This factor increases the travel time and shows a negative impact on transport economy. To reduce average waiting time, several WSN-based schemes and methodologies have been developed and stated in the literature. Srivastava et al. The maximum intersection utilization configuration is shown in Figure 8. The empty lane with green light configuration is shown in Figure 9. Maximum intersection utilization configuration [ 50 ]. Empty lane with green light [ 50 ]. Zhou et al. The proposed algorithm adjusts the length and sequence of traffic lights.
The experimental outcomes showed that the proposed algorithm produces the reduction of average waiting time of vehicles and high throughput compared to fixed-time control algorithms. The approach to make the traffic light adapt to traffic flow are discussed in [ 52 , 53 , 54 ]. Bharadwaj et al. Figure 10 , shows the process at the intersection. Based on RFID tags, the emergency vehicles are identified and all the vehicles are counted by the inductive loop method.
If the RFID tag is genuine, the emergency vehicle count is incremented by one. The centralized traffic server collects the count of normal and emergency vehicles and toggles the signal to green to about 30 s for the side with emergency vehicles. The proposed model has solved many traffic problems and saved the traveling time. A UHF RFID tag antenna has been utilized as a displacement sensor in [ 56 ] and the authors found that the sensor is sensitive to displacements for a dynamic range of 40 mm. Layout architecture for efficient dynamic traffic control system [ 55 ]. Faye et al. They organized sensors in a hierarchical architecture.
A logically separated 4-level hierarchical distributed network is shown in Figure Any node can perform light plan computation. Sensors are organized in two groups:. Logically separated 4-level hierarchical distributed network [ 57 ]. Vehicle arrival data is continuously collected by BL sensors and departure data is collected only for the light is green by AL sensors. AL sensors are in charge of data aggregation and for taking decisions.
As the sensor device directly communicates with the base station, spatial reuse and channel capacity problems are prevented. The proposed algorithm is flexible in managing conflicts and decisions. Dynamic traffic management techniques to reduce accidents due to the Red-Light-Running RLR phenomenon are discussed in [ 58 , 59 , 60 ].
The RLR phenomenon is an unhealthy and dangerous driving act. As the drivers wait in the traffic light queue, many drivers try to cross the intersection when the traffic light changes from green to yellow. This causes accidents and traffic congestion. The proposed techniques try to reduce RLR phenomenon by assigning green times to the road sections with long queues.
Some interesting methods are related to the dynamic management of traffic light cycles and phases using WSN and multi fuzzy logic controllers [ 61 , 62 ]. A fuzzy logic-based multi controller system is shown in Figure The multi controller system consists of a Wireless Sensor Network WSN , phase sorting module and fuzzy logic controller for each traffic light phase.
The WSN acquires traffic data. A phase sorting module calculates the phase order and a fuzzy logic controller calculates the green time duration of each phase. The results demonstrate that the multi-controller approach is effective in balancing the vehicles waiting time in queues for heavy and random traffic arrivals. The authors obtained All representative average waiting time reduction approaches that were analyzed in detail are highlighted in Table 8 , sorted by author names and reference numbers.
Fuzzy logic based multi controller system [ 61 ]. It was found that most of the developed systems were only tested in laboratory settings and only a few of these have actually been deployed in a real traffic environment [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Researchers have concentrated on different traffic parameters to optimize traffic management.
Some of the most significant solutions that optimized traffic management and achieved parameters are shown in Figure A WSN-based traffic management system has reduced travel time of metropolitan traffic by enhancing safety with the purpose of refining our everyday life. Traffic management systems experience some significant challenges and issues as discussed below:. Connectivity and coverage : Connectivity and coverage are the two vital factors for certifying effective resource management in WSNs. When many nodes are deployed to have more coverage, the efficient coordination between nodes and the central system is a big challenge.
What are the confines on the number of nodes for a certain percentage of coverage? Communication and energy cost : Minimizing communication cost of the system is essential to save energy and extend the network lifetime. The reliability of the system is always affected by the battery life issue. How to make use of low power mechanisms to reduce the WSN node power consumption? Congestion : Despite many techniques to avoid congestion problem, the traffic management system is still not able to react quickly to non-recursive congestion. How to design a traffic management system to provide a solution for the non-recursive congestion problem?
