgure one is normal conjunction image(trafﬁc signal running as per the default time period)and another one is LCD display status, when an ambulance coming near to junction. Figure 5.d shows the actual connections of different components like RFID, GSM, ZigBee, interfacing different microcontrollers. Figure 5.e shows the status updated at the time of stolen vehicle is found. The stolen vehicle RFID number should be updated in the database. If stolen vehicle is found,
Traffic congestion is a major problem in cities of developing Countries like India. Growth in urban population and the middle-class segment consume vehicles to the rising number of vehicles in the cities. Congestion on roads eventually results in slow moving traffic, which increases the time of travel, thus be notable as one of the major issues in metropolitan cities. Emergency vehicles like ambulance and fire trucks need to reach their destinations at the earliest. If they spend a lot of time in traffic jams, valued lives of many people may be in danger. Here the image sequences from a camera are analyzed using various edge detection and object counting methods to obtain the most efficient technique. Then, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. The traffic signal indication continuously glows to green as long as the emergency vehicle is waiting at the traffic lane. After the vehicle crossed the junction, automatically the traffic signals follow the previous pattern generation of traffic signals. This can be implemented in LABVIEW.
A b s tract — In this paper, we proposed an Implementing Intelligent Traffic Control for Congestion, Ambulance clearance, and Stolen Vehicle Detection. This system was implemented based on present criteria that tracking three conditions in those one is heavy traffic control and another one is making a root of emergency vehicle like ambulance and VIP vehicle. In this paper we are going to implement a sensor network work which is used to detect the traffic density and also use RFID reader and tags. We use ARM7 system-on-chip to read the RFID tags attached to the vehicles. It counts number of vehicles that passes on a particular path during a specified duration. If the RFID tag read belongs to the stolen vehicles. GSM SIM300 used for message send to the police control room. In addition, when an ambulance approaching the junction, it will communicate the traffic controller in the junction to turn on the green light. This module uses Zigbee modules on CC2500.
Presents a savvy movement control framework to pass crisis vehicles easily. Every individual vehicle is outfitted with exceptional radio recurrence ID (RFID) tag (set at a key area), which makes it difficult to expel or devastate. We utilize RFID peruser, NSK EDK-125–TTL, and PIC16F877A framework on-chip to peruse the RFID labels connected to the vehicle. It checks number of vehicles that passes on a specific way amid a predetermined length. It likewise decides the system clog, and consequently the green light term for that way. In the event that the RFID-label read has a place with the stolen vehicle, then a message is sent utilizing GSM SIM300 to the police control room. Furthermore, when an emergency vehicle is moving toward the intersection, it will impart to the movement controller in the intersection to turn ON the green light. This module utilizes ZigBee modules on CC2500 and PIC16F877A framework on-chip for remote correspondences between the emergency vehicle and activity controller. The model was tried under various mixes of contributions to our remote correspondence research center and exploratory outcomes were found obviously. we proposed an Implementing Intelligent Traffic Control for Congestion, Ambulance leeway, and Stolen Vehicle Detection. This framework was actualized in light of present criteria that following three conditions in those one is overwhelming movement control and another is making a foundation of crisis vehicle like rescue vehicle and VIP vehicle. In this paper we will actualize a sensor organize work which is utilized to identify the movement thickness and furthermore utilize RFID peruser and labels. We utilize ARM7 framework on-chip to peruse the RFID labels connected to the vehicles. It checks number of vehicles that passes on a specific way amid a predefined span. In the event that the RFID label read has a place with the stolen vehicles. GSM SIM300 utilized for message send to the police control room. Furthermore, when an emergency vehicle moving toward the intersection, it will impart the activity controller in the intersection to turn on the green light. This module utilizes Zigbee modules on CC2500 .
Abstract - This paper deals with the effective use of wireless technology and high speed micro controller to provide smooth and clear flow of traffic for emergency vehicle to reach the destination on time. This is implemented by using ARDUINO, RFID reader for detecting the RFID tag placed in the emergency vehicle. The information on detecting the emergency vehicle is sent to the traffic system through RF transmitter and receiver system, for automatically controlling the traffic light until the emergency vehicle passes through. Pair of IR sensors is used to estimate the congestion near the traffic and this information is provided to the ambulance driver using GSM. In addition to this scheme, the system also detects the stolen vehicle passing through that path. On detecting the stolen vehicle the information is sent to the control room through GSM for immediate action.