Traffic incident notification : How making the traffic management system capable of sending incident notification to local media, police department and situation management office to act upon the situation? Coordination and implementation : How to maintain coordination between intersections? How to design an intelligent traffic cloud to resolve real-time problems by making use of cloud computing? In this paper, we have presented a comprehensive review of existing urban traffic management schemes.
The main challenges associated with congestion control, average waiting time reduction, prioritizing emergency vehicles and the design requirements of intelligent traffic system are discussed to provide an insight into the goals of urban traffic management. Despite the large number of research activities and the excellent progress that has been made in traffic management systems in recent years, challenges for further research remains.
A few issues are outlined for future work. A real-time traffic management system cannot be guaranteed. Processing of large amounts of real time traffic data, the run time of the control system and reliability are the problems to be solved to ensure real-time demand of the urban traffic management system. There is a need to design an intelligent traffic cloud by making use of cloud computing to solve the problems related to real-time. The use of virtual strips in DTMon [ 14 ] can be extended for detecting and tracking of the End-of-queue, caused by congestion.
Intra-vehicle channel interference [ 28 ] can be reduced further by assigning non-overlapping channels to RSUs. Modifying the dynamic traffic management system using WSN and multi fuzzy logic controllers proposed in [ 61 ] to detect emergency vehicles is an option to consider. Designing a promising traffic management system to provide smooth traffic flow in non-recursive congestion situation can be an interesting issue for future research.
In further research, it is recommended that information variables, such as the number of accidents and traffic violations are to be involved in traffic management system to assist decision makers in the formulation of traffic rules and policies. All the authors have equally contributed to the development and writing of this article. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online Jan Leonhard M. Reindl, Academic Editor.
Author information Article notes Copyright and License information Disclaimer. Received Aug 3; Accepted Nov This article has been cited by other articles in PMC. Abstract Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the number of vehicles traveling on them.
Introduction Over the years vehicle usage has increased exponentially worldwide. Open in a separate window. Figure 1. Figure 2. Key Issues in Urban Traffic Management System Traffic congestion is a burning issue in many cities due to an exponential growth of running vehicles.
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Figure 3. Figure 4. Overview This section provides an overview of the sensing evolution, traffic sensing technologies, the characteristic of the general sensor node and the hierarchical functionality of a WSN-based urban traffic management system. Sensing Evolution The Sensor is a key element of any smart system and a course of action is taken based on its location.
Traffic Sensing Technologies The safe and efficient operation of a traffic management system relies largely on the application of advanced technologies. Table 1 Overview of traffic sensing technologies [ 3 ]. Technology Principles Advantages Disadvantages Specific Equipment Inductive loop The inductive-loop sensor detects the vehicle or conductive metal object by sensing the loop inductance, which is dropped by inducing currents in the object.
Flexible design to fulfill a great variety of applications. Unresponsive to bad weather. Offers accurate count data. Installation and maintenance require pavement cut and lane closure. Many loops are required to cover a location. The detection accuracy drops with vehicle classes. Roadway sensors, lead-in cables, pull box and electronic unit in the control cabinet. RFID Radio-frequency identification RFID technology uses radio waves to give-and-take data between a reader and an electronic tag attached to a vehicle for the purpose of tracking.
RFID is economical. It does not disturb traffic. RFID only senses equipped vehicles at a point on the road. Antenna transmitter and receiver , Transponder, tag reader system, and computer. Microwave radar The Microwave radar transmits signals in the recognition regions and captures the echoed signals from vehicles. The reflected signal is processed to find the speed and direction of the vehicle. Speed is measured directly.
Multiple lane operation. Continuous wave Doppler sensors are incapable of sensing immobile vehicles. Antenna transmitter and receiver , control unit and processor. Acoustic Acoustic sensors detect audible sounds produced by vehicular traffic and there by vehicle presence, and speed are measured. Unresponsive to precipitation. Vehicle count accuracy may be affected by cold temperature. Transducer, filters, microphones, pre amplifier, storage equipment.
Magnetometer Magnetometers have sensors that sense the horizontal and vertical components of the Earth's magnetic field. Less prone than loops to pressures of traffic. Data transmission over RF Radio Frequency link. Installation needs a pavement cut. Inadequate installation decreases pavement life cycle.
Maintenance and installation require lane closure. Magnetic probe detector, micro loop probes and control unit. Magnetic A magnetic sensor detects the presence of a vehicle by measuring the perturbation in the Earth's magnetic field because of a ferrous metal object. Applicable where loops are not likely. Installation of some models does not require a pavement cut.
Insensitive to bad weather. Less prone than inductive loops to pressures of vehicles. Installation needs a boring under the road. Incapable of sensing immobile vehicles. Infrared The infrared sensor illuminates the low powered infrared energy in the recognition regions and captures the echoed energy from the vehicles. The echoed energy is focused onto an infrared-sensitive material, which transforms the echoed and illuminated energy into electrical signals. These signals are processed and analyzed to obtain the presence of a vehicle.
Sensitive to bad weather. Installation, maintenance and lens cleaning require lane closure. Multi spectrum camera. Traffic surveillance can be taken at high accuracy. It is a non-intrusive and non- interruptive technology. Helicopters are expensive and necessitate pilots to operate. It costs time and resource to gather traffic data. Analysis of aerial pictures is complicated. Helicopters, Analog color PAL camera and computer.
Ultrasonic An Ultrasonic sensor transmits ultrasonic waves and again collects the echoed waves from an object. It uses the time lapse between the transmitted and reflected sonic wave to identify the location of the object. Monitors multiple lanes. Proficient of detecting over height vehicles. Performance is affected by environmental circumstances.
Transducers Transmitter and Receiver , amplifier and oscillator. VIP Video image processor This system normally consists of a camera, processor-based workstation for analyzing the images, and software for understanding the imageries and transforming them into traffic data. Simple to add and change detection areas.
Offers broad-area detection. Installation and maintenance require lane closure. Analog color PAL camera and image processing unit. Table 2 Traffic output data and communications bandwidth of commercially available sensors [ 3 ]. General Sensor Node A sensor is a transducer which transforms the physical nature parameters like light, temperature, velocity, pressure, moisture, etc.
A traffic monitoring sensor node typically comprises of four main modules as given below: A sensing module—This module acquires data. A radio module—This module is for wireless data communication.
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A power module—This module is for energy supply. Table 3 Wireless Communication Technologies [ 4 ].
IEEE Bluetooth Standard for data and voice transmission between many devices via a safe and free radio link. Wi-Fi System of wireless data broadcast over computational webs. GSM Typical system for communication via mobile phones including digital technology. RFID Uses radio waves to detect objects carrying tags. Hierarchy of Urban Traffic Management Systems The urban traffic management system is mainly divided into three subsystems, namely the sensor network subsystem, the traffic control subsystem and the safety subsystem.
Figure 5. Hierarchical functionality of WSN based urban traffic management system. State-of-the-Art Review This section provides a complete review of related projects, architectures, data collection schemes, routing algorithms, congestion avoidance schemes, priority based traffic management schemes, and average waiting time reduction schemes on urban traffic management, based on WSNs. Table 4 Urban Traffic Management Projects. To perform an optimal traffic management. Hong Kong Government. To design, construct, and test a low-cost sensor network instrument to monitor traffic in work zones.
To use technology for creating a safe, efficient and greener environment.
Prediction of the traffic flow. To perform road weather management. To dynamically adjust signal timing to meet current traffic demands. To provide congestion information, alternate route, travel time and alert travelers about any accident. To minimize travel time and reduce stoppage. To monitor and analyze the traffic through high definition video surveillance and broadcast system.
Specific Architectures, Data Collection Schemes and Routing Algorithms To gratify the requirements for real-time traffic management, researchers have proposed a number of explicit architectures, data collection schemes and routing algorithms for WSN-based urban traffic management. Figure 6. Table 5 Summary of architectures, data collection schemes and routing algorithms. Reference Proposed Approach Outcome Arbabi et al. Collection of high quality travels time and speed.
Mazloumi et al. Provides shortest route. Traveling time of vehicle reduces. Bazzi et al. Provides highly reliable communication. Maximum coverage range. Reduced redundancy information. Consumes less network bandwidth. Remote transmission. Traffic accidents are reduced. Effectively control traffic flow. Position and velocity of a moving vehicle are determined with less computation. Cabezas et al.