Nowadays the road accidents in modern urban areas are increased to uncertain level. The loss of human life due to accident is to be avoided. Traffic congestion and tidal flow are major facts that cause delay to ambulance . To bar loss of human life due to accidents we introduce a scheme called ITLS (Intelligent Traffic Light system). The main theme behind this scheme is to provide a smooth flow for the emergency vehicles like ambulance to reach the hospitals in time and thus minimizing the delay caused by traffic congestion. The idea behind this scheme is to implement ITLS which would control mechanically the traffic lights in the path of the ambulance. The ambulance is controlled by the control unit which furnishes adequate route to the ambulance and also controls the traffic light according to the ambulance location and thus reaching the hospital safety .
allowed to maneuver in any path. For the duration of this time reader comparable to crimson light outlets the autos passing by way of the lane.Smart visitors mild Controller: each city has more than one intersections Two lights are called linked Lights which can be positioned on opposite aspects of the avenue that become a member of two intersections. The RFID reader shops the files of all of the vehicles that passed by way of the avenue. The traffic mild controller follows the same circular robin sequence of the lights. But if an Emergency auto is detected at any visitors light then controller leave the circular robin schedule and generate the green signal for the ambulance. The opposite mission of the controller is to calculate the time of inexperienced signal that is centered on the number of automobile. To solve the drawback of starvation a point in time is outlined. If this restrict exceeds then that mild gets its turn.
There is lacking of study in Malaysia that focuses on EMS delivery optimization through application of ambulance location model (ALM). Other researches related to EMS in Malaysia (Hauswald and Yeoh, 1997; Ng and Abdul Ghani, 2006; Hameed et al., 2010) do not consider the performance of EMS delivery. Previous work by Lim (2011) considers the performance of EMS delivery although by using hypothetical region. This research further expands the work from Lim (2011), by applying and comparing the performance of two ALMs using real map data.
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Automatic traffic accident detection and notification with smart phone: this paper specify the use of smart phone to automatically detect the accident and notify a control unit and also provide the situational details like GPS coordinates and photographs of the accident spot . Another paper Automatic vehicle accident detection system uses GPS and GSM module to locate the accident spot and send the details of the victim to the medical help centre and to the nearby police station . Automatic accident detection and ambulance rescue with intelligent traffic light system focuses on controlling the traffic light in the path of the ambulance so that time delay in reaching the hospital can be minimized .
In the public sector like ambulance, fire, police and other services and in private sector like courier, taxi, repair, maintenance etc. When we start a service policy, our first aim is to partition a network into smaller networks. The location problem aims to determining the number, the position and the mission of required distribution centres within the populated region. We have nine nodes in Bareilly city and we had to find a median node (City post office) for ambulance services where we have to locate a facility. We minimize the expected travel time to random call. We can apply this model to fire, police and other services.
a health center and an ambulance was presence in the four camps of the study and is not available in Zayouna camp (Table 1) where the United Nations Office, (2015) the need to provide immediate assistance to the displaced and some cases need emergency care for women, children and the elderly, the disabled and survivors of torture and sexual violence through the provision of direct services by health centers and ambulances rushed from inside the camps.
These results suggest that the introduction of a low tech, novel intervention is very acceptable to ambulance clini- cians, intuitive to use, requires little education on use and positively supports their contemporaneous data re- cording and information exchange processes. Although there are some data to suggest the effectiveness of the intervention in its ability to improve information sharing during pre- alert and handover, this study was neither designed nor powered to do this. However, the overall positive results suggest that further well conducted stud- ies to test an updated intervention, based on feedback provided, are worthwhile. Importantly, future studies should carefully consider, in the absence of advanced data sharing technology, how information required for pre-alert and handover can be recorded contemporan- eously to minimise data loss. This will be essential from both a patient safety perspective and to more objectively measure the impact of any future handover intervention.