Latency and jitter are improved. Improved delivery performance of data packets in VSN. Monitoring vehicle density and traffic directionality. Low power consumption. Classification of vehicles. Fast traffic information delivery. Congestion Avoidance Schemes Traffic congestion is a plight situation on roads that occurs as use increases. Table 6 Summary of congestion avoidance schemes. Reference Proposed Approach Outcome Du et al. Traffic monitoring is improved. Knorr et al. Penetration rates are slightly improved. The emission decreased.
Accurate measurement of execution times. High packet delivery ratio. Low delivery delay. The communication cost required for monitoring traffic is minimized. Congestion is reduced at the traffic signal. Increased utilization of the infrastructure. Smooth traffic flow. Lower end-to-end delays. Laisheng et al. Reduce Congestion. Priority-Based Traffic Management Schemes An emergency vehicle is any vehicle that is designated and authorized for emergency service such as police, ambulance and fire.
Figure 7. Table 7 Summary of priority-based traffic management schemes. Reference Proposed Approach Outcome Rajeshwari et al. Emergency vehicle clearances. Stolen vehicle detection. Chakraborty et al. Effective management of high prioritized vehicles. Priority based signaling.http://numytoons.com/3250.php
A Survey on Urban Traffic Management System Using Wireless Sensor Networks
Saving fuel consumption. Remote communications and control operations of ITS distribution nodes are unified and simplified. Accurate vehicle classification and count. Low error rate. Transmission delay reduction. Faster transmission of emergency messages reporting dangerous events. Average Waiting Time Reduction Schemes Average waiting time refers to the amount of time a vehicle is waiting in a queue before the traffic signal is ON.
Figure 8. Figure 9. Figure Table 8 Summary of average waiting time reduction schemes. Reference Proposed Approach Outcome Srivastava et al. The average waiting time: Orthodox policy: MIU: ELWGL: 6. Optimal green light length and green light sequence. Higher throughput. Low vehicle waiting time. Bhuvaneswari et al. The system is self-configurable. Average waiting time of vehicles reduces.
Detects real-time traffic stats. Dynamic traffic light control. Reduces congestion. Saves travel time.
Special priority for emergency vehicles. Reduced average waiting time at an intersection. Frequent traffic light decisions.
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Al-Nasser et al. Minimized Average waiting time. Reduce the RLR phenomenon occurrence. Collotta et al. Reduces the vehicles waiting times. Real-time traffic monitoring. Gomez et al. Obtained Challenges A WSN-based traffic management system has reduced travel time of metropolitan traffic by enhancing safety with the purpose of refining our everyday life. Traffic management systems experience some significant challenges and issues as discussed below: Connectivity and coverage : Connectivity and coverage are the two vital factors for certifying effective resource management in WSNs.
Conclusions and Future Work In this paper, we have presented a comprehensive review of existing urban traffic management schemes. Author Contributions All the authors have equally contributed to the development and writing of this article. Conflicts of Interest The authors hereby declares no conflict of interest.
References 1. Litman T. Padmavathi G. A study on vehicle detection and tracking using wireless sensor networks. Albaladejo C. Sridhar N. Al-Holous N. Alfelor R. Ban X. Sivanandam R. Benekohal R. Christopher D. Arbabi H. Mazloumi E.
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Using GPS data to gain insight into public transport travel time variability. Bazzi A. Alexander P. Co-operative intelligent transport system: 5. IEEE Proc. Bruno R. Chao K. An intelligent traffic flow control system based on radio frequency identification and wireless sensor networks. Saqib M. Cabezas C. Choi O. Lee U. Dissemination and harvesting of urban data using vehicular sensing platforms.
IEEE Trans. Friesen M. Vehicular traffic monitoring using Bluetooth scanning over a wireless sensor networks. Liu Y. Zhou J. A user-customizable urban traffic information collection method based on wireless sensor networks. Ahmad F. Effective urban traffic monitoring by vehicular sensor networks. Knorr F. Wang Y. Mobility management algorithms and applications for mobile sensor networks. Mobile Comput.
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