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Background: Several Emergency Medical Systems use a criteria-based prioritization system for ambulance response. The emergency medical priority dispatching of ambulances was introduced in the 1980s. In a system of this kind, the operators at the medical emergency dispatch centers have to assess the patients’ symptoms and the need for ambulance response. The prioritization of the ambulance response is based on the seriousness of the patient’s symptoms, his/her current condition and, in the case of trauma, the trauma mechanism. The priority system is supposed to optimize the use of the ambulance service and to match and meet the patients’ needs with an adequate response from the ambulances. The aim of this study was to describe the dispatching and utilization of the ambulance service in a part of Finland. Re- sults: There was a substantial divergence between the initial priority assigned and the patients’ medical status at the scene. The ambulance staff confirmed the need for ambulance transport for 65% of all the patients who were assigned an ambulance by the dispatch center. Conclusions: Using a criteria-based dispatch protocol, the dispatch operator works with a wider safety margin in the priority assessments for ambulance response than was actually confirmed by the ambulance personnel at the scene. In this sample, there may be some overuse of the ambulance service. According to the assessments made by the ambulance staff, 35% of the patients did not require ambulance transport. The emer- gency system has to accept and work with safety margins. At the same time, there must be a balance between a safety margin and a waste of limited resources.
Intelligent traffic control systems are advanced applications which, without embodying intelligence as such, aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. In this project, I attempt to make an intelligent system which helps in congestion control, ambulance clearance and stolen vehicle detection which solves the road safety problems to a greater extend. Each individual vehicle is equipped with special radio frequency identification (RFID) tag. We use RFID readers along with microcontrollers to read the tags attached to each vehicle. It counts the number of vehicles that pass through the path during a specific duration, so that network congestion is determined and hence green light duration for a particular path. If RFID tag belongs to the stolen vehicle, it is informed to the police control room through GSM. In addition if the ambulance is approaching the junction, it will communicate to the traffic controller on the junction to turn the green light on. This module uses ZIGBEE and PIC microcontroller for wireless communication between ambulance and the traffic controller.
According to Mallory and Knights (2015), “medical clearance protocols should follow the Emergency Medical Treatment and Active Labor Act (EMTLA) criteria screening and stabilization examination psychiatric patients have comorbid medical conditions that need treatment prior to transfer to a psychiatric hospital, however medical problems are sometimes neglected once patients present with psychiatric symptoms. The SMART medical clearance process was used to rule out patients presenting with psychiatric symptoms but had no prior psychiatric illness, in the month of August, 4 patients were identified as not having psychiatric illnesses but organic illness presenting with psychiatric symptoms and were dropped off from the psychiatric list. We also used the protocol to identify patients with new onset of psychiatric illnesses, further laboratory work was indicated to ensure medical illness or substance use were not the cause. Some patients who were not cleared with the SMART protocol were identified as being evaluated by the psychiatric team, and used as a standard process to determine whether the implementation of the SMART protocol in the ED reduced the length of stay and expedited placement in an inpatient psychiatric facility. The psychiatric response team (PRT) staff recorded the number of patients that were seen in the ED daily with information gathered from epic, the number that were SMART cleared, the length of stay of each of the patents and the disposition time and date. This information is represented in a run graph (Table 2) and compared with the process prior to implementation of this project (Table 3). According to data collected on medically cleared patients to assessment preSMART August 2016, number of hours between medical clearances to psychiatric evaluation was 2.58 hours, with a median average of 1.33 hours.
 Omkar Udawant et al. proposed a smart ambulance system. The basic idea is whenever the ambulance is within the range of 100m, the signal changes to green for some time.They makes use of cloud and GPRS technology. Ambulance contains sensors like heart rate sensor, blood pressure, ECG. These sensors data will be sent to hospital’s database simultaneously. Treatment will be planned by the hospital authorities according to patient condition. So it saves so much time.
In the future this application can be upgrades to the next level by making it more interactive in such a way that during the time of the users registration a form will be given for the user’s to fill in all their medical details which can be stored on the cloud, then once the patient is in the ambulance all the medical details that was filled by the patient at the time of their registration along with the present patient condition will be sent to the hospital even before the ambulance reaches the hospital so that the doctors can be ready to treat the patients and same many life.